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Xiaoshuai Sun

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39 papers
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39

AAAI Conference 2026 Conference Paper

Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach

  • Lvpan Cai
  • Haowei Wang
  • Jiayi Ji
  • Yanshu Zhoumen
  • Shen Chen
  • Taiping Yao
  • Xiaoshuai Sun

The rise of AI-generated image tools has made localized forgeries increasingly realistic, posing challenges for visual content integrity. Although recent efforts have explored localized AIGC detection, existing datasets predominantly focus on object-level forgeries while overlooking broader scene edits in regions such as sky or ground. To address these limitations, we introduce BR-Gen, a large-scale dataset of 150,000 locally forged images with diverse scene-aware annotations, which are based on semantic calibration to ensure high-quality samples. BR-Gen is constructed through a fully automated ``Perception-Creation-Evaluation'' pipeline to ensure semantic coherence and visual realism. In addition, we further propose NFA-ViT, a Noise-guided Forgery Amplification Vision Transformer that enhances the detection of localized forgeries by amplifying subtle forgery-related features across the entire image. NFA-ViT mines heterogeneous regions in images, i.e., potential edited areas, by noise fingerprints. Subsequently, attention mechanism is introduced to compel the interaction between normal and abnormal features, thereby propagating the traces throughout the entire image, allowing subtle forgeries to influence a broader context and improving overall detection robustness. Extensive experiments demonstrate that BR-Gen constructs entirely new scenarios that are not covered by existing methods. Take a step further, NFA-ViT outperforms existing methods on BR-Gen and generalizes well across current benchmarks.

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NeurIPS Conference 2025 Conference Paper

Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings

  • Qiong Wu
  • Wenhao Lin
  • Yiyi Zhou
  • Weihao Ye
  • Zhanpeng Zeng
  • Xiaoshuai Sun
  • Rongrong Ji

In this paper, we study the visual redundancy problem of multimodal large language models (MLLMs) from the perspective of attention behaviors. Via extensive empirical experiments, we observe and conclude three main inference stages of MLLMs: (i) Early fusion between tokens is first accomplished quickly. (ii) Intra-modality modeling then comes to play. (iii) Multimodal reasoning resumes and lasts until the end of inference. In particular, we reveal that visual tokens will stop contributing to reasoning when the text tokens receive enough image information. Based on this observation, we propose an effective method to improve the efficiency of MLLMs, termed dynamic visual-token exit (DyVTE), which is orthogonal but collaborative to previous token-wise visual compression methods. To validate the efficacy of DyVTE, we apply it to a set of MLLMs, including LLaVA, VILA, EAGLE and InternVL. The experimental results not only show the effectiveness of our DyVTE in improving MLLMs' efficiency, e. g. , DyVTE reduces the computation overhead of LLaVA-1. 5 by up to 45. 7% without performance drop, but also reveal a general pattern across multiple MLLMs, well facilitating the in-depth analysis of MLLMs. Our code is anonymously released at https: //anonymous. 4open. science/r/AnonymousDyVTE-26AB/.

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ICLR Conference 2025 Conference Paper

Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models

  • Gen Luo
  • Yiyi Zhou
  • Yuxin Zhang 0002
  • Xiawu Zheng
  • Xiaoshuai Sun
  • Rongrong Ji

In existing multimodal large language models (MLLMs), image resolution plays a significant role for granular visual recognition. However, directly increasing image resolution leads to expensive computational cost for MLLMs. In this paper, we reveal that a combination of low- and high-resolution visual features can efficiently mitigate this shortcoming. Based on this principle, we propose a novel and efficient method for MLLMs, termed Mixture-of-Resolution Adaptation (MRA). In particular, MRA adopts two visual pathways for images of different resolutions, where high-resolution visual information is embedded into the low-resolution pathway via the novel mixture-of-resolution adapters (MR-Adapters). This design also greatly reduces the input sequence length of MLLMs. To validate MRA, we apply it to a recent MLLM called LLaVA, and term the new model LLaVA-HR. We conduct extensive experiments on 17 vision-language (VL) tasks, which show that LLaVA-HR outperforms existing MLLMs on 15 VL tasks, e.g., +5.2\% on TextVQA. More importantly, both training and inference of LLaVA-HR remain efficient with MRA, e.g., 20 training hours and faster inference speed than LLaVA-NeXT. Source codes are released at: https://github.com/luogen1996/LLaVA-HR.

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AAAI Conference 2025 Conference Paper

IPDN: Image-enhanced Prompt Decoding Network for 3D Referring Expression Segmentation

  • Qi Chen
  • Changli Wu
  • Jiayi Ji
  • Yiwei Ma
  • Danni Yang
  • Xiaoshuai Sun

3D Referring Expression Segmentation (3D-RES) aims to segment point cloud scenes based on a given expression. However, existing 3D-RES approaches face two major challenges: feature ambiguity and intent ambiguity. Feature ambiguity arises from information loss or distortion during point cloud acquisition due to limitations such as lighting and viewpoint. Intent ambiguity refers to the model's equal treatment of all queries during the decoding process, lacking top-down task-specific guidance. In this paper, we introduce an Image-enhanced Prompt Decoding Network (IPDN), which leverages multi-view images and task-driven information to enhance the model's reasoning capabilities. To address feature ambiguity, we propose the Multi-view Semantic Embedding (MSE) module, which injects multi-view 2D image information into the 3D scene and compensates for potential spatial information loss. To tackle intent ambiguity, we designed a Prompt-Aware Decoder (PAD) that guides the decoding process by deriving task-driven signals from the interaction between the expression and visual features. Comprehensive experiments demonstrate that IPDN outperforms the state-of-the-art by 1.9 and 4.2 points in mIoU metrics on the 3D-RES and 3D-GRES tasks, respectively.

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ICLR Conference 2025 Conference Paper

Routing Experts: Learning to Route Dynamic Experts in Existing Multi-modal Large Language Models

  • Qiong Wu 0012
  • Zhaoxi Ke
  • Yiyi Zhou
  • Xiaoshuai Sun
  • Rongrong Ji

Recently, mixture of experts (MoE) has become a popular paradigm for achieving the trade-off between modal capacity and efficiency of multimodal large language models (MLLMs). Different from previous efforts, we are dedicated to exploring the dynamic experts in existing MLLMs and showing that a standard MLLM can also be a mixture of experts. However, achieving this target is still notoriously challenging. The well-trained MLLMs are more accustomed to the fixed pathway and a drastic change in its inference manner also greatly impedes its performance. To address these issues, we propose a novel dynamic expert routing method for existing MLLMs, termed Routing Experts (RoE), which can achieve example-dependent optimal path routing without obvious structure tweaks. Meanwhile, a new structure sparsity regularization is also introduced to force the well-trained MLLMs to learn more short-cut pathways. In addition, we also address the alignment of the training and inference of MLLMs in terms of network routing. To validate RoE, we apply it to a set of existing MLLMs, including LLaVA-1.5, LLaVA-HR and VILA, and conduct extensive experiments on a bunch of VL benchmarks. The experiment results not only show the effectiveness of our RoE in improving MLLMs' efficiency, but also yield obvious advantages over MoE-LLaVA in both performance and speed, e.g., an average performance gain of 3.3% on 5 benchmarks while being 1.61 times faster. Our code is anonymously released at https://github.com/DoubtedSteam/RoE

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AAAI Conference 2025 Conference Paper

StoryWeaver: A Unified World Model for Knowledge-Enhanced Story Character Customization

  • Jinlu Zhang
  • Jiji Tang
  • Rongsheng Zhang
  • Tangjie Lv
  • Xiaoshuai Sun

Story visualization has gained increasing attention in artificial intelligence. However, existing methods still struggle with maintaining a balance between character identity preservation and text-semantics alignment, largely due to a lack of detailed semantic modeling of the story scene. To tackle this challenge, we propose a novel knowledge graph, namely Character-Graph (CG), which represents various story-related knowledge, including the characters, their attributes and the relationship. We then introduce StoryWeaver, an image generator that achieves Customization via Character-Graph (C-CG), capable of consistent story visualization with rich text semantics. To further improve the multi-character generation performance, we incorporate knowledge-enhanced spatial guidance (KE-SG) into StoryWeaver to precisely inject character semantics into generation. To validate the effectiveness of our proposed method, extensive experiments are conducted using a new benchmark called TBC-Bench. The experiments confirm that our StoryWeaver excels not only in creating vivid visual story plots but also in accurately conveying character identities across various scenarios with considerable storage efficiency, e.g., achieving an average increase of +9.03% DINO-I and +13.44% CLIP-T. Furthermore, ablation experiments are conducted to verify the superiority of each proposed module.

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ICLR Conference 2025 Conference Paper

γ-MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large Language Models

  • Yaxin Luo
  • Gen Luo
  • Jiayi Ji
  • Yiyi Zhou
  • Xiaoshuai Sun
  • Zhiqiang Shen
  • Rongrong Ji

Despite the significant progress in multimodal large language models (MLLMs), their high computational cost remains a barrier to real-world deployment. Inspired by the mixture of depths (MoDs) in natural language processing, we aim to address this limitation from the perspective of ``activated tokens''. Our key insight is that if most tokens are redundant for the layer computation, then can be skipped directly via the MoD layer. However, directly converting the dense layers of MLLMs to MoD layers leads to substantial performance degradation. To address this issue, we propose an innovative MoD adaptation strategy for existing MLLMs called $\gamma$-MoD. In $\gamma$-MoD, a novel metric is proposed to guide the deployment of MoDs in the MLLM, namely rank of attention maps (ARank). Through ARank, we can effectively identify which layer is redundant and should be replaced with the MoD layer. Based on ARank, we further propose two novel designs to maximize the computational sparsity of MLLM while maintaining its performance, namely shared vision-language router and masked routing learning. With these designs, more than 90% dense layers of the MLLM can be effectively converted to the MoD ones. To validate our method, we apply it to three popular MLLMs, and conduct extensive experiments on 9 benchmark datasets. Experimental results not only validate the significant efficiency benefit of $\gamma$-MoD to existing MLLMs but also confirm its generalization ability on various MLLMs. For example, with a minor performance drop, i.e., -1.5%, $\gamma$-MoD can reduce the training and inference time of LLaVA-HR by 31.0% and 53.2%, respectively.

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AAAI Conference 2024 Conference Paper

3D-STMN: Dependency-Driven Superpoint-Text Matching Network for End-to-End 3D Referring Expression Segmentation

  • Changli Wu
  • Yiwei Ma
  • Qi Chen
  • Haowei Wang
  • Gen Luo
  • Jiayi Ji
  • Xiaoshuai Sun

In 3D Referring Expression Segmentation (3D-RES), the earlier approach adopts a two-stage paradigm, extracting segmentation proposals and then matching them with referring expressions. However, this conventional paradigm encounters significant challenges, most notably in terms of the generation of lackluster initial proposals and a pronounced deceleration in inference speed. Recognizing these limitations, we introduce an innovative end-to-end Superpoint-Text Matching Network (3D-STMN) that is enriched by dependency-driven insights. One of the keystones of our model is the Superpoint-Text Matching (STM) mechanism. Unlike traditional methods that navigate through instance proposals, STM directly correlates linguistic indications with their respective superpoints, clusters of semantically related points. This architectural decision empowers our model to efficiently harness cross-modal semantic relationships, primarily leveraging densely annotated superpoint-text pairs, as opposed to the more sparse instance-text pairs. In pursuit of enhancing the role of text in guiding the segmentation process, we further incorporate the Dependency-Driven Interaction (DDI) module to deepen the network's semantic comprehension of referring expressions. Using the dependency trees as a beacon, this module discerns the intricate relationships between primary terms and their associated descriptors in expressions, thereby elevating both the localization and segmentation capacities. Comprehensive experiments on the ScanRefer benchmark reveal that our model not only sets new performance standards, registering an mIoU gain of 11.7 points but also achieves a staggering enhancement in inference speed, surpassing traditional methods by 95.7 times. The code and models are available at https://github.com/sosppxo/3D-STMN.

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NeurIPS Conference 2024 Conference Paper

ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models

  • Mingrui Wu
  • Xinyue Cai
  • Jiayi Ji
  • Jiale Li
  • OuCheng Huang
  • Hao Fei
  • Guannan Jiang
  • Xiaoshuai Sun

In this work, we propose a training-free method to inject visual prompts into Multimodal Large Language Models (MLLMs) through learnable latent variable optimization. We observe that attention, as the core module of MLLMs, connects text prompt tokens and visual tokens, ultimately determining the final results. Our approach involves adjusting visual tokens from the MLP output during inference, controlling the attention response to ensure text prompt tokens attend to visual tokens in referring regions. We optimize a learnable latent variable based on an energy function, enhancing the strength of referring regions in the attention map. This enables detailed region description and reasoning without the need for substantial training costs or model retraining. Our method offers a promising direction for integrating referring abilities into MLLMs, and supports referring with box, mask, scribble and point. The results demonstrate that our method exhibits out-of-domain generalization and interpretability.

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NeurIPS Conference 2024 Conference Paper

DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion

  • Ke Sun
  • Shen Chen
  • Taiping Yao
  • Hong Liu
  • Xiaoshuai Sun
  • Shouhong Ding
  • Rongrong Ji

The rapid progress of Deepfake technology has made face swapping highly realistic, raising concerns about the malicious use of fabricated facial content. Existing methods often struggle to generalize to unseen domains due to the diverse nature of facial manipulations. In this paper, we revisit the generation process and identify a universal principle: Deepfake images inherently contain information from both source and target identities, while genuine faces maintain a consistent identity. Building upon this insight, we introduce DiffusionFake, a novel plug-and-play framework that reverses the generative process of face forgeries to enhance the generalization of detection models. DiffusionFake achieves this by injecting the features extracted by the detection model into a frozen pre-trained Stable Diffusion model, compelling it to reconstruct the corresponding target and source images. This guided reconstruction process constrains the detection network to capture the source and target related features to facilitate the reconstruction, thereby learning rich and disentangled representations that are more resilient to unseen forgeries. Extensive experiments demonstrate that DiffusionFake significantly improves cross-domain generalization of various detector architectures without introducing additional parameters during inference. The code are available in https: //github. com/skJack/DiffusionFake. git.

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ICML Conference 2024 Conference Paper

Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models

  • Mingrui Wu
  • Jiayi Ji
  • Oucheng Huang
  • Jiale Li
  • Yuhang Wu 0004
  • Xiaoshuai Sun
  • Rongrong Ji

The issue of hallucinations is a prevalent concern in existing Large Vision-Language Models (LVLMs). Previous efforts have primarily focused on investigating object hallucinations, which can be easily alleviated by introducing object detectors. However, these efforts neglect hallucinations in inter-object relationships, which is essential for visual comprehension. In this work, we introduce R-Bench, a novel benchmark for evaluating Vision Relationship Hallucination. R-Bench features image-level questions that focus on the existence of relationships and instance-level questions that assess local visual comprehension. We identify three types of relationship co-occurrences that lead to hallucinations: relationship-relationship, subject-relationship, and relationship-object. The visual instruction tuning dataset’s long-tail distribution significantly impacts LVLMs’ understanding of visual relationships. Additionally, our analysis reveals that current LVLMs tend to overlook visual content, overly rely on the common sense knowledge of Large Language Models (LLMs), and struggle with spatial relationship reasoning based on contextual information.

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ICML Conference 2024 Conference Paper

Fast Text-to-3D-Aware Face Generation and Manipulation via Direct Cross-modal Mapping and Geometric Regularization

  • Jinlu Zhang 0002
  • Yiyi Zhou
  • Qiancheng Zheng
  • Xiaoxiong Du
  • Gen Luo
  • Jun Peng 0007
  • Xiaoshuai Sun
  • Rongrong Ji

Text-to-3D-aware face (T3D Face) generation and manipulation is an emerging research hot spot in machine learning, which still suffers from low efficiency and poor quality. In this paper, we propose an E *nd-to-End E fficient and E ffective network for fast and accurate T3D face generation and manipulation, termed $E^3$-FaceNet. Different from existing complex generation paradigms, $E^3$-FaceNet resorts to a direct mapping from text instructions to 3D-aware visual space. We introduce a novel Style Code Enhancer to enhance cross-modal semantic alignment, alongside an innovative Geometric Regularization* objective to maintain consistency across multi-view generations. Extensive experiments on three benchmark datasets demonstrate that $E^3$-FaceNet can not only achieve picture-like 3D face generation and manipulation, but also improve inference speed by orders of magnitudes. For instance, compared with Latent3D, $E^3$-FaceNet speeds up the five-view generations by almost 470 times, while still exceeding in generation quality. Our code is released at https: //github. com/Aria-Zhangjl/E3-FaceNet.

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NeurIPS Conference 2024 Conference Paper

I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing

  • Yiwei Ma
  • Jiayi Ji
  • Ke Ye
  • Weihuang Lin
  • Zhibin Wang
  • Yonghan Zheng
  • Qiang Zhou
  • Xiaoshuai Sun

Significant progress has been made in the field of Instruction-based Image Editing (IIE). However, evaluating these models poses a significant challenge. A crucial requirement in this field is the establishment of a comprehensive evaluation benchmark for accurately assessing editing results and providing valuable insights for its further development. In response to this need, we propose I2EBench, a comprehensive benchmark designed to automatically evaluate the quality of edited images produced by IIE models from multiple dimensions. I2EBench consists of 2, 000+ images for editing, along with 4, 000+ corresponding original and diverse instructions. It offers three distinctive characteristics: 1) Comprehensive Evaluation Dimensions: I2EBench comprises 16 evaluation dimensions that cover both high-level and low-level aspects, providing a comprehensive assessment of each IIE model. 2) Human Perception Alignment: To ensure the alignment of our benchmark with human perception, we conducted an extensive user study for each evaluation dimension. 3) Valuable Research Insights: By analyzing the advantages and disadvantages of existing IIE models across the 16 dimensions, we offer valuable research insights to guide future development in the field. We will open-source I2EBench, including all instructions, input images, human annotations, edited images from all evaluated methods, and a simple script for evaluating the results from new IIE models. The code, dataset, and generated images from all IIE models are provided in GitHub: https: //github. com/cocoshe/I2EBench.

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AAAI Conference 2024 Conference Paper

Improving Panoptic Narrative Grounding by Harnessing Semantic Relationships and Visual Confirmation

  • Tianyu Guo
  • Haowei Wang
  • Yiwei Ma
  • Jiayi Ji
  • Xiaoshuai Sun

Recent advancements in single-stage Panoptic Narrative Grounding (PNG) have demonstrated significant potential. These methods predict pixel-level masks by directly matching pixels and phrases. However, they often neglect the modeling of semantic and visual relationships between phrase-level instances, limiting their ability for complex multi-modal reasoning in PNG. To tackle this issue, we propose XPNG, a “differentiation-refinement-localization” reasoning paradigm for accurately locating instances or regions. In XPNG, we introduce a Semantic Context Convolution (SCC) module to leverage semantic priors for generating distinctive features. This well-crafted module employs a combination of dynamic channel-wise convolution and pixel-wise convolution to embed semantic information and establish inter-object relationships guided by semantics. Subsequently, we propose a Visual Context Verification (VCV) module to provide visual cues, eliminating potential space biases introduced by semantics and further refining the visual features generated by the previous module. Extensive experiments on PNG benchmark datasets reveal that our approach achieves state-of-the-art performance, significantly outperforming existing methods by a considerable margin and yielding a 3.9-point improvement in overall metrics. Our codes and results are available at our project webpage: https://github.com/TianyuGoGO/XPNG.

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NeurIPS Conference 2024 Conference Paper

RG-SAN: Rule-Guided Spatial Awareness Network for End-to-End 3D Referring Expression Segmentation

  • Changli Wu
  • Qi Chen
  • Jiayi Ji
  • Haowei Wang
  • Yiwei Ma
  • You Huang
  • Hao Fei
  • Xiaoshuai Sun

3D Referring Expression Segmentation (3D-RES) aims to segment 3D objects by correlating referring expressions with point clouds. However, traditional approaches frequently encounter issues like over-segmentation or mis-segmentation, due to insufficient emphasis on spatial information of instances. In this paper, we introduce a Rule-Guided Spatial Awareness Network (RG-SAN) by utilizing solely the spatial information of the target instance for supervision. This approach enables the network to accurately depict the spatial relationships among all entities described in the text, thus enhancing the reasoning capabilities. The RG-SAN consists of the Text-driven Localization Module (TLM) and the Rule-guided Weak Supervision (RWS) strategy. The TLM initially locates all mentioned instances and iteratively refines their positional information. The RWS strategy, acknowledging that only target objects have supervised positional information, employs dependency tree rules to precisely guide the core instance’s positioning. Extensive testing on the ScanRefer benchmark has shown that RG-SAN not only establishes new performance benchmarks, with an mIoU increase of 5. 1 points, but also exhibits significant improvements in robustness when processing descriptions with spatial ambiguity. All codes are available at https: //github. com/sosppxo/RG-SAN.

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ICML Conference 2024 Conference Paper

SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation

  • Danni Yang
  • Jiayi Ji
  • Yiwei Ma
  • Tianyu Guo 0005
  • Haowei Wang 0001
  • Xiaoshuai Sun
  • Rongrong Ji

In this paper, we introduce SemiRES, a semi-supervised framework that effectively leverages a combination of labeled and unlabeled data to perform RES. A significant hurdle in applying semi-supervised techniques to RES is the prevalence of noisy pseudo-labels, particularly at the boundaries of objects. SemiRES incorporates the Segment Anything Model (SAM), renowned for its precise boundary demarcation, to improve the accuracy of these pseudo-labels. Within SemiRES, we offer two alternative matching strategies: IoU-based Optimal Matching (IOM) and Composite Parts Integration (CPI). These strategies are designed to extract the most accurate masks from SAM’s output, thus guiding the training of the student model with enhanced precision. In instances where a precise mask cannot be matched from the available candidates, we develop the Pixel-Wise Adjustment (PWA) strategy, guiding the student model’s training directly by the pseudo-labels. Extensive experiments on three RES benchmarks—RefCOCO, RefCOCO+, and G-Ref reveal its superior performance compared to fully supervised methods, especially in low-data scenarios. Remarkably, with only 1% labeled data, our SemiRES outperforms the supervised baseline by a large margin, e. g. +18. 64% gains on RefCOCO val set.

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AAAI Conference 2024 Conference Paper

Toward Open-Set Human Object Interaction Detection

  • Mingrui Wu
  • Yuqi Liu
  • Jiayi Ji
  • Xiaoshuai Sun
  • Rongrong Ji

This work is oriented toward the task of open-set Human Object Interaction (HOI) detection. The challenge lies in identifying completely new, out-of-domain relationships, as opposed to in-domain ones which have seen improvements in zero-shot HOI detection. To address this challenge, we introduce a simple Disentangled HOI Detection (DHD) model for detecting novel relationships by integrating an open-set object detector with a Visual Language Model (VLM). We utilize a disentangled image-text contrastive learning metric for training and connect the bottom-up visual features to text embeddings through lightweight unary and pair-wise adapters. Our model can benefit from the open-set object detector and the VLM to detect novel action categories and combine actions with novel object categories. We further present the VG-HOI dataset, a comprehensive benchmark with over 17k HOI relationships for open-set scenarios. Experimental results show that our model can detect unknown action classes and combine unknown object classes. Furthermore, it can generalize to over 17k HOI classes while being trained on just 600 HOI classes.

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AAAI Conference 2024 Conference Paper

Towards Efficient Diffusion-Based Image Editing with Instant Attention Masks

  • Siyu Zou
  • Jiji Tang
  • Yiyi Zhou
  • Jing He
  • Chaoyi Zhao
  • Rongsheng Zhang
  • Zhipeng Hu
  • Xiaoshuai Sun

Diffusion-based Image Editing (DIE) is an emerging research hot-spot, which often applies a semantic mask to control the target area for diffusion-based editing. However, most existing solutions obtain these masks via manual operations or off-line processing, greatly reducing their efficiency. In this paper, we propose a novel and efficient image editing method for Text-to-Image (T2I) diffusion models, termed Instant Diffusion Editing (InstDiffEdit). In particular, InstDiffEdit aims to employ the cross-modal attention ability of existing diffusion models to achieve instant mask guidance during the diffusion steps. To reduce the noise of attention maps and realize the full automatics, we equip InstDiffEdit with a training-free refinement scheme to adaptively aggregate the attention distributions for the automatic yet accurate mask generation. Meanwhile, to supplement the existing evaluations of DIE, we propose a new benchmark called Editing-Mask to examine the mask accuracy and local editing ability of existing methods. To validate InstDiffEdit, we also conduct extensive experiments on ImageNet and Imagen, and compare it with a bunch of the SOTA methods. The experimental results show that InstDiffEdit not only outperforms the SOTA methods in both image quality and editing results, but also has a much faster inference speed, i.e., +5 to +6 times. Our code available at https://anonymous.4open.science/r/InstDiffEdit-C306

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ICML Conference 2024 Conference Paper

X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation

  • Yiwei Ma
  • Zhekai Lin
  • Jiayi Ji
  • Yijun Fan
  • Xiaoshuai Sun
  • Rongrong Ji

Recent advancements in automatic 3D avatar generation guided by text have made significant progress. However, existing methods have limitations such as oversaturation and low-quality output. To address these challenges, we propose X-Oscar, a progressive framework for generating high-quality animatable avatars from text prompts. It follows a sequential "Geometry→Texture→Animation" paradigm, simplifying optimization through step-by-step generation. To tackle oversaturation, we introduce Adaptive Variational Parameter (AVP), representing avatars as an adaptive distribution during training. Additionally, we present Avatar-aware Score Distillation Sampling (ASDS), a novel technique that incorporates avatar-aware noise into rendered images for improved generation quality during optimization. Extensive evaluations confirm the superiority of X-Oscar over existing text-to-3D and text-to-avatar approaches. Our anonymous project page: https: //anonymous1440. github. io/.

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AAAI Conference 2024 Conference Paper

X-RefSeg3D: Enhancing Referring 3D Instance Segmentation via Structured Cross-Modal Graph Neural Networks

  • Zhipeng Qian
  • Yiwei Ma
  • Jiayi Ji
  • Xiaoshuai Sun

Referring 3D instance segmentation is a challenging task aimed at accurately segmenting a target instance within a 3D scene based on a given referring expression. However, previous methods have overlooked the distinct roles played by different words in referring expressions. Additionally, they have failed to incorporate the positional relationship within referring expressions with the spatial correlations in 3D scenes. To alleviate these issues, we present a novel model called X-RefSeg3D, which constructs a cross-modal graph for the input 3D scene and unites textual and spatial relationships for reasoning via graph neural networks. Our approach begins by capturing object-specific text features, which are then fused with the instance features to construct a comprehensive cross-modal scene graph. Subsequently, we integrate the obtained cross-modal features into graph neural networks, leveraging the K-nearest algorithm to derive explicit instructions from expressions and factual relationships in scenes. This enables the effective capture of higher-order relationships among instances, thereby enhancing feature fusion and facilitating reasoning. Finally, the refined feature undergoes a matching module to compute the ultimate matching score. Experimental results on ScanRefer demonstrate the effectiveness of our method, surpassing previous approaches by a substantial margin of +3.67% in terms of mIOU.

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NeurIPS Conference 2023 Conference Paper

Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models

  • Gen Luo
  • Yiyi Zhou
  • Tianhe Ren
  • Shengxin Chen
  • Xiaoshuai Sun
  • Rongrong Ji

Recently, growing interest has been aroused in extending the multimodal capability of large language models (LLMs), e. g. , vision-language (VL) learning, which is regarded as the next milestone of artificial general intelligence. However, existing solutions are prohibitively expensive, which not only need to optimize excessive parameters, but also require another large-scale pre-training before VL instruction tuning. In this paper, we propose a novel and affordable solution for the effective VL adaption of LLMs, called Mixture-of-Modality Adaptation (MMA). Instead of using large neural networks to connect the image encoder and LLM, MMA adopts lightweight modules, i. e. , adapters, to bridge the gap between LLMs and VL tasks, which also enables the joint optimization of the image and language models. Meanwhile, MMA is also equipped with a routing algorithm to help LLMs achieve an automatic shift between single- and multi-modal instructions without compromising their ability of natural language understanding. To validate MMA, we apply it to a recent LLM called LLaMA and term this formed large vision-language instructed model as LaVIN. To validate MMA and LaVIN, we conduct extensive experiments under two setups, namely multimodal science question answering and multimodal dialogue. The experimental results not only demonstrate the competitive performance and the superior training efficiency of LaVIN than existing multimodal LLMs, but also confirm its great potential as a general-purpose chatbot. More importantly, the actual expenditure of LaVIN is extremely cheap, e. g. , only 1. 4 training hours with 3. 8M trainable parameters, greatly confirming the effectiveness of MMA. Our code is anonymously released at: https: //anonymous. 4open. science/r/LaVIN--1067.

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AAAI Conference 2023 Conference Paper

End-to-End Zero-Shot HOI Detection via Vision and Language Knowledge Distillation

  • Mingrui Wu
  • Jiaxin Gu
  • Yunhang Shen
  • Mingbao Lin
  • Chao Chen
  • Xiaoshuai Sun

Most existing Human-Object Interaction (HOI) Detection methods rely heavily on full annotations with predefined HOI categories, which is limited in diversity and costly to scale further. We aim at advancing zero-shot HOI detection to detect both seen and unseen HOIs simultaneously. The fundamental challenges are to discover potential human-object pairs and identify novel HOI categories. To overcome the above challenges, we propose a novel End-to-end zero-shot HOI Detection (EoID) framework via vision-language knowledge distillation. We first design an Interactive Score module combined with a Two-stage Bipartite Matching algorithm to achieve interaction distinguishment for human-object pairs in an action-agnostic manner. Then we transfer the distribution of action probability from the pretrained vision-language teacher as well as the seen ground truth to the HOI model to attain zero-shot HOI classification. Extensive experiments on HICO-Det dataset demonstrate that our model discovers potential interactive pairs and enables the recognition of unseen HOIs. Finally, our method outperforms the previous SOTA under various zero-shot settings. Moreover, our method is generalizable to large-scale object detection data to further scale up the action sets. The source code is available at: https://github.com/mrwu-mac/EoID.

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NeurIPS Conference 2023 Conference Paper

Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models

  • Qiong Wu
  • Wei Yu
  • Yiyi Zhou
  • Shubin Huang
  • Xiaoshuai Sun
  • Rongrong Ji

With ever increasing parameters and computation, vision-language pre-trained (VLP) models exhibit prohibitive expenditure in downstream task adaption. Recent endeavors mainly focus on parameter efficient transfer learning (PETL) for VLP models by only updating a small number of parameters. However, excessive computational overhead still plagues the application of VLPs. In this paper, we aim at parameter and computation efficient transfer learning (PCETL) for VLP models. In particular, PCETL not only needs to limit the number of trainable parameters in VLP models, but also to reduce the computational redundancy during inference, thus enabling a more efficient transfer. To approach this target, we propose a novel dynamic architecture skipping (DAS) approach towards effective PCETL. Instead of directly optimizing the intrinsic architectures of VLP models, DAS first observes the significances of their modules to downstream tasks via a reinforcement learning (RL) based process, and then skips the redundant ones with lightweight networks, i. e. adapters, according to the obtained rewards. In this case, the VLP model can well maintain the scale of trainable parameters while speeding up its inference on downstream tasks. To validate DAS, we apply it to two representative VLP models, namely ViLT and METER, and conduct extensive experiments on a bunch of VL tasks. The experimental results not only show the great advantages of DAS in reducing computational complexity, e. g. -11. 97% FLOPs of METER on VQA2. 0, but also confirm its competitiveness against existing PETL methods in terms of parameter scale and performance. Our source code is given in our appendix.

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AAAI Conference 2023 Conference Paper

Towards Real-Time Panoptic Narrative Grounding by an End-to-End Grounding Network

  • Haowei Wang
  • Jiayi Ji
  • Yiyi Zhou
  • Yongjian Wu
  • Xiaoshuai Sun

Panoptic Narrative Grounding (PNG) is an emerging cross-modal grounding task, which locates the target regions of an image corresponding to the text description. Existing approaches for PNG are mainly based on a two-stage paradigm, which is computationally expensive. In this paper, we propose a one-stage network for real-time PNG, termed End-to-End Panoptic Narrative Grounding network (EPNG), which directly generates masks for referents. Specifically, we propose two innovative designs, i.e., Locality-Perceptive Attention (LPA) and a bidirectional Semantic Alignment Loss (SAL), to properly handle the many-to-many relationship between textual expressions and visual objects. LPA embeds the local spatial priors into attention modeling, i.e., a pixel may belong to multiple masks at different scales, thereby improving segmentation. To help understand the complex semantic relationships, SAL proposes a bidirectional contrastive objective to regularize the semantic consistency inter modalities. Extensive experiments on the PNG benchmark dataset demonstrate the effectiveness and efficiency of our method. Compared to the single-stage baseline, our method achieves a significant improvement of up to 9.4% accuracy. More importantly, our EPNG is 10 times faster than the two-stage model. Meanwhile, the generalization ability of EPNG is also validated by zero-shot experiments on other grounding tasks. The source codes and trained models for all our experiments are publicly available at https://github.com/Mr-Neko/EPNG.git.

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NeurIPS Conference 2022 Conference Paper

Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach

  • Peng Mi
  • Li Shen
  • Tianhe Ren
  • Yiyi Zhou
  • Xiaoshuai Sun
  • Rongrong Ji
  • Dacheng Tao

Deep neural networks often suffer from poor generalization caused by complex and non-convex loss landscapes. One of the popular solutions is Sharpness-Aware Minimization (SAM), which smooths the loss landscape via minimizing the maximized change of training loss when adding a perturbation to the weight. However, we find the indiscriminate perturbation of SAM on all parameters is suboptimal, which also results in excessive computation, ~\emph{i. e. }, double the overhead of common optimizers like Stochastic Gradient Descent~(SGD). In this paper, we propose an efficient and effective training scheme coined as Sparse SAM (SSAM), which achieves sparse perturbation by a binary mask. To obtain the sparse mask, we provide two solutions which are based on Fisher information and dynamic sparse training, respectively. In addition, we theoretically prove that SSAM can converge at the same rate as SAM, ~\emph{i. e. }, $O(\log T/\sqrt{T})$. Sparse SAM not only has the potential for training acceleration but also smooths the loss landscape effectively. Extensive experimental results on CIFAR10, CIFAR100, and ImageNet-1K confirm the superior efficiency of our method to SAM, and the performance is preserved or even better with a perturbation of merely 50\% sparsity. Code is available at \url{https: //github. com/Mi-Peng/Sparse-Sharpness-Aware-Minimization}.

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AAAI Conference 2021 Conference Paper

Dual-level Collaborative Transformer for Image Captioning

  • Yunpeng Luo
  • Jiayi Ji
  • Xiaoshuai Sun
  • Liujuan Cao
  • Yongjian Wu
  • Feiyue Huang
  • Chia-Wen Lin
  • Rongrong Ji

Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning. However, they are still criticized for the lack of contextual information and fine-grained details, which in contrast are the merits of traditional grid features. In this paper, we introduce a novel Dual-Level Collaborative Transformer (DLCT) network to realize the complementary advantages of the two features. Concretely, in DLCT, these two features are first processed by a novel Dualway Self Attenion (DWSA) to mine their intrinsic properties, where a Comprehensive Relation Attention component is also introduced to embed the geometric information. In addition, we propose a Locality-Constrained Cross Attention module to address the semantic noises caused by the direct fusion of these two features, where a geometric alignment graph is constructed to accurately align and reinforce region and grid features. To validate our model, we conduct extensive experiments on the highly competitive MS-COCO dataset, and achieve new state-of-the-art performance on both local and online test sets, i. e. , 133. 8% CIDEr on Karpathy split and 135. 4% CIDEr on the official split.

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AAAI Conference 2021 Conference Paper

Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network

  • Jiayi Ji
  • Yunpeng Luo
  • Xiaoshuai Sun
  • Fuhai Chen
  • Gen Luo
  • Yongjian Wu
  • Yue Gao
  • Rongrong Ji

Transformer-based architectures have shown great success in image captioning, where object regions are encoded and then attended into the vectorial representations to guide the caption decoding. However, such vectorial representations only contain region-level information without considering the global information reflecting the entire image, which fails to expand the capability of complex multi-modal reasoning in image captioning. In this paper, we introduce a Global Enhanced Transformer (termed GET) to enable the extraction of a more comprehensive global representation, and then adaptively guide the decoder to generate high-quality captions. In GET, a Global Enhanced Encoder is designed for the embedding of the global feature, and a Global Adaptive Decoder are designed for the guidance of the caption generation. The former models intra- and inter-layer global representation by taking advantage of the proposed Global Enhanced Attention and a layer-wise fusion module. The latter contains a Global Adaptive Controller that can adaptively fuse the global information into the decoder to guide the caption generation. Extensive experiments on MS COCO dataset demonstrate the superiority of our GET over many state-of-the-arts.

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AAAI Conference 2020 Conference Paper

SSAH: Semi-Supervised Adversarial Deep Hashing with Self-Paced Hard Sample Generation

  • Sheng Jin
  • Shangchen Zhou
  • Yao Liu
  • Chao Chen
  • Xiaoshuai Sun
  • Hongxun Yao
  • Xian-Sheng Hua

Deep hashing methods have been proved to be effective and efficient for large-scale Web media search. The success of these data-driven methods largely depends on collecting suf- ficient labeled data, which is usually a crucial limitation in practical cases. The current solutions to this issue utilize Generative Adversarial Network (GAN) to augment data in semisupervised learning. However, existing GAN-based methods treat image generations and hashing learning as two isolated processes, leading to generation ineffectiveness. Besides, most works fail to exploit the semantic information in unlabeled data. In this paper, we propose a novel Semisupervised Self-pace Adversarial Hashing method, named SSAH to solve the above problems in a unified framework. The SSAH method consists of an adversarial network (A- Net) and a hashing network (H-Net). To improve the quality of generative images, first, the A-Net learns hard samples with multi-scale occlusions and multi-angle rotated deformations which compete against the learning of accurate hashing codes. Second, we design a novel self-paced hard generation policy to gradually increase the hashing difficulty of generated samples. To make use of the semantic information in unlabeled ones, we propose a semi-supervised consistent loss. The experimental results show that our method can significantly improve state-of-the-art models on both the widelyused hashing datasets and fine-grained datasets.

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AAAI Conference 2019 Conference Paper

Dynamic Capsule Attention for Visual Question Answering

  • Yiyi Zhou
  • Rongrong Ji
  • Jinsong Su
  • Xiaoshuai Sun
  • Weiqiu Chen

In visual question answering (VQA), recent advances have well advocated the use of attention mechanism to precisely link the question to the potential answer areas. As the difficulty of the question increases, more VQA models adopt multiple attention layers to capture the deeper visual-linguistic correlation. But a negative consequence is the explosion of parameters, which makes the model vulnerable to over-fitting, especially when limited training examples are given. In this paper, we propose an extremely compact alternative to this static multi-layer architecture towards accurate yet efficient attention modeling, termed as Dynamic Capsule Attention (CapsAtt). Inspired by the recent work of Capsule Network, CapsAtt treats visual features as capsules and obtains the attention output via dynamic routing, which updates the attention weights by calculating coupling coefficients between the underlying and output capsules. Meanwhile, CapsAtt also discards redundant projection matrices to make the model much more compact. We quantify CapsAtt on three benchmark VQA datasets, i. e. , COCO-QA, VQA1. 0 and VQA2. 0. Compared to the traditional multi-layer attention model, CapsAtt achieves significant improvements of up to 4. 1%, 5. 2% and 2. 2% on three datasets, respectively. Moreover, with much fewer parameters, our approach also yields competitive results compared to the latest VQA models. To further verify the generalization ability of CapsAtt, we also deploy it on another challenging multi-modal task of image captioning, where state-of-the-art performance is achieved with a simple network structure.

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AAAI Conference 2019 Conference Paper

Free VQA Models from Knowledge Inertia by Pairwise Inconformity Learning

  • Yiyi Zhou
  • Rongrong Ji
  • Jinsong Su
  • Xiangming Li
  • Xiaoshuai Sun

In this paper, we uncover the issue of knowledge inertia in visual question answering (VQA), which commonly exists in most VQA models and forces the models to mainly rely on the question content to “guess” answer, without regard to the visual information. Such an issue not only impairs the performance of VQA models, but also greatly reduces the credibility of the answer prediction. To this end, simply highlighting the visual features in the model is undoable, since the prediction is built upon the joint modeling of two modalities and largely influenced by the data distribution. In this paper, we propose a Pairwise Inconformity Learning (PIL) to tackle the issue of knowledge inertia. In particular, PIL takes full advantage of the similar image pairs with diverse answers to an identical question provided in VQA2. 0 dataset. It builds a multi-modal embedding space to project pos. /neg. feature pairs, upon which word vectors of answers are modeled as anchors. By doing so, PIL strengthens the importance of visual features in prediction with a novel dynamic-margin based triplet loss that efficiently increases the semantic discrepancies between pos. /neg. image pairs. To verify the proposed PIL, we plug it on a baseline VQA model as well as a set of recent VQA models, and conduct extensive experiments on two benchmark datasets, i. e. , VQA1. 0 and VQA2. 0. Experimental results show that PIL can boost the accuracy of the existing VQA models (1. 56%-2. 93% gain) with a negligible increase in parameters (0. 85%-5. 4% parameters). Qualitative results also reveal the elimination of knowledge inertia in the existing VQA models after implementing our PIL.

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IJCAI Conference 2019 Conference Paper

Hypergraph Induced Convolutional Manifold Networks

  • Taisong Jin
  • Liujuan Cao
  • Baochang Zhang
  • Xiaoshuai Sun
  • Cheng Deng
  • Rongrong Ji

Deep convolutional neural networks (DCNN) with manifold embedding have achieved considerable attention in computer vision. However, prior arts are usually based on the neighborhood-based graph modeling only the pairwise relationship between two samples, which fail to fully capture intra-class variations and thus suffer from severe performance loss for noisy data. While such intra-class variations can be well captured via sophisticated hypergraph structure, we are motivated and lead a hypergraph induced Convolutional Manifold Network (H-CMN) to significantly improve the representation capacity of DCNN for the complex data. Specifically, two innovative designs are provides: 1) our manifold preserving method is implemented based on a mini-batch, which can be efficiently plugged into the existing DCNN training pipelines and be scalable for large datasets; 2) a robust hypergraph is built for each mini-batch, which not only offers a strong robustness against typical noise, but also captures the variances from multiple features. Extensive experiments on the image classification task on large benchmarking datasets demonstrate that our model achieves much better performance than the state-of-the-art

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NeurIPS Conference 2019 Conference Paper

Information Competing Process for Learning Diversified Representations

  • Jie Hu
  • Rongrong Ji
  • Shengchuan Zhang
  • Xiaoshuai Sun
  • Qixiang Ye
  • Chia-Wen Lin
  • Qi Tian

Learning representations with diversified information remains as an open problem. Towards learning diversified representations, a new approach, termed Information Competing Process (ICP), is proposed in this paper. Aiming to enrich the information carried by feature representations, ICP separates a representation into two parts with different mutual information constraints. The separated parts are forced to accomplish the downstream task independently in a competitive environment which prevents the two parts from learning what each other learned for the downstream task. Such competing parts are then combined synergistically to complete the task. By fusing representation parts learned competitively under different conditions, ICP facilitates obtaining diversified representations which contain rich information. Experiments on image classification and image reconstruction tasks demonstrate the great potential of ICP to learn discriminative and disentangled representations in both supervised and self-supervised learning settings.

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AAAI Conference 2019 Conference Paper

Towards Optimal Discrete Online Hashing with Balanced Similarity

  • Mingbao Lin
  • Rongrong Ji
  • Hong Liu
  • Xiaoshuai Sun
  • Yongjian Wu
  • Yunsheng Wu

When facing large-scale image datasets, online hashing serves as a promising solution for online retrieval and prediction tasks. It encodes the online streaming data into compact binary codes, and simultaneously updates the hash functions to renew codes of the existing dataset. To this end, the existing methods update hash functions solely based on the new data batch, without investigating the correlation between such new data and the existing dataset. In addition, existing works update the hash functions using a relaxation process in its corresponding approximated continuous space. And it remains as an open problem to directly apply discrete optimizations in online hashing. In this paper, we propose a novel supervised online hashing method, termed Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above problems in a unified framework. BSODH employs a well-designed hashing algorithm to preserve the similarity between the streaming data and the existing dataset via an asymmetric graph regularization. We further identify the “data-imbalance” problem brought by the constructed asymmetric graph, which restricts the application of discrete optimization in our problem. Therefore, a novel balanced similarity is further proposed, which uses two equilibrium factors to balance the similar and dissimilar weights and eventually enables the usage of discrete optimizations. Extensive experiments conducted on three widely-used benchmarks demonstrate the advantages of the proposed method over the stateof-the-art methods.

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AAAI Conference 2019 Conference Paper

Towards Optimal Fine Grained Retrieval via Decorrelated Centralized Loss with Normalize-Scale Layer

  • Xiawu Zheng
  • Rongrong Ji
  • Xiaoshuai Sun
  • Baochang Zhang
  • Yongjian Wu
  • Feiyue Huang

Recent advances on fine-grained image retrieval prefer learning convolutional neural network (CNN) with specific fullyconnect layer designed loss function for discriminative feature representation. Essentially, such loss should establish a robust metric to efficiently distinguish high-dimensional features within and outside fine-grained categories. To this end, the existing loss functions are defected in two aspects: (a) The feature relationship is encoded inside the training batch. Such a local scope leads to low accuracy. (b) The error is established by the mean square, which needs pairwise distance computation in training set and results in low efficiency. In this paper, we propose a novel metric learning scheme, termed Normalize-Scale Layer and Decorrelated Global Centralized Ranking Loss, which achieves extremely efficient and discriminative learning, i. e. , 5× speedup over triplet loss and 12% recall boost on CARS196. Our method originates from the classic softmax loss, which has a global structure but does not directly optimize the distance metric as well as the inter/intra class distance. We tackle this issue through a hypersphere layer and a global centralized ranking loss with a pairwise decorrelated learning. In particular, we first propose a Normalize-Scale Layer to eliminate the gap between metric distance (for measuring distance in retrieval) and dot product (for dimension reduction in classification). Second, the relationship between features is encoded under a global centralized ranking loss, which targets at optimizing metric distance globally and accelerating learning procedure. Finally, the centers are further decorrelated by Gram-Schmidt process, leading to extreme efficiency (with 20 epochs in training procedure) and discriminability in feature learning. We have conducted quantitative evaluations on two fine-grained retrieval benchmark. The superior performance demonstrates the merits of the proposed approach over the state-of-the-arts.

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NeurIPS Conference 2019 Conference Paper

Variational Structured Semantic Inference for Diverse Image Captioning

  • Fuhai Chen
  • Rongrong Ji
  • Jiayi Ji
  • Xiaoshuai Sun
  • Baochang Zhang
  • Xuri Ge
  • Yongjian Wu
  • Feiyue Huang

Despite the exciting progress in image captioning, generating diverse captions for a given image remains as an open problem. Existing methods typically apply generative models such as Variational Auto-Encoder to diversify the captions, which however neglect two key factors of diverse expression, i. e. , the lexical diversity and the syntactic diversity. To model these two inherent diversities in image captioning, we propose a Variational Structured Semantic Inferring model (termed VSSI-cap) executed in a novel structured encoder-inferer-decoder schema. VSSI-cap mainly innovates in a novel structure, i. e. , Variational Multi-modal Inferring tree (termed VarMI-tree). In particular, conditioned on the visual-textual features from the encoder, the VarMI-tree models the lexical and syntactic diversities by inferring their latent variables (with variations) in an approximate posterior inference guided by a visual semantic prior. Then, a reconstruction loss and the posterior-prior KL-divergence are jointly estimated to optimize the VSSI-cap model. Finally, diverse captions are generated upon the visual features and the latent variables from this structured encoder-inferer-decoder model. Experiments on the benchmark dataset show that the proposed VSSI-cap achieves significant improvements over the state-of-the-arts.

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IJCAI Conference 2018 Conference Paper

Centralized Ranking Loss with Weakly Supervised Localization for Fine-Grained Object Retrieval

  • Xiawu Zheng
  • Rongrong Ji
  • Xiaoshuai Sun
  • Yongjian Wu
  • Feiyue Huang
  • Yanhua Yang

Fine-grained object retrieval has attracted extensive research focus recently. Its state-of-the-art schemesare typically based upon convolutional neural network (CNN) features. Despite the extensive progress, two issues remain open. On one hand, the deep features are coarsely extracted at image level rather than precisely at object level, which are interrupted by background clutters. On the other hand, training CNN features with a standard triplet loss is time consuming and incapable to learn discriminative features. In this paper, we present a novel fine-grained object retrieval scheme that conquers these issues in a unified framework. Firstly, we introduce a novel centralized ranking loss (CRL), which achieves a very efficient (1, 000times training speedup comparing to the triplet loss) and discriminative feature learning by a? centralized? global pooling. Secondly, a weakly supervised attractive feature extraction is proposed, which segments object contours with top-down saliency. Consequently, the contours are integrated into the CNN response map to precisely extract features? within? the target object. Interestingly, we have discovered that the combination of CRL and weakly supervised learning can reinforce each other. We evaluate the performance ofthe proposed scheme on widely-used benchmarks including CUB200-2011 and CARS196. We havereported significant gains over the state-of-the-art schemes, e. g. , 5. 4% over SCDA [Wei et al. , 2017]on CARS196, and 3. 7% on CUB200-2011.

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AAAI Conference 2017 Conference Paper

An Integrated Model for Effective Saliency Prediction

  • Xiaoshuai Sun
  • Zi Huang
  • Hongzhi Yin
  • Heng Tao Shen

In this paper, we proposed an integrated model of both semantic-aware and contrast-aware saliency (SCA) combining both bottom-up and top-down cues for effective eye fixation prediction. The proposed SCA model contains two pathways. The first pathway is a deep neural network customized for semantic-aware saliency, which aims to capture the semantic information in images, especially for the presence of meaningful objects and object parts. The second pathway is based on on-line feature learning and information maximization, which learns an adaptive representation for the input and discovers the high contrast salient patterns within the image context. The two pathways characterize both long-term and short-term attention cues and are integrated using maxima normalization. Experimental results on artificial images and several benchmark dataset demonstrate the superior performance and better plausibility of the proposed model over both classic approaches and recent deep models.

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AAAI Conference 2017 Conference Paper

Web-Based Semantic Fragment Discovery for On-Line Lingual-Visual Similarity

  • Xiaoshuai Sun
  • Jiewei Cao
  • Chao Li
  • Lei Zhu
  • Heng Tao Shen

In this paper, we present an automatic approach for on-line discovery of visual-lingual semantic fragments from weakly labeled Internet images. Instead of learning region-entity correspondences from well-labeled image-sentence pairs, our approach directly collects and enhances the weakly labeled visual contents from the Web and constructs an adaptive visual representation which automatically links generic lingual phrases to their related visual contents. To ensure reliable and efficient semantic discovery, we adopt non-parametric density estimation to re-rank the related visual instances and proposed a fast self-similarity-based quality assessment method to identify the high-quality semantic fragments. The discovered semantic fragments provide an adaptive joint representation for texts and images, based on which lingual-visual similarity can be defined for further co-analysis of heterogeneous multimedia data. Experimental results on semantic fragment quality assessment, sentence-based image retrieval, automatic multimedia insertion and ordering demonstrated the effectiveness of the proposed framework. The experiments show that the proposed methods can make effective use of the Web knowledge, and are able to generate competitive results compared to state-of-the-art approaches in various tasks.

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TIST Journal 2012 Journal Article

Context-Aware Semi-Local Feature Detector

  • Rongrong Ji
  • Hongxun Yao
  • Qi Tian
  • Pengfei Xu
  • Xiaoshuai Sun
  • Xianming Liu

How can interest point detectors benefit from contextual cues? In this articles, we introduce a context-aware semi-local detector (CASL) framework to give a systematic answer with three contributions: (1) We integrate the context of interest points to recurrently refine their detections. (2) This integration boosts interest point detectors from the traditionally local scale to a semi-local scale to discover more discriminative salient regions. (3) Such context-aware structure further enables us to bring forward category learning (usually in the subsequent recognition phase) into interest point detection to locate category-aware, meaningful salient regions. Our CASL detector consists of two phases. The first phase accumulates multiscale spatial correlations of local features into a difference of contextual Gaussians (DoCG) field. DoCG quantizes detector context to highlight contextually salient regions at a semi-local scale, which also reveals visual attentions to a certain extent. The second phase locates contextual peaks by mean shift search over the DoCG field, which subsequently integrates contextual cues into feature description. This phase enables us to integrate category learning into mean shift search kernels. This learning-based CASL mechanism produces more category-aware features, which substantially benefits the subsequent visual categorization process. We conducted experiments in image search, object characterization, and feature detector repeatability evaluations, which reported superior discriminability and comparable repeatability to state-of-the-art works.

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