Author name cluster

Hao Shao

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

2 author rows

ICML Conference 2025 Conference Paper

EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM

Zhuofan Zong
Dongzhi Jiang
Bingqi Ma
Guanglu Song
Hao Shao
Dazhong Shen
Yu Liu 0015
Hongsheng Li 0001

Significant achievements in personalization of diffusion models have been witnessed. Conventional tuning-free methods mostly encode multiple reference images by averaging or concatenating their image embeddings as the injection condition, but such an image-independent operation cannot perform interaction among images to capture consistent visual elements within multiple references. Although tuning-based approaches can effectively extract consistent elements within multiple images through the training process, it necessitates test-time finetuning for each distinct image group. This paper introduces EasyRef, a plug-and-play adaption method that empowers diffusion models to condition consistent visual elements (e. g. , style and human facial identity, etc.) across multiple reference images under instruction controls. To effectively exploit consistent visual elements within multiple images, we leverage the multi-image comprehension and instruction-following capabilities of the multimodal large language model (MLLM), prompting it to capture consistent visual elements based on the instruction. Besides, injecting the MLLM’s representations into the diffusion process through adapters can easily generalize to unseen domains. To mitigate computational costs and enhance fine-grained detail preservation, we introduce an efficient reference aggregation strategy and a progressive training scheme. Finally, we introduce MRBench, a new multi-reference image generation benchmark. Experimental results demonstrate EasyRef surpasses both tuning-free and tuning-based methods, achieving superior aesthetic quality and robust zero-shot generalization across diverse domains.

Details

ICLR Conference 2025 Conference Paper

SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction

Yang Zhou 0019
Hao Shao
Letian Wang
Steven L. Waslander
Hongsheng Li 0001
Yu Liu 0015

Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic, human-robot-mixed environments. However, the scarcity of large-scale driving datasets has hindered the development of robust and generalizable motion prediction models, limiting their ability to capture complex interactions and road geometries. Inspired by recent advances in natural language processing (NLP) and computer vision (CV), self-supervised learning (SSL) has gained significant attention in the motion prediction community for learning rich and transferable scene representations. Nonetheless, existing pre-training methods for motion prediction have largely focused on specific model architectures and single dataset, limiting their scalability and generalizability. To address these challenges, we propose SmartPretrain, a general and scalable SSL framework for motion prediction that is both model-agnostic and dataset-agnostic. Our approach integrates contrastive and reconstructive SSL, leveraging the strengths of both generative and discriminative paradigms to effectively represent spatiotemporal evolution and interactions without imposing architectural constraints. Additionally, SmartPretrain employs a dataset-agnostic scenario sampling strategy that integrates multiple datasets, enhancing data volume, diversity, and robustness. Extensive experiments on multiple datasets demonstrate that SmartPretrain consistently improves the performance of state-of-the-art prediction models across datasets, data splits and main metrics. For instance, SmartPretrain significantly reduces the MissRate of Forecast-MAE by 10.6\%. These results highlight SmartPretrain's effectiveness as a unified, scalable solution for motion prediction, breaking free from the limitations of the small-data regime.

Details

NeurIPS Conference 2025 Conference Paper

VividFace: A Robost and High-Fidelity Video Face Swapping Framework

Hao Shao
Shulun Wang
Yang Zhou
Guanglu Song
Dailan He
Zhuofan Zong
Shuo Qin
Yu Liu

Video face swapping has seen increasing adoption in diverse applications, yet existing methods primarily trained on static images struggle to address temporal consistency and complex real-world scenarios. To overcome these limitations, we propose the first video face swapping framework, VividFace, a robust and high-fidelity diffusion-based framework. VividFace employs a novel hybrid training strategy that leverages abundant static image data alongside temporal video sequences, enabling it to effectively model temporal coherence and identity consistency in videos. Central to our approach is a carefully designed diffusion model integrated with a specialized VAE, capable of processing image-video hybrid data efficiently. To further enhance identity and pose disentanglement, we introduce and release the Attribute-Identity Disentanglement Triplet (AIDT) dataset, comprising a large-scale collection of triplets where each set contains three face images—two sharing the same pose and two sharing the same identity. Augmented comprehensively with occlusion scenarios, AIDT significantly boosts the robustness of VividFace against occlusions. Moreover, we incorporate advanced 3D reconstruction techniques as conditioning inputs to address significant pose variations effectively. Extensive experiments demonstrate that VividFace achieves state-of-the-art performance in identity preservation, temporal consistency, and visual realism, surpassing existing methods while requiring fewer inference steps. Our framework notably mitigates common challenges such as temporal flickering, identity loss, and sensitivity to occlusions and pose variations. The AIDT dataset, source code, and pre-trained weights will be released to support future research. The code and pretrained weights are available on the project page.

PDF Details

NeurIPS Conference 2024 Conference Paper

MoVA: Adapting Mixture of Vision Experts to Multimodal Context

Zhuofan Zong
Bingqi Ma
Dazhong Shen
Guanglu Song
Hao Shao
Dongzhi JIANG
Hongsheng Li
Yu Liu

As the key component in multimodal large language models (MLLMs), the ability of the visual encoder greatly affects MLLM's understanding on diverse image content. Although some large-scale pretrained vision encoders such as vision encoders in CLIP and DINOv2 have brought promising performance, we found that there is still no single vision encoder that can dominate various image content understanding, e. g. , the CLIP vision encoder leads to outstanding results on general image understanding but poor performance on document or chart content. To alleviate the bias of CLIP vision encoder, we first delve into the inherent behavior of different pre-trained vision encoders and then propose the MoVA, a powerful and novel MLLM, adaptively routing and fusing task-specific vision experts with a coarse-to-fine mechanism. In the coarse-grained stage, we design a context-aware expert routing strategy to dynamically select the most suitable vision experts according to the user instruction, input image, and expertise of vision experts. This benefits from the powerful model function understanding ability of the large language model (LLM). In the fine-grained stage, we elaborately conduct the mixture-of-vision-expert adapter (MoV-Adapter) to extract and fuse task-specific knowledge from various experts. This coarse-to-fine paradigm effectively leverages representations from experts based on multimodal context and model expertise, further enhancing the generalization ability. We conduct extensive experiments to evaluate the effectiveness of the proposed approach. Without any bells and whistles, MoVA can achieve significant performance gains over current state-of-the-art methods in a wide range of challenging multimodal benchmarks.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Polyper: Boundary Sensitive Polyp Segmentation

Hao Shao
Yang Zhang
Qibin Hou

We present a new boundary sensitive framework for polyp segmentation, termed Polyper.Our method is motivated by a clinical approach that seasoned medical practitioners often leverage the inherent features of interior polyp regions to tackle blurred boundaries.Inspired by this, we propose to explicitly leverages boundary regions to bolster the model's boundary discrimination capability while minimizing computational resource wastage. Our approach first extracts low-confidence boundary regions and high-confidence prediction regions from an initial segmentation map through differentiable morphological operators.Then, we design the boundary sensitive attention that concentrates on augmenting the features near the boundary regions using the high-confidence prediction region's characteristics to generate good segmentation results.Our proposed method can be seamlessly integrated with classical encoder networks, like ResNet-50, MiT-B1, and Swin Transformer.To evaludate the effectiveness of Polyper, we conduct experiments on five publicly available challenging datasets, and receive state-of-the-art performance on all of them. Code is available at https://github.com/haoshao-nku/medical_seg.git.

PDF Details DOI

ICML Conference 2024 Conference Paper

SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models

Dongyang Liu
Renrui Zhang
Longtian Qiu
Siyuan Huang 0004
Weifeng Lin
Shitian Zhao
Shijie Geng
Ziyi Lin

We propose SPHINX-X, an extensive Multi-modality Large Language Model (MLLM) series developed upon SPHINX. To improve the architecture and training efficiency, we modify the SPHINX framework by removing redundant visual encoders, bypassing fully-padded sub-images with skip tokens, and simplifying multi-stage training into a one-stage all-in-one paradigm. To fully unleash the potential of MLLMs, we assemble a comprehensive multi-domain and multi-modal dataset covering publicly available resources in language, vision, and vision-language tasks. We further enrich this collection with our curated OCR intensive and Set-of-Mark datasets, extending the diversity and generality. By training over different base LLMs including TinyLlama-1. 1B, InternLM2-7B, LLaMA2-13B, and Mixtral-8$\times$7B, we obtain a spectrum of MLLMs that vary in parameter size and multilingual capabilities. Comprehensive benchmarking reveals a strong correlation between the multi-modal performance with the data and parameter scales. Code and models are released at https: //github. com/Alpha-VLLM/LLaMA2-Accessory.

Details

NeurIPS Conference 2024 Conference Paper

Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning

Hao Shao
Shengju Qian
Han Xiao
Guanglu Song
Zhuofan Zong
Letian Wang
Yu Liu
Hongsheng Li

Multi-Modal Large Language Models (MLLMs) have demonstrated impressive performance in various VQA tasks. However, they often lack interpretability and struggle with complex visual inputs, especially when the resolution of the input image is high or when the interested region that could provide key information for answering the question is small. To address these challenges, we collect and introduce the large-scale Visual CoT dataset comprising 438k question-answer pairs, annotated with intermediate bounding boxes highlighting key regions essential for answering the questions. Additionally, about 98k pairs of them are annotated with detailed reasoning steps. Importantly, we propose a multi-turn processing pipeline that dynamically focuses on visual inputs and provides interpretable thoughts. We also introduce the related benchmark to evaluate the MLLMs in scenarios requiring specific local region identification. Extensive experiments demonstrate the effectiveness of our framework and shed light on better inference strategies. The Visual CoT dataset, benchmark, and pre-trained models are available on this website to support further research in this area.

PDF Details DOI

NeurIPS Conference 2021 Conference Paper

Blending Anti-Aliasing into Vision Transformer

Shengju Qian
Hao Shao
Yi Zhu
Mu Li
Jiaya Jia

The transformer architectures, based on self-attention mechanism and convolution-free design, recently found superior performance and booming applications in computer vision. However, the discontinuous patch-wise tokenization process implicitly introduces jagged artifacts into attention maps, arising the traditional problem of aliasing for vision transformers. Aliasing effect occurs when discrete patterns are used to produce high frequency or continuous information, resulting in the indistinguishable distortions. Recent researches have found that modern convolution networks still suffer from this phenomenon. In this work, we analyze the uncharted problem of aliasing in vision transformer and explore to incorporate anti-aliasing properties. Specifically, we propose a plug-and-play Aliasing-Reduction Module (ARM) to alleviate the aforementioned issue. We investigate the effectiveness and generalization of the proposed method across multiple tasks and various vision transformer families. This lightweight design consistently attains a clear boost over several famous structures. Furthermore, our module also improves data efficiency and robustness of vision transformers.

PDF Details

AAAI Conference 2020 Conference Paper

Temporal Interlacing Network

Hao Shao
Shengju Qian
Yu Liu

For a long time, the vision community tries to learn the spatio-temporal representation by combining convolutional neural network together with various temporal models, such as the families of Markov chain, optical ﬂow, RNN and temporal convolution. However, these pipelines consume enormous computing resources due to the alternately learning process for spatial and temporal information. One natural question is whether we can embed the temporal information into the spatial one so the information in the two domains can be jointly learned once-only. In this work, we answer this question by presenting a simple yet powerful operator – temporal interlacing network (TIN). Instead of learning the temporal features, TIN fuses the two kinds of information by interlacing spatial representations from the past to the future, and vice versa. A differentiable interlacing target can be learned to control the interlacing process. In this way, a heavy temporal model is replaced by a simple interlacing operator. We theoretically prove that with a learnable interlacing target, TIN performs equivalently to the regularized temporal convolution network (r-TCN), but gains 4% more accuracy with 6x less latency on 6 challenging benchmarks. These results push the state-of-the-art performances of video understanding by a considerable margin. Not surprising, the ensemble model of the proposed TIN won the 1st place in the ICCV19 - Multi Moments in Time challenge. Code is made available to facilitate further research. 1

PDF Details

AIJ Journal 2019 Journal Article

Syntax-aware entity representations for neural relation extraction

Zhengqiu He
Wenliang Chen
Zhenghua Li
Wei Zhang
Hao Shao
Min Zhang

Distantly supervised relation extraction has been widely used to find novel relational facts between entities from text, and can be easily scaled to very large corpora. Previous studies on neural relation extraction treat this task as a multi-instance learning problem, and encode the sentences in low-dimensional spaces via neural networks. Although great progress has been made, they seldom consider the information represented by entities, which are of great significance to relation extraction. In this article, we propose several methods based on different tree-based models to learn syntax-aware entity representations for neural relation extraction. First, we encode the context of entities on dependency trees as sentence-level entity embedding based on tree-structured neural network models. Then, we utilize inter-sentence attention mechanism to obtain sentence bag level entity embedding over all sentences containing the specified entity pair. Finally, we combine both sentence embedding and entity embedding for relation classification. Experimental results on a widely used real-world dataset indicate that our system performs better than the state-of-the-art systems of relation extraction.

Details DOI

ICRA Conference 1999 Conference Paper

Development & Application of Wall-Climbing Robots

Yan Wang 0001
Shuliang Liu
Dianguo Xu 0001
Yanzheng Zhao
Hao Shao
Xueshan Gao

In this paper we introduce wall-climbing robots of two different absorption schemes: one with single suction cup and the other with permanent magnetic crawlers. The former adopts an omnidirectional vehicle and can perform remote-control inspection of nuclear storage tanks. Moreover, it is further developed for cleaning both the ceramic tile and glass surfaces of high-rise buildings. The latter consists of two types of robots: one is for maintenance automation of storage tanks in petrochemical enterprises, which can perform operations of sand-blasting, spray-painting and inspection; and the other is for monitoring the boiler wall water tubing.

Details