Xing Xu

EAAI Journal 2026 Journal Article

Bimodal temporal modeling reinforcement learning with safety mechanism for highway lane change in mixed traffic

• Xing Xu
• Tingpeng Shi
• Zhang Zhang
• Yun Zhao
• Yali Wang
• Yanan Mi

AAAI Conference 2026 Conference Paper

De-biased Natural Language Egocentric Task Verification via Prototypical Evidence Learning

• Chong Liu
• Xun Jiang
• Fumin Shen
• Lei Zhu
• Jingkuan Song
• Heng Tao Shen
• Xing Xu

Natural Language-based Egocentric Task Verification (NLETV) aims to verify the alignment between action sequences in egocentric videos and their corresponding textual descriptions. However, existing NLETV approaches are still facing two critical challenges: (1) These methods are designed for simulating environments, ignoring the domain gap between synthetic and realistic data. (2) The matching processes are regarded as a simple binary classification problem, which undermines model reliability due to evaluation bias and uncalibrated decision settings. To address these challenges, we propose a novel method termed Prototypical Evidential Learning (PEL), which can be adapted to existing NLETV approaches and boost the model generalization and mitigate prediction bias. Our method leverages prototypes to guide cross-domain alignment and evidence collection. Specifically, PEL consists of two key components: (1) Prototypical Domain Adaptation module enabling cross-domain feature alignment and intra-domain prototype preservation between synthetic and realistic domains; (2) Matching Evidence Collector module, which quantifies prediction uncertainty on the prototypical representations through evidential deep learning. It enforces the model to collect the vision-text consistency and discrepancy evidence, thus addressing the issues of biased decisions in binary classification. Extensive experiments on two public datasets demonstrate that our PEL method outperforms existing state-of-the-art NLETV methods and shows remarkable generalizability.

AAAI Conference 2026 Conference Paper

Hyper-Opinion Vagueness Quantification for Robust Multimodal Learning

• Disen Hu
• Xun Jiang
• Xiaofeng Cao
• Zheng Wang
• Jingkuan Song
• Heng Tao Shen
• Xing Xu

Robust Multimodal Learning (RML) aims to address the issues of unreliable predictions of multimodal models. Nevertheless, previous RML works often struggle to distinguish between different categories that rely on identical intra-modal cues, making ambiguous predictions. We defined this degree of ``uncertain'' in extracting discriminative features of a multimodal model as vagueness. Neglecting such vagueness, as previous RML works commonly do, will undermine the ability to extract unique semantics of each category in multimodal models, further resulting in worse robustness under disturbances that affect semantic representations. Additionally, this vagueness will lead the parameter updating processes towards unreliable fusion, thus diverting the learning processes of the multimodal model from learning unique features of each category. Based on the above insight, we propose a novel robust multimodal learning approach, termed Hyper-Opinion Quantifying Vagueness (HOQV). Specifically, we first introduce hyper-opinion to capture and quantify the vagueness of multimodal learning in discriminating representations of different categories. Moreover, to mitigate the interference in parameter updating of unreliable representations with high vagueness, we also design the Hyper-Opinion Gradient Modulation to guide the optimization processes. We evaluate our HOQV on six datasets with different disturbances, including noise and adversarial attack, and demonstrate that our proposed method achieves state-of-the-art performance consistently.

AAAI Conference 2024 Conference Paper

Adaptive Uncertainty-Based Learning for Text-Based Person Retrieval

• Shenshen Li
• Chen He
• Xing Xu
• Fumin Shen
• Yang Yang
• Heng Tao Shen

Text-based person retrieval aims at retrieving a specific pedestrian image from a gallery based on textual descriptions. The primary challenge is how to overcome the inherent heterogeneous modality gap in the situation of significant intra-class variation and minimal inter-class variation. Existing approaches commonly employ vision-language pre-training or attention mechanisms to learn appropriate cross-modal alignments from noise inputs. Despite commendable progress, current methods inevitably suffer from two defects: 1) Matching ambiguity, which mainly derives from unreliable matching pairs; 2) One-sided cross-modal alignments, stemming from the absence of exploring one-to-many correspondence, i.e., coarse-grained semantic alignment. These critical issues significantly deteriorate retrieval performance. To this end, we propose a novel framework termed Adaptive Uncertainty-based Learning (AUL) for text-based person retrieval from the uncertainty perspective. Specifically, our AUL framework consists of three key components: 1) Uncertainty-aware Matching Filtration that leverages Subjective Logic to effectively mitigate the disturbance of unreliable matching pairs and select high-confidence cross-modal matches for training; 2) Uncertainty-based Alignment Refinement, which not only simulates coarse-grained alignments by constructing uncertainty representations but also performs progressive learning to incorporate coarse- and fine-grained alignments properly; 3) Cross-modal Masked Modeling that aims at exploring more comprehensive relations between vision and language. Extensive experiments demonstrate that our AUL method consistently achieves state-of-the-art performance on three benchmark datasets in supervised, weakly supervised, and domain generalization settings. Our code is available at https://github.com/CFM-MSG/Code-AUL.

AAAI Conference 2024 Conference Paper

T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering

• Lei Wang
• Yi Hu
• Jiabang He
• Xing Xu
• Ning Liu
• Hui Liu
• Heng Tao Shen

Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal scenarios, such as the science question answering task, by fine-tuning multimodal models with high-quality human-annotated CoT rationales. However, collecting high-quality COT rationales is usually time-consuming and costly. Besides, the annotated rationales are hardly accurate due to the external essential information missed. To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with LLM signals. The T-SciQ approach generates high-quality CoT rationales as teaching signals and is advanced to train much smaller models to perform CoT reasoning in complex modalities. Additionally, we introduce a novel data mixing strategy to produce more effective teaching data samples for simple and complex science question answer problems. Extensive experimental results show that our T-SciQ method achieves a new state-of-the-art performance on the ScienceQA benchmark, with an accuracy of 96.18%. Moreover, our approach outperforms the most powerful fine-tuned baseline by 4.5%. The code is publicly available at https://github.com/T-SciQ/T-SciQ.

AAAI Conference 2023 Conference Paper

Alignment-Enriched Tuning for Patch-Level Pre-trained Document Image Models

• Lei Wang
• Jiabang He
• Xing Xu
• Ning Liu
• Hui Liu

Alignment between image and text has shown promising improvements on patch-level pre-trained document image models. However, investigating more effective or finer-grained alignment techniques during pre-training requires a large amount of computation cost and time. Thus, a question naturally arises: Could we fine-tune the pre-trained models adaptive to downstream tasks with alignment objectives and achieve comparable or better performance? In this paper, we propose a new model architecture with alignment-enriched tuning (dubbed AETNet) upon pre-trained document image models, to adapt downstream tasks with the joint task-specific supervised and alignment-aware contrastive objective. Specifically, we introduce an extra visual transformer as the alignment-ware image encoder and an extra text transformer as the alignment-ware text encoder before multimodal fusion. We consider alignment in the following three aspects: 1) document-level alignment by leveraging the cross-modal and intra-modal contrastive loss; 2) global-local alignment for modeling localized and structural information in document images; and 3) local-level alignment for more accurate patch-level information. Experiments on various downstream tasks show that AETNet can achieve state-of-the-art performance on various downstream tasks. Notably, AETNet consistently outperforms state-of-the-art pre-trained models, such as LayoutLMv3 with fine-tuning techniques, on three different downstream tasks. Code is available at https://github.com/MAEHCM/AET.

AAAI Conference 2022 Conference Paper

TVT: Three-Way Vision Transformer through Multi-Modal Hypersphere Learning for Zero-Shot Sketch-Based Image Retrieval

• Jialin Tian
• Xing Xu
• Fumin Shen
• Yang Yang
• Heng Tao Shen

In this paper, we study the zero-shot sketch-based image retrieval (ZS-SBIR) task, which retrieves natural images related to sketch queries from unseen categories. In the literature, convolutional neural networks (CNNs) have become the defacto standard and they are either trained end-to-end or used to extract pre-trained features for images and sketches. However, CNNs are limited in modeling the global structural information of objects due to the intrinsic locality of convolution operations. To this end, we propose a Transformerbased approach called Three-Way Vision Transformer (TVT) to leverage the ability of Vision Transformer (ViT) to model global contexts due to the global self-attention mechanism. Going beyond simply applying ViT to this task, we propose a token-based strategy of adding fusion and distillation tokens and making them complementary to each other. Specifically, we integrate three ViTs, which are pre-trained on data of each modality, into a three-way pipeline through the processes of distillation and multi-modal hypersphere learning. The distillation process is proposed to supervise fusion ViT (ViT with an extra fusion token) with soft targets from modality-specific ViTs, which prevent fusion ViT from catastrophic forgetting. Furthermore, our method learns a multi-modal hypersphere by performing inter- and intra-modal alignment without loss of uniformity, which aims to bridge the modal gap between modalities of sketch and image and avoid the collapse in dimensions. Extensive experiments on three benchmark datasets, i. e. , Sketchy, TU-Berlin, and QuickDraw, demonstrate the superiority of our TVT method over the state-ofthe-art ZS-SBIR methods.

AAAI Conference 2021 Conference Paper

Enhancing Audio-Visual Association with Self-Supervised Curriculum Learning

• Jingran Zhang
• Xing Xu
• Fumin Shen
• Huimin Lu
• Xin Liu
• Heng Tao Shen

The recent success of audio-visual representations learning can be largely attributed to their pervasive concurrency property, which can be used as a self-supervision signal and extract correlation information. While most recent works focus on capturing the shared associations between the audio and visual modalities, they rarely consider multiple audio and video pairs at once and pay little attention to exploiting the valuable information within each modality. To tackle this problem, we propose a novel audio-visual representation learning method dubbed self-supervised curriculum learning (SSCL) under the teacher-student learning manner. Specifically, taking advantage of contrastive learning, a twostage scheme is exploited, which transfers the cross-modal information between teacher and student model as a phased process. The proposed SSCL approach regards the pervasive property of audiovisual concurrency as latent supervision and mutually distills the structure knowledge of visual to audio data. Notably, the SSCL method can learn discriminative audio and visual representations for various downstream applications. Extensive experiments conducted on both action video recognition and audio sound recognition tasks show the remarkably improved performance of the SSCL method compared with the state-of-the-art self-supervised audio-visual representation learning methods.

IJCAI Conference 2021 Conference Paper

Feature Space Targeted Attacks by Statistic Alignment

• Lianli Gao
• Yaya Cheng
• Qilong Zhang
• Xing Xu
• Jingkuan Song

By adding human-imperceptible perturbations to images, DNNs can be easily fooled. As one of the mainstream methods, feature space targeted attacks perturb images by modulating their intermediate feature maps, for the discrepancy between the intermediate source and target features is minimized. However, the current choice of pixel-wise Euclidean Distance to measure the discrepancy is questionable because it unreasonably imposes a spatial-consistency constraint on the source and target features. Intuitively, an image can be categorized as "cat'' no matter the cat is on the left or right of the image. To address this issue, we propose to measure this discrepancy using statistic alignment. Specifically, we design two novel approaches called Pair-wise Alignment Attack and Global-wise Alignment Attack, which attempt to measure similarities between feature maps by high-order statistics with translation invariance. Furthermore, we systematically analyze the layer-wise transferability with varied difficulties to obtain highly reliable attacks. Extensive experiments verify the effectiveness of our proposed method, and it outperforms the state-of-the-art algorithms by a large margin. Our code is publicly available at https: //github. com/yaya-cheng/PAA-GAA.

IJCAI Conference 2021 Conference Paper

PoseGTAC: Graph Transformer Encoder-Decoder with Atrous Convolution for 3D Human Pose Estimation

• Yiran Zhu
• Xing Xu
• Fumin Shen
• Yanli Ji
• Lianli Gao
• Heng Tao Shen

Graph neural networks (GNNs) have been widely used in the 3D human pose estimation task, since the pose representation of a human body can be naturally modeled by the graph structure. Generally, most of the existing GNN-based models utilize the restricted receptive fields of filters and single-scale information, while neglecting the valuable multi-scale contextual information. To tackle this issue, we propose a novel Graph Transformer Encoder-Decoder with Atrous Convolution, named PoseGTAC, to effectively extract multi-scale context and long-range information. In our proposed PoseGTAC model, Graph Atrous Convolution (GAC) and Graph Transformer Layer (GTL), respectively for the extraction of local multi-scale and global long-range information, are combined and stacked in an encoder-decoder structure, where graph pooling and unpooling are adopted for the interaction of multi-scale information from local to global (e. g. , part-scale and body-scale). Extensive experiments on the Human3. 6M and MPI-INF-3DHP datasets demonstrate that the proposed PoseGTAC model exceeds all previous methods and achieves state-of-the-art performance.

AAAI Conference 2020 Conference Paper

Learning Cross-Aligned Latent Embeddings for Zero-Shot Cross-Modal Retrieval

• Kaiyi Lin
• Xing Xu
• Lianli Gao
• Zheng Wang
• Heng Tao Shen

Zero-Shot Cross-Modal Retrieval (ZS-CMR) is an emerging research hotspot that aims to retrieve data of new classes across different modality data. It is challenging for not only the heterogeneous distributions across different modalities, but also the inconsistent semantics across seen and unseen classes. A handful of recently proposed methods typically borrow the idea from zero-shot learning, i. e. , exploiting word embeddings of class labels (i. e. , class-embeddings) as common semantic space, and using generative adversarial network (GAN) to capture the underlying multimodal data structures, as well as strengthen relations between input data and semantic space to generalize across seen and unseen classes. In this paper, we propose a novel method termed Learning Cross-Aligned Latent Embeddings (LCALE) as an alternative to these GAN based methods for ZS-CMR. Unlike using the class-embeddings as the semantic space, our method seeks for a shared low-dimensional latent space of input multimodal features and class-embeddings by modalityspecific variational autoencoders. Notably, we align the distributions learned from multimodal input features and from class-embeddings to construct latent embeddings that contain the essential cross-modal correlation associated with unseen classes. Effective cross-reconstruction and crossalignment criterions are further developed to preserve classdiscriminative information in latent space, which benefits the efficiency for retrieval and enable the knowledge transfer to unseen classes. We evaluate our model using four benchmark datasets on image-text retrieval tasks and one largescale dataset on image-sketch retrieval tasks. The experimental results show that our method establishes the new state-ofthe-art performance for both tasks on all datasets.

AAAI Conference 2019 Conference Paper

Deliberate Attention Networks for Image Captioning

• Lianli Gao
• Kaixuan Fan
• Jingkuan Song
• Xianglong Liu
• Xing Xu
• Heng Tao Shen

In daily life, deliberation is a common behavior for human to improve or refine their work (e. g. , writing, reading and drawing). To date, encoder-decoder framework with attention mechanisms has achieved great progress for image captioning. However, such framework is in essential an one-pass forward process while encoding to hidden states and attending to visual features, but lacks of the deliberation action. The learned hidden states and visual attention are directly used to predict the final captions without further polishing. In this paper, we present a novel Deliberate Residual Attention Network, namely DA, for image captioning. The first-pass residual-based attention layer prepares the hidden states and visual attention for generating a preliminary version of the captions, while the second-pass deliberate residual-based attention layer refines them. Since the second-pass is based on the rough global features captured by the hidden layer and visual attention in the first-pass, our DA has the potential to generate better sentences. We further equip our DA with discriminative loss and reinforcement learning to disambiguate image/caption pairs and reduce exposure bias. Our model improves the state-of-the-arts on the MSCOCO dataset and reaches 37. 5% BELU-4, 28. 5% ME- TEOR and 125. 6% CIDEr. It also outperforms the-state-ofthe-arts from 25. 1% BLEU-4, 20. 4% METEOR and 53. 1% CIDEr to 29. 4% BLEU-4, 23. 0% METEOR and 66. 6% on the Flickr30K dataset.

AAAI Conference 2019 Conference Paper

Perceptual Pyramid Adversarial Networks for Text-to-Image Synthesis

• Lianli Gao
• Daiyuan Chen
• Jingkuan Song
• Xing Xu
• Dongxiang Zhang
• Heng Tao Shen

Generating photo-realistic images conditioned on semantic text descriptions is a challenging task in computer vision field. Due to the nature of hierarchical representations learned in CNN, it is intuitive to utilize richer convolutional features to improve text-to-image synthesis. In this paper, we propose Perceptual Pyramid Adversarial Network (PPAN) to directly synthesize multi-scale images conditioned on texts in an adversarial way. Specifically, we design one pyramid generator and three independent discriminators to synthesize and regularize multi-scale photo-realistic images in one feed-forward process. At each pyramid level, our method takes coarse-resolution features as input, synthesizes highresolution images, and uses convolutions for up-sampling to finer level. Furthermore, the generator adopts the perceptual loss to enforce semantic similarity between the synthesized image and the ground truth, while a multi-purpose discriminator encourages semantic consistency, image fidelity and class invariance. Experimental results show that our PPAN sets new records for text-to-image synthesis on two benchmark datasets: CUB (i. e. , 4. 38 Inception Score and. 290 Visual-semantic Similarity) and Oxford-102 (i. e. , 3. 52 Inception Score and. 297 Visual-semantic Similarity).

AAAI Conference 2019 Conference Paper

Template-Based Math Word Problem Solvers with Recursive Neural Networks

• Lei Wang
• Dongxiang Zhang
• Jipeng Zhang
• Xing Xu
• Lianli Gao
• Bing Tian Dai
• Heng Tao Shen

The design of automatic solvers to arithmetic math word problems has attracted considerable attention in recent years and a large number of datasets and methods have been published. Among them, Math23K is the largest data corpus that is very helpful to evaluate the generality and robustness of a proposed solution. The best performer in Math23K is a seq2seq model based on LSTM to generate the math expression. However, the model suffers from performance degradation in large space of target expressions. In this paper, we propose a template-based solution based on recursive neural network for math expression construction. More specifically, we first apply a seq2seq model to predict a tree-structure template, with inferred numbers as leaf nodes and unknown operators as inner nodes. Then, we design a recursive neural network to encode the quantity with Bi-LSTM and self attention, and infer the unknown operator nodes in a bottom-up manner. The experimental results clearly establish the superiority of our new framework as we improve the accuracy by a wide margin in two of the largest datasets, i. e. , from 58. 1% to 66. 9% in Math23K and from 62. 8% to 66. 8% in MAWPS.

AAAI Conference 2018 Conference Paper

Binary Generative Adversarial Networks for Image Retrieval

• Jingkuan Song
• Tao He
• Lianli Gao
• Xing Xu
• Alan Hanjalic
• Heng Tao Shen

The most striking successes in image retrieval using deep hashing have mostly involved discriminative models, which require labels. In this paper, we use binary generative adversarial networks (BGAN) to embed images to binary codes in an unsupervised way. By restricting the input noise variable of generative adversarial networks (GAN) to be binary and conditioned on the features of each input image, BGAN can simultaneously learn a binary representation per image, and generate an image plausibly similar to the original one. In the proposed framework, we address two main problems: 1) how to directly generate binary codes without relaxation? 2) how to equip the binary representation with the ability of accurate image retrieval? We resolve these problems by proposing new sign-activation strategy and a loss function steering the learning process, which consists of new models for adversarial loss, a content loss, and a neighborhood structure loss. Experimental results on standard datasets (CIFAR-10, NUSWIDE, and Flickr) demonstrate that our BGAN significantly outperforms existing hashing methods by up to 107% in terms of mAP (See Table 2)1.

AAAI Conference 2018 Conference Paper

Deep Region Hashing for Generic Instance Search from Images

• Jingkuan Song
• Tao He
• Lianli Gao
• Xing Xu
• Heng Tao Shen

Instance Search (INS) is a fundamental problem for many applications, while it is more challenging comparing to traditional image search since the relevancy is defined at the instance level. Existing works have demonstrated the success of many complex ensemble systems that are typically conducted by firstly generating object proposals, and then extracting handcrafted and/or CNN features of each proposal for matching. However, object bounding box proposals and feature extraction are often conducted in two separated steps, thus the effectiveness of these methods collapses. Also, due to the large amount of generated proposals, matching speed becomes the bottleneck that limits its application to largescale datasets. To tackle these issues, in this paper we propose an effective and efficient Deep Region Hashing (DRH) approach for large-scale INS using an image patch as the query. Specifically, DRH is an end-to-end deep neural network which consists of object proposal, feature extraction, and hash code generation. DRH shares full-image convolutional feature map with the region proposal network, thus enabling nearly cost-free region proposals. Also, each highdimensional, real-valued region features are mapped onto a low-dimensional, compact binary codes for the efficient object region level matching on large-scale dataset. Experimental results on four datasets show that our DRH can achieve even better performance than the state-of-the-arts in terms of mAP, while the efficiency is improved by nearly 100 times.