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Wengang Zhou

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AAAI Conference 2026 Conference Paper

DocR1: Evidence Page-Guided GRPO for Multi-Page Document Understanding

  • Junyu Xiong
  • Yonghui Wang
  • Weichao Zhao
  • Chenyu Liu
  • Bing Yin
  • Wengang Zhou
  • Houqiang Li

Understanding multi-page documents poses a significant challenge for multimodal large language models (MLLMs), as it requires fine-grained visual comprehension and multi-hop reasoning across pages. While prior work has explored reinforcement learning (RL) for enhancing advanced reasoning in MLLMs, its application to multi-page document understanding remains underexplored. In this paper, we introduce DocR1, an MLLM trained with a novel RL framework, Evidence Page-Guided GRPO (EviGRPO). EviGRPO incorporates an evidence-aware reward mechanism that promotes a coarse-to-fine reasoning strategy, guiding the model to first retrieve relevant pages before generating answers. To support this, we design a rigorous two-stage annotation pipeline and a curriculum learning strategy that enables effective training with limited supervision. Using this pipeline, we construct two datasets: EviBench, a high-quality training set with 4.8k examples, and ArxivFullQA, a benchmark with 8.6k QA examples over full scientific papers. Extensive experiments across a wide range of benchmarks demonstrate that DocR1 achieves state-of-the-art performance on multi-page tasks while maintaining strong results on single-page benchmarks.

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AAMAS Conference 2026 Conference Paper

Offline Multi-Agent Reinforcement Learning with Global Moderate Generalization

  • Yuanrui Duan
  • Wengang Zhou
  • Yufeng Shi
  • Xiancheng Gao
  • Lin Liu
  • Houqiang Li

Offline multi-agent reinforcement learning (MARL) suffers from severe value overestimation and extrapolation errors. These challenges lead to over-generalization when value functions or policies encounterout-of-distribution(OOD)actions. Theconsiderablebody of work on generalization issues has yielded successful in-sample learning methods, which avoid OOD actions altogether. However, we argue that the conservatism inherent in this approach could impose unnecessary limitations. This study demonstrates that moderate generalization can be both reliable and beneficial for improving performance. Building on this insight, this paper introduces the offline multi-agent reinforcement learning algorithm with global moderate generalization (OMGMG). OMGMG enforces moderate generalization at the global level and dynamically distributes the generalization effects to individual agents through value decomposition techniques, thereby achieving macro-level control over the generalization process. OMGMG comprises two core components: global moderate action generalization and global moderate generalization propagation. The former approach improves value function estimation by selecting joint actions within the vicinity of the dataset. The latter approach ensures the effective propagation of reinforcement learning signals while mitigating the issue of erroneous generalization propagation during the bootstrapping process. Extensive experimental evaluations on the multi-agent Mujoco and StarCraft II benchmark demonstrate that our OMGMG surpasses the current state-of-the-art offline MARL methods across the majority of tasks.

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AAAI Conference 2026 Conference Paper

Rethinking Long-tailed Dataset Distillation: A Uni-Level Framework with Unbiased Recovery and Relabeling

  • Xiao Cui
  • Yulei Qin
  • Xinyue Li
  • Wengang Zhou
  • Hongsheng Li
  • Houqiang Li

Dataset distillation creates a small distilled set that enables efficient training by capturing key information from the full dataset. While existing dataset distillation methods perform well on balanced datasets, they struggle under long-tailed distributions, where imbalanced class frequencies induce biased model representations and corrupt statistical estimates such as Batch Normalization (BN) statistics. In this paper, we rethink long-tailed dataset distillation by revisiting the limitations of trajectory-based methods, and instead adopt the statistical alignment perspective to jointly mitigate model bias and restore fair supervision. To this end, we introduce three dedicated components that enable unbiased recovery of distilled images and soft relabeling: (1) enhancing expert models (an observer model for recovery and a teacher model for relabeling) to enable reliable statistics estimation and soft-label generation; (2) recalibrating BN statistics via a full forward pass with dynamically adjusted momentum to reduce representation skew; (3) initializing synthetic images by incrementally selecting high-confidence and diverse augmentations via a multi-round mechanism that promotes coverage and diversity. Extensive experiments on four long-tailed benchmarks show consistent improvements over state-of-the-art methods across varying degrees of class imbalance.Notably, our approach improves top-1 accuracy by 15.6% on CIFAR-100-LT and 11.8% on Tiny-ImageNet-LT under IPC=10 and IF=10.

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NeurIPS Conference 2025 Conference Paper

Enhancing the Outcome Reward-based RL Training of MLLMs with Self-Consistency Sampling

  • Jiahao Wang
  • Weiye Xu
  • Aijun Yang
  • Wengang Zhou
  • Lewei Lu
  • Houqiang Li
  • Xiaohua Wang
  • Jinguo Zhu

Outcome‑reward reinforcement learning (RL) is a common—and increasingly significant—way to refine the step‑by‑step reasoning of multimodal large language models (MLLMs). In the multiple‑choice setting—a dominant format for multimodal reasoning benchmarks—the paradigm faces a significant yet often overlooked obstacle: unfaithful trajectories that guess the correct option after a faulty chain of thought receive the same reward as genuine reasoning, which is a flaw that cannot be ignored. We propose Self‑Consistency Sampling (SCS) to correct this issue. For each question, SCS (i) introduces small visual perturbations and (ii) performs repeated truncation‑and‑resampling of a reference trajectory; agreement among the resulting trajectories yields a differentiable consistency score that down‑weights unreliable traces during policy updates. Plugging SCS into RLOO, GRPO, REINFORCE++ series improves accuracy by up to 7. 7 percentage points on six multimodal benchmarks with negligible extra computation, offering a simple, general remedy for outcome‑reward RL in MLLMs.

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NeurIPS Conference 2025 Conference Paper

Image as a World: Generating Interactive World from Single Image via Panoramic Video Generation

  • Dongnan Gui
  • Xun Guo
  • Wengang Zhou
  • Yan Lu

Generating an interactive visual world from a single image is both challenging and practically valuable, as single-view inputs are easy to acquire and align well with prompt-driven applications such as gaming and virtual reality. This paper introduces a novel unified framework, Image as a World ( IaaW ), which synthesizes high-quality 360-degree videos from a single image that are both controllable and temporally continuable. Our framework consists of three stages: world initialization, which jointly synthesizes spatially complete and temporally dynamic scenes from a single view; world exploration, which supports user-specified viewpoint rotation; and world continuation, which extends the generated scene forward in time with temporal consistency. To support this pipeline, we design a visual world model based on generative diffusion models modulated with spherical 3D positional encoding and multi-view composition to represent geometry and view semantics. Additionally, a vision-language model (IaaW-VLM) is fine-tuned to produce both global and view-specific prompts, improving semantic alignment and controllability. Extensive experiments demonstrate that our method produces panoramic videos with superior visual quality, minimal distortion and seamless continuation in both qualitative and quantitative evaluations. To the best of our knowledge, this is the first work to generate a controllable, consistent, and temporally expandable 360-degree world from a single image.

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NeurIPS Conference 2025 Conference Paper

Mitigating Hallucination in VideoLLMs via Temporal-Aware Activation Engineering

  • JIANFENG CAI
  • Jiale Hong
  • Zongmeng Zhang
  • Wengang Zhou
  • Houqiang Li

Multimodal large language models (MLLMs) have achieved remarkable progress in video understanding. However, hallucination, where the model generates plausible yet incorrect outputs, persists as a significant and under-addressed challenge in the video domain. Among existing solutions, activation engineering has proven successful in mitigating hallucinations in LLMs and ImageLLMs, yet its applicability to VideoLLMs remains largely unexplored. In this work, we are the first to systematically investigate the effectiveness and underlying mechanisms of activation engineering for mitigating hallucinations in VideoLLMs. We initially conduct an investigation of the key factors affecting the performance of activation engineering and find that a model’s sensitivity to hallucination depends on $\textbf{temporal variation}$ rather than task type. Moreover, selecting appropriate internal modules and dataset for activation engineering is critical for reducing hallucination. Guided by these findings, we propose a temporal-aware activation engineering framework for VideoLLMs, which adaptively identifies and manipulates hallucination-sensitive modules based on the temporal variation characteristic, substantially mitigating hallucinations without additional LLM fine-tuning. Experiments across multiple models and benchmarks demonstrate that our method markedly reduces hallucination in VideoLLMs, thereby validating the robustness of our findings.

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AAAI Conference 2025 Conference Paper

Multi-Level Optimal Transport for Universal Cross-Tokenizer Knowledge Distillation on Language Models

  • Xiao Cui
  • Mo Zhu
  • Yulei Qin
  • Liang Xie
  • Wengang Zhou
  • Houqiang Li

Knowledge distillation (KD) has become a prevalent technique for compressing large language models (LLMs). Existing KD methods are constrained by the need for identical tokenizers (i.e., vocabularies) between teacher and student models, limiting their versatility in handling LLMs of different architecture families. In this paper, we introduce the Multi-Level Optimal Transport (MultiLevelOT), a novel approach that advances the optimal transport for universal cross-tokenizer knowledge distillation. Our method aligns the logit distributions of the teacher and the student at both token and sequence levels using diverse cost matrices, eliminating the need for dimensional or token-by-token correspondence. At the token level, MultiLevelOT integrates both global and local information by jointly optimizing all tokens within a sequence to enhance robustness. At the sequence level, we efficiently capture complex distribution structures of logits via the Sinkhorn distance, which approximates the Wasserstein distance for divergence measures. Extensive experiments on tasks such as extractive QA, generative QA, and summarization demonstrate that the MultiLevelOT outperforms state-of-the-art cross-tokenizer KD methods under various settings. Our approach is robust to different student and teacher models across model families, architectures, and parameter sizes.

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NeurIPS Conference 2025 Conference Paper

Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation

  • Xiao Cui
  • Yulei Qin
  • Wengang Zhou
  • Hongsheng Li
  • Houqiang Li

Dataset distillation seeks to synthesize a compact distilled dataset, enabling models trained on it to achieve performance comparable to models trained on the full dataset. Recent methods for large-scale datasets focus on matching global distributional statistics (e. g. , mean and variance), but overlook critical instance-level characteristics and intraclass variations, leading to suboptimal generalization. We address this limitation by reformulating dataset distillation as an Optimal Transport (OT) distance minimization problem, enabling fine-grained alignment at both global and instance levels throughout the pipeline. OT offers a geometrically faithful framework for distribution matching. It effectively preserves local modes, intra-class patterns, and fine-grained variations that characterize the geometry of complex, high-dimensional distributions. Our method comprises three components tailored for preserving distributional geometry: (1) OT-guided diffusion sampling, which aligns latent distributions of real and distilled images; (2) label-image-aligned soft relabeling, which adapts label distributions based on the complexity of distilled image distributions; and (3) OT-based logit matching, which aligns the output of student models with soft-label distributions. Extensive experiments across diverse architectures and large-scale datasets demonstrate that our method consistently outperforms state-of-the-art approaches in an efficient manner, achieving at least 4\% accuracy improvement under IPC=10 settings for each architecture on ImageNet-1K.

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IJCAI Conference 2025 Conference Paper

Self-Classification Enhancement and Correction for Weakly Supervised Object Detection

  • Yufei Yin
  • Lechao Cheng
  • Wengang Zhou
  • Jiajun Deng
  • Zhou Yu
  • Houqiang Li

In recent years, weakly supervised object detection (WSOD) has attracted much attention due to its low labeling cost. The success of recent WSOD models is often ascribed to the two-stage multi-class classification (MCC) task, i. e. , multiple instance learning and online classification refinement. Despite achieving non-trivial progresses, these methods overlook potential classification ambiguities between these two MCC tasks and fail to leverage their unique strengths. In this work, we introduce a novel WSOD framework to ameliorate these two issues. For one thing, we propose a self-classification enhancement module that integrates intra-class binary classification (ICBC) to bridge the gap between the two distinct MCC tasks. The ICBC task enhances the network’s discrimination between positive and mis-located samples in a class-wise manner and forges a mutually reinforcing relationship with the MCC task. For another, we propose a self-classification correction algorithm during inference, which combines the results of both MCC tasks to effectively reduce the mis-classified predictions. Extensive experiments on the prevalent VOC 2007 & 2012 datasets demonstrate the superior performance of our framework.

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AAAI Conference 2024 Conference Paper

Revisiting Open-Set Panoptic Segmentation

  • Yufei Yin
  • Hao Chen
  • Wengang Zhou
  • Jiajun Deng
  • Haiming Xu
  • Houqiang Li

In this paper, we focus on the open-set panoptic segmentation (OPS) task to circumvent the data explosion problem. Different from the close-set setting, OPS targets to detect both known and unknown categories, where the latter is not annotated during training. Different from existing work that only selects a few common categories as unknown ones, we move forward to the real-world scenario by considering the various tail categories (~1k). To this end, we first build a new dataset with long-tail distribution for the OPS task. Based on this dataset, we additionally add a new class type for unknown classes and re-define the training annotations to make the OPS definition more complete and reasonable. Moreover, we analyze the influence of several significant factors in the OPS task and explore the upper bound of performance on unknown classes with different settings. Furthermore, based on the analyses, we design an effective two-phase framework for the OPS task, including thing-agnostic map generation and unknown segment mining. We further adopt semi-supervised learning to improve the OPS performance. Experimental results on different datasets validate the effectiveness of our method.

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AAAI Conference 2024 Conference Paper

SUF: Stabilized Unconstrained Fine-Tuning for Offline-to-Online Reinforcement Learning

  • Jiaheng Feng
  • Mingxiao Feng
  • Haolin Song
  • Wengang Zhou
  • Houqiang Li

Offline-to-online reinforcement learning (RL) provides a promising solution to improving suboptimal offline pre-trained policies through online fine-tuning. However, one efficient method, unconstrained fine-tuning, often suffers from severe policy collapse due to excessive distribution shift. To ensure stability, existing methods retain offline constraints and employ additional techniques during fine-tuning, which hurts efficiency. In this work, we introduce a novel perspective: eliminating the policy collapse without imposing constraints. We observe that such policy collapse arises from the mismatch between unconstrained fine-tuning and the conventional RL training framework. To this end, we propose Stabilized Unconstrained Fine-tuning (SUF), a streamlined framework that benefits from the efficiency of unconstrained fine-tuning while ensuring stability by modifying the Update-To-Data ratio. With just a few lines of code adjustments, SUF demonstrates remarkable adaptability to diverse backbones and superior performance over state-of-the-art baselines.

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NeurIPS Conference 2024 Conference Paper

TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy

  • Weichao Zhao
  • Hao Feng
  • Qi Liu
  • Jingqun Tang
  • Shu Wei
  • Binghong Wu
  • Lei Liao
  • Yongjie Ye

Tables contain factual and quantitative data accompanied by various structures and contents that pose challenges for machine comprehension. Previous methods generally design task-specific architectures and objectives for individual tasks, resulting in modal isolation and intricate workflows. In this paper, we present a novel large vision-language model, TabPedia, equipped with a concept synergy mechanism. In this mechanism, all the involved diverse visual table understanding (VTU) tasks and multi-source visual embeddings are abstracted as concepts. This unified framework allows TabPedia to seamlessly integrate VTU tasks, such as table detection, table structure recognition, table querying, and table question answering, by leveraging the capabilities of large language models (LLMs). Moreover, the concept synergy mechanism enables table perception-related and comprehension-related tasks to work in harmony, as they can effectively leverage the needed clues from the corresponding source perception embeddings. Furthermore, to better evaluate the VTU task in real-world scenarios, we establish a new and comprehensive table VQA benchmark, ComTQA, featuring approximately 9, 000 QA pairs. Extensive quantitative and qualitative experiments on both table perception and comprehension tasks, conducted across various public benchmarks, validate the effectiveness of our TabPedia. The superior performance further confirms the feasibility of using LLMs for understanding visual tables when all concepts work in synergy. The benchmark ComTQA has been open-sourced at https: //huggingface. co/datasets/ByteDance/ComTQA. The source code and model also have been released at https: //github. com/zhaowc-ustc/TabPedia.

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AAAI Conference 2023 Conference Paper

BEST: BERT Pre-training for Sign Language Recognition with Coupling Tokenization

  • Weichao Zhao
  • Hezhen Hu
  • Wengang Zhou
  • Jiaxin Shi
  • Houqiang Li

In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model. Considering the dominance of hand and body in sign language expression, we organize them as pose triplet units and feed them into the Transformer backbone in a frame-wise manner. Pre-training is performed via reconstructing the masked triplet unit from the corrupted input sequence, which learns the hierarchical correlation context cues among internal and external triplet units. Notably, different from the highly semantic word token in BERT, the pose unit is a low-level signal originally locating in continuous space, which prevents the direct adoption of the BERT cross entropy objective. To this end, we bridge this semantic gap via coupling tokenization of the triplet unit. It adaptively extracts the discrete pseudo label from the pose triplet unit, which represents the semantic gesture / body state. After pre-training, we fine-tune the pre-trained encoder on the downstream SLR task, jointly with the newly added task-specific layer. Extensive experiments are conducted to validate the effectiveness of our proposed method, achieving new state-of-the-art performance on all four benchmarks with a notable gain.

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NeurIPS Conference 2023 Conference Paper

CLIP4HOI: Towards Adapting CLIP for Practical Zero-Shot HOI Detection

  • Yunyao Mao
  • Jiajun Deng
  • Wengang Zhou
  • Li Li
  • Yao Fang
  • Houqiang Li

Zero-shot Human-Object Interaction (HOI) detection aims to identify both seen and unseen HOI categories. A strong zero-shot HOI detector is supposed to be not only capable of discriminating novel interactions but also robust to positional distribution discrepancy between seen and unseen categories when locating human-object pairs. However, top-performing zero-shot HOI detectors rely on seen and predefined unseen categories to distill knowledge from CLIP and jointly locate human-object pairs without considering the potential positional distribution discrepancy, leading to impaired transferability. In this paper, we introduce CLIP4HOI, a novel framework for zero-shot HOI detection. CLIP4HOI is developed on the vision-language model CLIP and ameliorates the above issues in the following two aspects. First, to avoid the model from overfitting to the joint positional distribution of seen human-object pairs, we seek to tackle the problem of zero-shot HOI detection in a disentangled two-stage paradigm. To be specific, humans and objects are independently identified and all feasible human-object pairs are processed by Human-Object interactor for pairwise proposal generation. Second, to facilitate better transferability, the CLIP model is elaborately adapted into a fine-grained HOI classifier for proposal discrimination, avoiding data-sensitive knowledge distillation. Finally, experiments on prevalent benchmarks show that our CLIP4HOI outperforms previous approaches on both rare and unseen categories, and sets a series of state-of-the-art records under a variety of zero-shot settings.

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NeurIPS Conference 2023 Conference Paper

DIFFER:Decomposing Individual Reward for Fair Experience Replay in Multi-Agent Reinforcement Learning

  • Xunhan Hu
  • Jian Zhao
  • Wengang Zhou
  • Ruili Feng
  • Houqiang Li

Cooperative multi-agent reinforcement learning (MARL) is a challenging task, as agents must learn complex and diverse individual strategies from a shared team reward. However, existing methods struggle to distinguish and exploit important individual experiences, as they lack an effective way to decompose the team reward into individual rewards. To address this challenge, we propose DIFFER, a powerful theoretical framework for decomposing individual rewards to enable fair experience replay in MARL. By enforcing the invariance of network gradients, we establish a partial differential equation whose solution yields the underlying individual reward function. The individual TD-error can then be computed from the solved closed-form individual rewards, indicating the importance of each piece of experience in the learning task and guiding the training process. Our method elegantly achieves an equivalence to the original learning framework when individual experiences are homogeneous, while also adapting to achieve more muscular efficiency and fairness when diversity is observed. Our extensive experiments on popular benchmarks validate the effectiveness of our theory and method, demonstrating significant improvements in learning efficiency and fairness. Code is available in supplement material.

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NeurIPS Conference 2023 Conference Paper

Hierarchical Multi-Agent Skill Discovery

  • Mingyu Yang
  • Yaodong Yang
  • Zhenbo Lu
  • Wengang Zhou
  • Houqiang Li

Skill discovery has shown significant progress in unsupervised reinforcement learning. This approach enables the discovery of a wide range of skills without any extrinsic reward, which can be effectively combined to tackle complex tasks. However, such unsupervised skill learning has not been well applied to multi-agent reinforcement learning (MARL) due to two primary challenges. One is how to learn skills not only for the individual agents but also for the entire team, and the other is how to coordinate the skills of different agents to accomplish multi-agent tasks. To address these challenges, we present Hierarchical Multi-Agent Skill Discovery (HMASD), a two-level hierarchical algorithm for discovering both team and individual skills in MARL. The high-level policy employs a transformer structure to realize sequential skill assignment, while the low-level policy learns to discover valuable team and individual skills. We evaluate HMASD on sparse reward multi-agent benchmarks, and the results show that HMASD achieves significant performance improvements compared to strong MARL baselines.

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AAAI Conference 2023 Conference Paper

Low-Light Video Enhancement with Synthetic Event Guidance

  • Lin Liu
  • Junfeng An
  • Jianzhuang Liu
  • Shanxin Yuan
  • Xiangyu Chen
  • Wengang Zhou
  • Houqiang Li
  • Yan Feng Wang

Low-light video enhancement (LLVE) is an important yet challenging task with many applications such as photographing and autonomous driving. Unlike single image low-light enhancement, most LLVE methods utilize temporal information from adjacent frames to restore the color and remove the noise of the target frame. However, these algorithms, based on the framework of multi-frame alignment and enhancement, may produce multi-frame fusion artifacts when encountering extreme low light or fast motion. In this paper, inspired by the low latency and high dynamic range of events, we use synthetic events from multiple frames to guide the enhancement and restoration of low-light videos. Our method contains three stages: 1) event synthesis and enhancement, 2) event and image fusion, and 3) low-light enhancement. In this framework, we design two novel modules (event-image fusion transform and event-guided dual branch) for the second and third stages, respectively. Extensive experiments show that our method outperforms existing low-light video or single image enhancement approaches on both synthetic and real LLVE datasets. Our code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/LLVE-SEG.

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IJCAI Conference 2023 Conference Paper

MA2CL: Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning

  • Haolin Song
  • Mingxiao Feng
  • Wengang Zhou
  • Houqiang Li

Recent approaches have utilized self-supervised auxiliary tasks as representation learning to improve the performance and sample efficiency of vision-based reinforcement learning algorithms in single-agent settings. However, in multi-agent reinforcement learning (MARL), these techniques face challenges because each agent only receives partial observation from an environment influenced by others, resulting in correlated observations in the agent dimension. So it is necessary to consider agent-level information in representation learning for MARL. In this paper, we propose an effective framework called Multi-Agent Masked Attentive Contrastive Learning (MA2CL), which encourages learning representation to be both temporal and agent-level predictive by reconstructing the masked agent observation in latent space. Specifically, we use an attention reconstruction model for recovering and the model is trained via contrastive learning. MA2CL allows better utilization of contextual information at the agent level, facilitating the training of MARL agents for cooperation tasks. Extensive experiments demonstrate that our method significantly improves the performance and sample efficiency of different MARL algorithms and outperforms other methods in various vision-based and state-based scenarios.

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NeurIPS Conference 2023 Conference Paper

Multi-Agent First Order Constrained Optimization in Policy Space

  • Youpeng Zhao
  • Yaodong Yang
  • Zhenbo Lu
  • Wengang Zhou
  • Houqiang Li

In the realm of multi-agent reinforcement learning (MARL), achieving high performance is crucial for a successful multi-agent system. Meanwhile, the ability to avoid unsafe actions is becoming an urgent and imperative problem to solve for real-life applications. Whereas, it is still challenging to develop a safety-aware method for multi-agent systems in MARL. In this work, we introduce a novel approach called Multi-Agent First Order Constrained Optimization in Policy Space (MAFOCOPS), which effectively addresses the dual objectives of attaining satisfactory performance and enforcing safety constraints. Using data generated from the current policy, MAFOCOPS first finds the optimal update policy by solving a constrained optimization problem in the nonparameterized policy space. Then, the update policy is projected back into the parametric policy space to achieve a feasible policy. Notably, our method is first-order in nature, ensuring the ease of implementation, and exhibits an approximate upper bound on the worst-case constraint violation. Empirical results show that our approach achieves remarkable performance while satisfying safe constraints on several safe MARL benchmarks.

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AAMAS Conference 2023 Conference Paper

Multi-Agent Reinforcement Learning with Safety Layer for Active Voltage Control

  • Yufeng Shi
  • Mingxiao Feng
  • Minrui Wang
  • Wengang Zhou
  • Houqiang Li

The main goal of active voltage control is to keep the voltage of each bus in the grid within a safe range. With the increasing penetration of renewable and distributed energy sources in the grid, growing complexity, increasing uncertainty, and aggravating volatility bring great challenges to voltage control in modern power systems. Traditional algorithms can hardly guarantee real-time safe control to cope with these challenges. In recent years, substantial attention has been paid to the application of multi-agent reinforcement learning algorithms (MARL) to coordinate the control units in each area of the grid in real time for active voltage control in complex scenarios. However, these MARL algorithms do not explicitly guarantee that the power system satisfies the security constraints. There is a little in-depth study on safe multi-agent policy learning in multi-agent-based voltage control, especially the direct correction of unsafe actions. In this paper, we formalize the active voltage control problem as a Constrained Markov Game and approach it with a centralized data-driven safety layer that requires global observations and maps unsafe actions to safe actions. In order to make the policy network rely on local observations for decentralized execution, we introduce two novel components into the policy network: action correction penalty loss and action correction subnetworks. Notably, our approaches are easily extendable to other MARL algorithms for continuous actions. In the experiments, we evaluate our methods in the power distribution network simulation environment and demonstrate the capability of the safety layer to correct unsafe actions and the effectiveness of the safety layer to improve the performance of the policy itself.

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NeurIPS Conference 2023 Conference Paper

State Sequences Prediction via Fourier Transform for Representation Learning

  • Mingxuan Ye
  • Yufei Kuang
  • Jie Wang
  • Yang Rui
  • Wengang Zhou
  • Houqiang Li
  • Feng Wu

While deep reinforcement learning (RL) has been demonstrated effective in solving complex control tasks, sample efficiency remains a key challenge due to the large amounts of data required for remarkable performance. Existing research explores the application of representation learning for data-efficient RL, e. g. , learning predictive representations by predicting long-term future states. However, many existing methods do not fully exploit the structural information inherent in sequential state signals, which can potentially improve the quality of long-term decision-making but is difficult to discern in the time domain. To tackle this problem, we propose State Sequences Prediction via Fourier Transform (SPF), a novel method that exploits the frequency domain of state sequences to extract the underlying patterns in time series data for learning expressive representations efficiently. Specifically, we theoretically analyze the existence of structural information in state sequences, which is closely related to policy performance and signal regularity, and then propose to predict the Fourier transform of infinite-step future state sequences to extract such information. One of the appealing features of SPF is that it is simple to implement while not requiring storage of infinite-step future states as prediction targets. Experiments demonstrate that the proposed method outperforms several state-of-the-art algorithms in terms of both sample efficiency and performance.

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TMLR Journal 2022 Journal Article

Direct Molecular Conformation Generation

  • Jinhua Zhu
  • Yingce Xia
  • Chang Liu
  • Lijun Wu
  • Shufang Xie
  • Yusong Wang
  • Tong Wang
  • Tao Qin

Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previous methods usually first predict the interatomic distances, the gradients of interatomic distances or the local structures (e.g., torsion angles) of a molecule, and then reconstruct its 3D conformation. How to directly generate the conformation without the above intermediate values is not fully explored. In this work, we propose a method that directly predicts the coordinates of atoms: (1) the loss function is invariant to roto-translation of coordinates and permutation of symmetric atoms; (2) the newly proposed model adaptively aggregates the bond and atom information and iteratively refines the coordinates of the generated conformation. Our method achieves the best results on GEOM-QM9 and GEOM-Drugs datasets. Further analysis shows that our generated conformations have closer properties (e.g., HOMO-LUMO gap) with the groundtruth conformations. In addition, our method improves molecular docking by providing better initial conformations. All the results demonstrate the effectiveness of our method and the great potential of the direct approach. The code is released at \url{https://github.com/DirectMolecularConfGen/DMCG}.

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NeurIPS Conference 2022 Conference Paper

Hand-Object Interaction Image Generation

  • Hezhen Hu
  • Weilun Wang
  • Wengang Zhou
  • Houqiang Li

In this work, we are dedicated to a new task, i. e. , hand-object interaction image generation, which aims to conditionally generate the hand-object image under the given hand, object and their interaction status. This task is challenging and research-worthy in many potential application scenarios, such as AR/VR games and online shopping, etc. To address this problem, we propose a novel HOGAN framework, which utilizes the expressive model-aware hand-object representation and leverages its inherent topology to build the unified surface space. In this space, we explicitly consider the complex self- and mutual occlusion during interaction. During final image synthesis, we consider different characteristics of hand and object and generate the target image in a split-and-combine manner. For evaluation, we build a comprehensive protocol to access both the fidelity and structure preservation of the generated image. Extensive experiments on two large-scale datasets, i. e. , HO3Dv3 and DexYCB, demonstrate the effectiveness and superiority of our framework both quantitatively and qualitatively. The code will be available at https: //github. com/play-with-HOI-generation/HOIG.

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NeurIPS Conference 2022 Conference Paper

LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning

  • Mingyu Yang
  • Jian Zhao
  • Xunhan Hu
  • Wengang Zhou
  • Jiangcheng Zhu
  • Houqiang Li

Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in recent years. For training efficiency and scalability, most of the MARL algorithms make all agents share the same policy or value network. However, in many complex multi-agent tasks, different agents are expected to possess specific abilities to handle different subtasks. In those scenarios, sharing parameters indiscriminately may lead to similar behavior across all agents, which will limit the exploration efficiency and degrade the final performance. To balance the training complexity and the diversity of agent behavior, we propose a novel framework to learn dynamic subtask assignment (LDSA) in cooperative MARL. Specifically, we first introduce a subtask encoder to construct a vector representation for each subtask according to its identity. To reasonably assign agents to different subtasks, we propose an ability-based subtask selection strategy, which can dynamically group agents with similar abilities into the same subtask. In this way, agents dealing with the same subtask share their learning of specific abilities and different subtasks correspond to different specific abilities. We further introduce two regularizers to increase the representation difference between subtasks and stabilize the training by discouraging agents from frequently changing subtasks, respectively. Empirical results show that LDSA learns reasonable and effective subtask assignment for better collaboration and significantly improves the learning performance on the challenging StarCraft II micromanagement benchmark and Google Research Football.

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AAAI Conference 2022 Conference Paper

Learning Token-Based Representation for Image Retrieval

  • Hui Wu
  • Min Wang
  • Wengang Zhou
  • Yang Hu
  • Houqiang Li

In image retrieval, deep local features learned in a data-driven manner have been demonstrated effective to improve retrieval performance. To realize efficient retrieval on large image database, some approaches quantize deep local features with a large codebook and match images with aggregated match kernel. However, the complexity of these approaches is nontrivial with large memory footprint, which limits their capability to jointly perform feature learning and aggregation. To generate compact global representations while maintaining regional matching capability, we propose a unified framework to jointly learn local feature representation and aggregation. In our framework, we first extract deep local features using CNNs. Then, we design a tokenizer module to aggregate them into a few visual tokens, each corresponding to a specific visual pattern. This helps to remove background noise, and capture more discriminative regions in the image. Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention. Finally, different visual tokens are concatenated to generate a compact global representation. The whole framework is trained end-to-end with image-level labels. Extensive experiments are conducted to evaluate our approach, which outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets.

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NeurIPS Conference 2021 Conference Paper

Contextual Similarity Aggregation with Self-attention for Visual Re-ranking

  • Jianbo Ouyang
  • Hui Wu
  • Min Wang
  • Wengang Zhou
  • Houqiang Li

In content-based image retrieval, the first-round retrieval result by simple visual feature comparison may be unsatisfactory, which can be refined by visual re-ranking techniques. In image retrieval, it is observed that the contextual similarity among the top-ranked images is an important clue to distinguish the semantic relevance. Inspired by this observation, in this paper, we propose a visual re-ranking method by contextual similarity aggregation with self-attention. In our approach, for each image in the top-K ranking list, we represent it into an affinity feature vector by comparing it with a set of anchor images. Then, the affinity features of the top-K images are refined by aggregating the contextual information with a transformer encoder. Finally, the affinity features are used to recalculate the similarity scores between the query and the top-K images for re-ranking of the latter. To further improve the robustness of our re-ranking model and enhance the performance of our method, a new data augmentation scheme is designed. Since our re-ranking model is not directly involved with the visual feature used in the initial retrieval, it is ready to be applied to retrieval result lists obtained from various retrieval algorithms. We conduct comprehensive experiments on four benchmark datasets to demonstrate the generality and effectiveness of our proposed visual re-ranking method.

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AAAI Conference 2021 Conference Paper

Contrastive Transformation for Self-supervised Correspondence Learning

  • Ning Wang
  • Wengang Zhou
  • Houqiang Li

In this paper, we focus on the self-supervised learning of visual correspondence using unlabeled videos in the wild. Our method simultaneously considers intra- and inter-video representation associations for reliable correspondence estimation. The intra-video learning transforms the image contents across frames within a single video via the frame pair-wise affinity. To obtain the discriminative representation for instance-level separation, we go beyond the intra-video analysis and construct the inter-video affinity to facilitate the contrastive transformation across different videos. By forcing the transformation consistency between intra- and inter-video levels, the fine-grained correspondence associations are well preserved and the instance-level feature discrimination is effectively reinforced. Our simple framework outperforms the recent selfsupervised correspondence methods on a range of visual tasks including video object tracking (VOT), video object segmentation (VOS), pose keypoint tracking, etc. It is worth mentioning that our method also surpasses the fully-supervised affinity representation (e. g. , ResNet) and performs competitively against the recent fully-supervised algorithms designed for the specific tasks (e. g. , VOT and VOS).

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AAAI Conference 2021 Conference Paper

Hand-Model-Aware Sign Language Recognition

  • Hezhen Hu
  • Wengang Zhou
  • Houqiang Li

Hand gestures play a dominant role in the expression of sign language. Current deep-learning based video sign language recognition (SLR) methods usually follow a datadriven paradigm under the supervision of the category label. However, those methods suffer limited interpretability and may encounter the overfitting issue due to limited sign data sources. In this paper, we introduce the hand prior and propose a new hand-model-aware framework for isolated SLR with the modeling hand as the intermediate representation. We first transform the cropped hand sequence into the latent semantic feature. Then the hand model introduces the hand prior and provides a mapping from the semantic feature to the compact hand pose representation. Finally, the inference module enhances the spatio-temporal pose representation and performs the final recognition. Due to the lack of annotation on the hand pose under current sign language datasets, we further guide its learning by utilizing multiple weaklysupervised losses to constrain its spatial and temporal consistency. To validate the effectiveness of our method, we perform extensive experiments on four benchmark datasets, including NMFs-CSL, SLR500, MSASL and WLASL. Experimental results demonstrate that our method achieves stateof-the-art performance on all four popular benchmarks with a notable margin.

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AAAI Conference 2021 Conference Paper

Instance Mining with Class Feature Banks for Weakly Supervised Object Detection

  • Yufei Yin
  • Jiajun Deng
  • Wengang Zhou
  • Houqiang Li

Recent progress on weakly supervised object detection (WSOD) is characterized by formulating WSOD as a Multiple Instance Learning (MIL) problem and taking online refinement with the selected region proposals from MIL. However, MIL inclines to select the most discriminative part rather than the entire instance as the top-scoring region proposals, which leads to weak localization capability for weakly supervised object detectors. We attribute this problem to the limited intra-class diversity within a single image. Specifically, due to the lack of annotated bounding boxes, the network tends to focus on the most common parts of each class and neglect the diverse parts of objects. To solve the problem, we introduce a novel Instance Mining with Class Feature Banks (IM-CFB) framework, which includes a Class Feature Banks (CFB) module and a Feature Guided Instance Mining (FGIM) algorithm. Concretely, Class Feature Banks (CFB) consist of sub-banks for each class, which are utilized to collect diversity information from a broader view. At the training stage, the RoI features of reliable region proposals are recorded and updated in the CFB. Then, FGIM leverages the features recorded in the CFB to ameliorate the region proposal selection of the MIL branch. Extensive experiments conducted on two publicly available datasets, Pascal VOC 2007 and 2012, demonstrate the effectiveness of our method. More remarkably, our method achieves 54. 3% on mAP and 70. 7% on Cor- Loc on Pascal VOC 2007. When further re-trained by a Fast- RCNN detector, we obtain to-date the best reported mAP and CorLoc of 55. 8% and 72. 2%, respectively.

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AAAI Conference 2021 Conference Paper

Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection

  • Jiajun Deng
  • Shaoshuai Shi
  • Peiwei Li
  • Wengang Zhou
  • Yanyong Zhang
  • Houqiang Li

Recent advances on 3D object detection heavily rely on how the 3D data are represented, i. e. , voxel-based or point-based representation. Many existing high performance 3D detectors are point-based because this structure can better retain precise point positions. Nevertheless, point-level features lead to high computation overheads due to unordered storage. In contrast, the voxel-based structure is better suited for feature extraction but often yields lower accuracy because the input data are divided into grids. In this paper, we take a slightly different viewpoint — we find that precise positioning of raw points is not essential for high performance 3D object detection and that the coarse voxel granularity can also offer sufficient detection accuracy. Bearing this view in mind, we devise a simple but effective voxel-based framework, named Voxel R-CNN. By taking full advantage of voxel features in a two stage approach, our method achieves comparable detection accuracy with state-of-the-art point-based models, but at a fraction of the computation cost. Voxel R-CNN consists of a 3D backbone network, a 2D bird-eye-view (BEV) Region Proposal Network and a detect head. A voxel RoI pooling is devised to extract RoI features directly from voxel features for further refinement. Extensive experiments are conducted on the widely used KITTI Dataset and the more recent Waymo Open Dataset. Our results show that compared to existing voxel-based methods, Voxel R-CNN delivers a higher detection accuracy while maintaining a realtime frame processing rate, i. e. , at a speed of 25 FPS on an NVIDIA RTX 2080 Ti GPU. The code is available at https: //github. com/djiajunustc/Voxel-R-CNN.

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AAAI Conference 2020 Conference Paper

Attentive Experience Replay

  • Peiquan Sun
  • Wengang Zhou
  • Houqiang Li

Experience replay (ER) has become an important component of deep reinforcement learning (RL) algorithms. ER enables RL algorithms to reuse past experiences for the update of current policy. By reusing a previous state for training, the RL agent would learn more accurate value estimation and better decision on that state. However, as the policy is continually updated, some states in past experiences become rarely visited, and optimization over these states might not improve the overall performance of current policy. To tackle this issue, we propose a new replay strategy to prioritize the transitions that contain states frequently visited by current policy. We introduce Attentive Experience Replay (AER), a novel experience replay algorithm that samples transitions according to the similarities between their states and the agent’s state. We couple AER with different off-policy algorithms and demonstrate that AER makes consistent improvements on the suite of OpenAI gym tasks.

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AAAI Conference 2020 Conference Paper

POST: POlicy-Based Switch Tracking

  • Ning Wang
  • Wengang Zhou
  • Guojun Qi
  • Houqiang Li

In visual object tracking, by reasonably fusing multiple experts, ensemble framework typically achieves superior performance compared to the individual experts. However, the necessity of parallelly running all the experts in most existing ensemble frameworks heavily limits their efficiency. In this paper, we propose POST, a POlicy-based Switch Tracker for robust and efficient visual tracking. The proposed POST tracker consists of multiple weak but complementary experts (trackers) and adaptively assigns one suitable expert for tracking in each frame. By formulating this expert switch in consecutive frames as a decision-making problem, we learn an agent via reinforcement learning to directly decide which expert to handle the current frame without running others. In this way, the proposed POST tracker maintains the performance merit of multiple diverse models while favorably ensuring the tracking efficiency. Extensive ablation studies and experimental comparisons against state-of-the-art trackers on 5 prevalent benchmarks verify the effectiveness of the proposed method.

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AAAI Conference 2020 Conference Paper

Relation-Guided Spatial Attention and Temporal Refinement for Video-Based Person Re-Identification

  • Xingze Li
  • Wengang Zhou
  • Yun Zhou
  • Houqiang Li

Video-based person re-identification has received considerable attention in recent years due to its significant application in video surveillance. Compared with image-based person reidentification, video-based person re-identification is characterized by a much richer context, which raises the significance of identifying informative regions and fusing the temporal information across frames. In this paper, we propose two relation-guided modules to learn reinforced feature representations for effective re-identification. First, a relation-guided spatial attention (RGSA) module is designed to explore the discriminative regions globally. The weight at each position is determined by its feature as well as the relation features from other positions, revealing the dependence between local and global contents. Based on the adaptively weighted frame-level feature, then, a relation-guided temporal refinement (RGTR) module is proposed to further refine the feature representations across frames. The learned relation information via the RGTR module enables the individual frames to complement each other in an aggregation manner, leading to robust video-level feature representations. Extensive experiments on four prevalent benchmarks verify the state-of-theart performance of the proposed method.

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AAAI Conference 2020 Conference Paper

Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition

  • Hao Zhou
  • Wengang Zhou
  • Yun Zhou
  • Houqiang Li

Despite the recent success of deep learning in continuous sign language recognition (CSLR), deep models typically focus on the most discriminative features, ignoring other potentially non-trivial and informative contents. Such characteristic heavily constrains their capability to learn implicit visual grammars behind the collaboration of different visual cues (i, e. , hand shape, facial expression and body posture). By injecting multi-cue learning into neural network design, we propose a spatial-temporal multi-cue (STMC) network to solve the vision-based sequence learning problem. Our STMC network consists of a spatial multi-cue (SMC) module and a temporal multi-cue (TMC) module. The SMC module is dedicated to spatial representation and explicitly decomposes visual features of different cues with the aid of a self-contained pose estimation branch. The TMC module models temporal correlations along two parallel paths, i. e. , intra-cue and intercue, which aims to preserve the uniqueness and explore the collaboration of multiple cues. Finally, we design a joint optimization strategy to achieve the end-to-end sequence learning of the STMC network. To validate the effectiveness, we perform experiments on three large-scale CSLR benchmarks: PHOENIX-2014, CSL and PHOENIX-2014-T. Experimental results demonstrate that the proposed method achieves new state-of-the-art performance on all three benchmarks.

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AAAI Conference 2019 Conference Paper

Re 2 EMA: Regularized and Reinitialized Exponential Moving Average for Target Model Update in Object Tracking

  • Jianglei Huang
  • Wengang Zhou

Target model update plays an important role in visual object tracking. However, performing optimal model update is challenging. In this work, we propose to achieve an optimal target model by learning a transformation matrix from the last target model to the newly generated one, which results into a minimization objective. In this objective, there exists two challenges. The first is that the newly generated target model is unreliable. To overcome this problem, we propose to impose a penalty to limit the distance between the learned target model and the last one. The second is that as time evolves, we can not decide whether the last target model has been corrupted or not. To get out of this dilemma, we propose a reinitialization term. Besides, to control the complexity of the transformation matrix, we also add a regularizer. We find that the optimization formula’s solution, with some simplifications, degenerates to EMA. Finally, despite the simplicity, extensive experiments conducted on several commonly used benchmarks demonstrate the effectiveness of our proposed approach in relatively long term scenarios.

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AAAI Conference 2019 Conference Paper

Spatial and Temporal Mutual Promotion for Video-Based Person Re-Identification

  • Yiheng Liu
  • Zhenxun Yuan
  • Wengang Zhou
  • Houqiang Li

Video-based person re-identification is a crucial task of matching video sequences of a person across multiple camera views. Generally, features directly extracted from a single frame suffer from occlusion, blur, illumination and posture changes. This leads to false activation or missing activation in some regions, which corrupts the appearance and motion representation. How to explore the abundant spatial-temporal information in video sequences is the key to solve this problem. To this end, we propose a Refining Recurrent Unit (RRU) that recovers the missing parts and suppresses noisy parts of the current frame’s features by referring historical frames. With RRU, the quality of each frame’s appearance representation is improved. Then we use the Spatial-Temporal clues Integration Module (STIM) to mine the spatial-temporal information from those upgraded features. Meanwhile, the multilevel training objective is used to enhance the capability of RRU and STIM. Through the cooperation of those modules, the spatial and temporal features mutually promote each other and the final spatial-temporal feature representation is more discriminative and robust. Extensive experiments are conducted on three challenging datasets, i. e. , iLIDS-VID, PRID- 2011 and MARS. The experimental results demonstrate that our approach outperforms existing state-of-the-art methods of video-based person re-identification on iLIDS-VID and MARS and achieves favorable results on PRID-2011.

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IJCAI Conference 2018 Conference Paper

Dilated Convolutional Network with Iterative Optimization for Continuous Sign Language Recognition

  • Junfu Pu
  • Wengang Zhou
  • Houqiang Li

This paper presents a novel deep neural architecture with iterative optimization strategy for real-world continuous sign language recognition. Generally, a continuous sign language recognition system consists of visual input encoder for feature extraction and a sequence learning model to learn the correspondence between the input sequence and the output sentence-level labels. We use a 3D residual convolutional network (3D-ResNet) to extract visual features. After that, a stacked dilated convolutional network with Connectionist Temporal Classification (CTC) is applied for learning the mapping between the sequential features and the text sentence. The deep network is hard to train since the CTC loss has limited contribution to early CNN parameters. To alleviate this problem, we design an iterative optimization strategy to train our architecture. We generate pseudo-labels for video clips from sequence learning model with CTC, and fine-tune the 3D-ResNet with the supervision of pseudo-labels for a better feature representation. We alternately optimize feature extractor and sequence learning model with iterative steps. Experimental results on RWTH-PHOENIX-Weather, a large real-world continuous sign language recognition benchmark, demonstrate the advantages and effectiveness of our proposed method.

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AAAI Conference 2018 Conference Paper

Hierarchical LSTM for Sign Language Translation

  • Dan Guo
  • Wengang Zhou
  • Houqiang Li
  • Meng Wang

Continuous Sign Language Translation (SLT) is a challenging task due to its specific linguistics under sequential gesture variation without word alignment. Current hybrid HMM and CTC (Connectionist temporal classification) based models are proposed to solve frame or word level alignment. They may fail to tackle the cases with messing word order corresponding to visual content in sentences. To solve the issue, this paper proposes a hierarchical-LSTM (HLSTM) encoderdecoder model with visual content and word embedding for SLT. It tackles different granularities by conveying spatiotemporal transitions among frames, clips and viseme units. It firstly explores spatio-temporal cues of video clips by 3D CNN and packs appropriate visemes by online key clip mining with adaptive variable-length. After pooling on recurrent outputs of the top layer of HLSTM, a temporal attentionaware weighting mechanism is proposed to balance the intrinsic relationship among viseme source positions. At last, another two LSTM layers are used to separately recurse viseme vectors and translate semantic. After preserving original visual content by 3D CNN and the top layer of HLSTM, it shortens the encoding time step of the bottom two LSTM layers with less computational complexity while attaining more nonlinearity. Our proposed model exhibits promising performance on singer-independent test with seen sentences and also outperforms the comparison algorithms on unseen sentences.

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IJCAI Conference 2018 Conference Paper

Improving Deep Neural Network Sparsity through Decorrelation Regularization

  • Xiaotian Zhu
  • Wengang Zhou
  • Houqiang Li

Modern deep learning models usually suffer high complexity in model size and computation when transplanted to resource constrained platforms. To this end, many works are dedicated to compressing deep neural networks. Adding group LASSO regularization is one of the most effective model compression methods since it generates structured sparse networks. We investigate the deep neural networks trained by group LASSO constraint and observe that even with strong sparsity regularization imposed, there still exists substantial filter correlation among the convolution filters, which is undesired for a compact neural network. We propose to suppress such correlation with a new kind of constraint called decorrelation regularization, which explicitly forces the network to learn a set of less correlated filters. The experiments on CIFAR10/100 and ILSVRC2012 datasets show that when combined our decorrelation regularization with group LASSO, the correlation between filters could be effectively weakened, which increases the sparsity of the resulting model and leads to better compressing performance.

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AAAI Conference 2018 Conference Paper

Video-Based Sign Language Recognition Without Temporal Segmentation

  • Jie Huang
  • Wengang Zhou
  • Qilin Zhang
  • Houqiang Li
  • Weiping Li

Millions of hearing impaired people around the world routinely use some variants of sign languages to communicate, thus the automatic translation of a sign language is meaningful and important. Currently, there are two sub-problems in Sign Language Recognition (SLR), i. e. , isolated SLR that recognizes word by word and continuous SLR that translates entire sentences. Existing continuous SLR methods typically utilize isolated SLRs as building blocks, with an extra layer of preprocessing (temporal segmentation) and another layer of post-processing (sentence synthesis). Unfortunately, temporal segmentation itself is non-trivial and inevitably propagates errors into subsequent steps. Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data. To address these challenges, we propose a novel continuous sign recognition framework, the Hierarchical Attention Network with Latent Space (LS-HAN), which eliminates the preprocessing of temporal segmentation. The proposed LS-HAN consists of three components: a two-stream Convolutional Neural Network (CNN) for video feature representation generation, a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention Network (HAN) for latent space based recognition. Experiments are carried out on two large scale datasets. Experimental results demonstrate the effectiveness of the proposed framework.

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