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Proceedings of the 41st International Conference on Machine Learning

作者： Yufei Kuang Jie Wang Yuyan Zhou Xijun Li Fangzhou Zhu Jianye Hao Feng Wu CAS Key Laboratory of Technology in GIPAS and MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China CAS Key Laboratory of Technology in GIPAS and MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China and Noah's Ark Lab Huawei Technologies Noah's Ark Lab Huawei Technologies Noah's Ark Lab Huawei Technologies and College of Intelligence and Computing Tianjin University

Machine learning (ML) approaches have been successfully applied to accelerating exact combinatorial optimization (CO) solvers. However, many of them fail to explain what patterns they have learned that accelerate the CO algorithms due to the black-box nature of ML models like neural networks, and thus they prevent researchers from further understanding the tasks they are interested in. To tackle this problem, we propose the first graph-based algorithm discovery framework--namely, graph symbolic discovery for exact combinatorial optimization solver (GS4CO)--that learns interpretable branching policies directly from the general bipartite graph representation of CO problems. Specifically, we mainly focus on the variable selection part of the branching policy. We design a unified representation for symbolic variable selection policies with graph inputs, and then we employ a Transformer with multiple treestructural encodings to generate symbolic trees end-to-end, which effectively reduces the cumulative error from iteratively distilling graph neural networks. Experiments show that GS4CO learned interpretable and lightweight policies outperform all the baselines on CPU machines, including both the human-designed and the learning-based. GS4CO shows an encouraging step towards general algorithm discovery on modern CO solvers. Codes are available at https://***/MIRALab-USTC/L2O-GS4CO.

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Asymmetric Feature Fusion for Image Retrieval

Asymmetric Feature Fusion for Image Retrieval

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hui Wu Min Wang Wengang Zhou Zhenbo Lu Houqiang Li CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China Hefei Comprehensive National Science Center Institute of Artificial Intelligence

In asymmetric retrieval systems, models with different capacities are deployed on platforms with different computational and storage resources. Despite the great progress, existing approaches still suffer from a dilemma between retrieval efficiency and asymmetric accuracy due to the limited capacity of the lightweight query model. In this work, we propose an Asymmetric Feature Fusion (AFF) paradigm, which advances existing asymmetric retrieval systems by considering the complementarity among different features just at the gallery side. Specifically, it first embeds each gallery image into various features, e.g., local features and global features. Then, a dynamic mixer is introduced to aggregate these features into compact embedding for efficient search. On the query side, only a single lightweight model is deployed for feature extraction. The query model and dynamic mixer are jointly trained by sharing a momentum-updated classifier. Notably, the proposed paradigm boosts the accuracy of asymmetric retrieval without introducing any extra overhead to the query side. Exhaustive experiments on various landmark retrieval datasets demonstrate the superiority of our paradigm.

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Learning Generalizable Human Motion Generator with Reinforcement Learning

arXiv

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arXiv 2024年

作者： Mao, Yunyao Liu, Xiaoyang Zhou, Wengang Lu, Zhenbo Li, Houqiang CAS Key Laboratory of Technology in GIPAS EEIS Department University of Science and Technology of China China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

Text-driven human motion generation, as one of the vital tasks in computer-aided content creation, has recently attracted increasing attention. While pioneering research has largely focused on improving numerical performance metrics on given datasets, practical applications reveal a common challenge: existing methods often overfit specific motion expressions in the training data, hindering their ability to generalize to novel descriptions like unseen combinations of motions. This limitation restricts their broader applicability. We argue that the aforementioned problem primarily arises from the scarcity of available motion-text pairs, given the many-to-many nature of text-driven motion generation. To tackle this problem, we formulate text-to-motion generation as a Markov decision process and present InstructMotion, which incorporate the trail and error paradigm in reinforcement learning for generalizable human motion generation. Leveraging contrastive pre-trained text and motion encoders, we delve into optimizing reward design to enable InstructMotion to operate effectively on both paired data, enhancing global semantic level text-motion alignment, and synthetic text-only data, facilitating better generalization to novel prompts without the need for ground-truth motion supervision. Extensive experiments on prevalent benchmarks and also our synthesized unpaired dataset demonstrate that the proposed InstructMotion achieves outstanding performance both quantitatively and qualitatively. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction

arXiv

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arXiv 2024年

作者： Guo, Zhiyang Zhou, Wengang Li, Li Wang, Min Li, Houqiang CAS Key Laboratory of Technology in GIPAS EEIS Department University of Science and Technology of China China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene reconstruction. However, existing methods focus mainly on extending static 3DGS into a time-variant representation, while overlooking the rich motion information carried by 2D observations, thus suffering from performance degradation and model redundancy. To address the above problem, we propose a novel motion-aware enhancement framework for dynamic scene reconstruction, which mines useful motion cues from optical flow to improve different paradigms of dynamic 3DGS. Specifically, we first establish a correspondence between 3D Gaussian movements and pixel-level flow. Then a novel flow augmentation method is introduced with additional insights into uncertainty and loss collaboration. Moreover, for the prevalent deformation-based paradigm that presents a harder optimization problem, a transient-aware deformation auxiliary module is proposed. We conduct extensive experiments on both multi-view and monocular scenes to verify the merits of our work. Compared with the baselines, our method shows significant superiority in both rendering quality and efficiency. © 2024, CC BY-NC-ND.

关键词： Image reconstruction

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MD: 3D Action Representation Learning with Inter- and Intra-Modal Mutual Distillation

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International Journal of Computer Vision 2025年 1-18页

作者： Mao, Yunyao Deng, Jiajun Zhou, Wengang Lu, Zhenbo Ouyang, Wanli Li, Houqiang CAS Key Laboratory of Technology in GIPAS Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China The University of Adelaide Adelaide Australia Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Shanghai Artificial Intelligent Laboratory Shanghai China

Recent progresses on self-supervised 3D human action representation learning are largely attributed to contrastive learning. However, in conventional contrastive frameworks, the rich complementarity between different skeleton modalities remains under-explored. Moreover, optimized with distinguishing self-augmented samples, models struggle with numerous similar positive instances in the case of limited action categories. In this work, we tackle the aforementioned problems by introducing a general Inter- and intra-modal mutual distillation ( $$\hbox {I}^2$$ MD) framework. In $$\hbox {I}^2$$ MD, we first re-formulate the cross-modal interaction as a cross-modal mutual distillation (CMD) process. Different from existing distillation solutions that transfer the knowledge of a pre-trained and fixed teacher to the student, in CMD, the knowledge is continuously updated and bidirectionally distilled between modalities during pre-training. To alleviate the interference of similar samples and exploit their underlying contexts, we further design the intra-modal mutual distillation (IMD) strategy, In IMD, the dynamic neighbors aggregation (DNA) mechanism is first introduced, where an additional cluster-level discrimination branch is instantiated in each modality. It adaptively aggregates highly-correlated neighboring features, forming local cluster-level contrasting. Mutual distillation is then performed between the two branches for cross-level knowledge exchange. Extensive experiments on three datasets show that our approach sets a series of new records.

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Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions

arXiv

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arXiv 2023年

作者： Liu, Qiyuan Zhou, Qi Yang, Rui Wang, Jie CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

Recent work has shown that representation learning plays a critical role in sample-efficient reinforcement learning (RL) from pixels. Unfortunately, in real-world scenarios, representation learning is usually fragile to task-irrelevant distractions such as variations in background or viewpoint. To tackle this problem, we propose a novel clustering-based approach, namely Clustering with Bisimulation Metrics (CBM), which learns robust representations by grouping visual observations in the latent space. Specifically, CBM alternates between two steps: (1) grouping observations by measuring their bisimulation distances to the learned prototypes;(2) learning a set of prototypes according to the current cluster assignments. Computing cluster assignments with bisimulation metrics enables CBM to capture task-relevant information, as bisimulation metrics quantify the behavioral similarity between observations. Moreover, CBM encourages the consistency of representations within each group, which facilitates filtering out task-irrelevant information and thus induces robust representations against distractions. An appealing feature is that CBM can achieve sample-efficient representation learning even if multiple distractions exist simultaneously. Experiments demonstrate that CBM significantly improves the sample efficiency of popular visual RL algorithms and achieves state-of-the-art performance on both multiple and single distraction settings. The code is available at https://***/MIRALab-USTC/RL-CBM. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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HandNeRF: Neural Radiance Fields for Animatable Interacting Hands

HandNeRF: Neural Radiance Fields for Animatable Interacting ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhiyang Guo Wengang Zhou Min Wang Li Li Houqiang Li EEIS Department CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center

We propose a novel framework to reconstruct accurate appearance and geometry with neural radiance fields (NeRF) for interacting hands, enabling the rendering of photo-realistic images and videos for gesture animation from arbitrary views. Given multi-view images of a single hand or interacting hands, an off-the-shelf skeleton estimator is first employed to parameterize the hand poses. Then we design a pose-driven deformation field to establish correspondence from those different poses to a shared canonical space, where a pose-disentangled NeRF for one hand is optimized. Such unified modeling efficiently complements the geometry and texture cues in rarely-observed areas for both hands. Meanwhile, we further leverage the pose priors to generate pseudo depth maps as guidance for occlusion aware density learning. Moreover, a neural feature distillation method is proposed to achieve cross-domain alignment for color optimization. We conduct extensive experiments to verify the merits of our proposed HandNeRF and report a series of state-of-the-art results both qualitatively and quantitatively on the large-scale InterHand2.6M dataset.

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State Sequences Prediction via Fourier Transform for Representation Learning

arXiv

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arXiv 2023年

作者： Ye, Mingxuan Kuang, Yufei Wang, Jie Yang, Rui Zhou, Wengang Li, Houqiang Wu, Feng CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

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.2 Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Sign Language Translation with Iterative Prototype

Sign Language Translation with Iterative Prototype

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International Conference on Computer Vision (ICCV)

作者： Huijie Yao Wengang Zhou Hao Feng Hezhen Hu Hao Zhou Houqiang Li EEIS Department CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center

This paper presents IP-SLT, a simple yet effective framework for sign language translation (SLT). Our IP-SLT adopts a recurrent structure and enhances the semantic representation (prototype) of the input sign language video via an iterative refinement manner. Our idea mimics the behavior of human reading, where a sentence can be digested repeatedly, till reaching accurate understanding. Technically, IP-SLT consists of feature extraction, prototype initialization, and iterative prototype refinement. The initialization module generates the initial prototype based on the visual feature extracted by the feature extraction module. Then, the iterative refinement module leverages the cross-attention mechanism to polish the previous prototype by aggregating it with the original video feature. Through repeated refinement, the prototype finally converges to a more stable and accurate state, leading to a fluent and appropriate translation. In addition, to leverage the sequential dependence of prototypes, we further propose an iterative distillation loss to compress the knowledge of the final iteration into previous ones. As the autoregressive decoding process is executed only once in inference, our IP-SLT is ready to improve various SLT systems with acceptable overhead. Extensive experiments are conducted on public benchmarks to demonstrate the effectiveness of the IP-SLT.

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CLIP4HOI: towards adapting CLIP for practical zero-shot HOI detection 23

CLIP4HOI: towards adapting CLIP for practical zero-shot HOI ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yunyao Mao Jiajun Deng Wengang Zhou Li Li Yao Fang Houqiang Li CAS Key Laboratory of Technology in GIPAS EEIS Department University of Science and Technology of China The University of Adelaide AIML Merchants Union Consumer Finance Company Limited

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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