24 citations · 67 across the 30 of their papers we have counts for
38 papers
CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance
Shutong Ding, Zhaoyang Liu, Yimiao Zhou +3
Diffusion policies are increasingly used in robotic control and manipulation, vision-language-action models, and world-model-based agents. Their ability to represent multimodal act…
FASTER: Fast Adjoint Stochastic Transport for Endpoint Refinement in Reward-Guided Image Editing
Yimiao Zhou, Zejia Zhong, Jingya Wang +1
Reward-guided image editing at test time seeks to improve a specified reward while preserving source content and visual plausibility. Many existing approaches optimize candidates t…
Steering Generative Reinforcement Learning into Stable Robotic Controller
Yixuan Wang, Shutong Ding, Ke Hu +3
Diffusion and flow-based generative policies provide a powerful policy class for reinforcement learning by inducing rich stochastic exploration through iterative action generation.…
Conformal Reliability: A New Evaluation Metric for Conditional Generation
Yachen Gao, Xinwei Sun, Yikai Wang +4
Conditional generative models have recently achieved remarkable success in various applications. However, a suitable metric for evaluating the reliability of these models, which ta…
Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance
Shutong Ding, Zejia Zhong, Zhongyi Wang +4
Recent advances in reinforcement learning (RL) have achieved great successes by leveraging the multimodality and exploration capability of diffusion policies. Among these approache…
Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
Yuting Zeng, Zhiwen Zheng, Jingya Wang +6
Safe and efficient assistive planning for visually impaired scenarios remains challenging, since existing methods struggle with multi-objective optimization, generalization, and in…
Distributional Reinforcement Learning with Diffusion Bridge Critics
Shutong Ding, Yimiao Zhou, Ke Hu +5
Recent advances in diffusion-based reinforcement learning (RL) methods have demonstrated promising results in a wide range of continuous control tasks. However, existing works in t…
Sample from What You See: Visuomotor Policy Learning via Diffusion Bridge with Observation-Embedded Stochastic Differential Equation
Zhaoyang Liu, Mokai Pan, Zhongyi Wang +5
Imitation learning with diffusion models has advanced robotic control by capturing the multi-modal action distributions. However, existing methods typically treat observations only…
Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
Yuting Zeng, Zhiwen Zheng, You Zhou +5
This paper proposes a momentum-constrained hybrid heuristic trajectory optimization framework (MHHTOF) tailored for assistive navigation in visually impaired scenarios, integrating…
A Unified and Fast-Sampling Diffusion Bridge Framework via Stochastic Optimal Control
Mokai Pan, Kaizhen Zhu, Yuexin Ma +4
Recent advances in diffusion bridge models leverage Doob's -transform to establish fixed endpoints between distributions, demonstrating promising results in image translation an…
OpenHOI: Open-World Hand-Object Interaction Synthesis with Multimodal Large Language Model
Zhenhao Zhang, Ye Shi, Lingxiao Yang +3
Understanding and synthesizing realistic 3D hand-object interactions (HOI) is critical for applications ranging from immersive AR/VR to dexterous robotics. Existing methods struggl…
DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion
Yahao Fan, Tianxiang Gui, Kaiyang Ji +8
Achieving versatile humanoid locomotion with a single policy presents a critical scalability challenge. Prevailing methods often rely on distilling multiple terrain-specific teache…
GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning
Shutong Ding, Ke Hu, Shan Zhong +5
Recent advances in reinforcement learning (RL) have demonstrated the powerful exploration capabilities and multimodality of generative diffusion-based policies. While substantial p…
Human-Object Interaction via Automatically Designed VLM-Guided Motion Policy
Zekai Deng, Ye Shi, Kaiyang Ji +3
Human-object interaction (HOI) synthesis is crucial for applications in animation, simulation, and robotics. However, existing approaches either rely on expensive motion capture da…
ARFlow: Human Action-Reaction Flow Matching with Physical Guidance
Wentao Jiang, Jingya Wang, Kaiyang Ji +3
Human action-reaction synthesis, a fundamental challenge in modeling causal human interactions, plays a critical role in applications ranging from virtual reality to social robotic…
Monocular Human-Object Reconstruction in the Wild
Chaofan Huo, Ye Shi, Jingya Wang
Learning the prior knowledge of the 3D human-object spatial relation is crucial for reconstructing human-object interaction from images and understanding how humans interact with o…
StackFLOW: Monocular Human-Object Reconstruction by Stacked Normalizing Flow with Offset
Chaofan Huo, Ye Shi, Yuexin Ma +3
Modeling and capturing the 3D spatial arrangement of the human and the object is the key to perceiving 3D human-object interaction from monocular images. In this work, we propose t…
Harmonizing Generalization and Personalization in Federated Prompt Learning
Tianyu Cui, Hongxia Li, Jingya Wang +1
Federated Prompt Learning (FPL) incorporates large pre-trained Vision-Language models (VLM) into federated learning through prompt tuning. The transferable representations and rema…
Global and Local Prompts Cooperation via Optimal Transport for Federated Learning
Hongxia Li, Wei Huang, Jingya Wang +1
Prompt learning in pretrained visual-language models has shown remarkable flexibility across various downstream tasks. Leveraging its inherent lightweight nature, recent research a…
Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport
Bin Li, Ye Shi, Qian Yu +1
Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images sharing the same category across diverse domains without relying on labeled data. Prior approaches have typ…
Guidance with Spherical Gaussian Constraint for Conditional Diffusion
Lingxiao Yang, Shutong Ding, Yifan Cai +3
Recent advances in diffusion models attempt to handle conditional generative tasks by utilizing a differentiable loss function for guidance without the need for additional training…
HybridGait: A Benchmark for Spatial-Temporal Cloth-Changing Gait Recognition with Hybrid Explorations
Yilan Dong, Chunlin Yu, Ruiyang Ha +5
Existing gait recognition benchmarks mostly include minor clothing variations in the laboratory environments, but lack persistent changes in appearance over time and space. In this…
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning
Zhongyi Cai, Ye Shi, Wei Huang +1
Federated Learning (FL) has emerged as a promising distributed learning paradigm that enables multiple clients to learn a global model collaboratively without sharing their private…
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels
Wanxing Chang, Ye Shi, Jingya Wang
Learning with noisy labels (LNL) poses a significant challenge in training a well-generalized model while avoiding overfitting to corrupted labels. Recent advances have achieved im…