1 citations · 2 across the 13 of their papers we have counts for
16 papers
LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts
Wenxuan Guo, Yingping Liang, Zhaoguo Wang +11
Scaling expert demonstrations alone does not enable generalist robot policies to improve from deployment. Real-world rollouts contain successes, suboptimal behavior, and failures:…
R2RDreamer: 3D-aware Data Augmentation for Spatially-generalized 2D Manipulation Policies
Xiuwei Xu, Haowen Sun, Angyuan Ma +7
Spatial generalization is critical for imitation-learned manipulation policies, but achieving it typically requires scaling demonstrations across diverse object poses, robot config…
iMaC: Translating Actions into Motion and Contact Images for Embodied World Models
Zhenyu Wu, Xiuwei Xu, Yukun Zhou +8
Embodied world models have emerged as a pivotal paradigm for visual robotic decision-making and interactive environment simulation. However, conventional embodied frameworks rely o…
DisDop: Distillation with Domain Priors for Open-Vocabulary Aerial Object Detection
Ruihao Xu, Yong Liu, Yansong Tang +6
With the widespread application of drones in recent years, object detection of aerial images has attracted increasing attention, especially open-vocabulary aerial detection which i…
Segment Anything with Motion, Geometry, and Semantic Adaptation for Complex Nonlinear Visual Object Tracking
Deyi Zhu, Yuji Wang, Yong Liu +4
Traditional visual object tracking (VOT) methods typically rely on task-specific supervised training, limiting their generalization to unseen objects and challenging scenarios with…
UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection
Yanran Zhang, Wenzhao Zheng, Yifei Li +5
In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fie…
ShapeGen: Robotic Data Generation for Category-Level Manipulation
Yirui Wang, Xiuwei Xu, Angyuan Ma +3
Manipulation policies deployed in uncontrolled real-world scenarios are faced with great in-category geometric diversity of everyday objects. In order to function robustly under su…
F2F-AP: Flow-to-Future Asynchronous Policy for Real-time Dynamic Manipulation
Haoyu Wei, Xiuwei Xu, Ziyang Cheng +5
Asynchronous inference has emerged as a prevalent paradigm in robotic manipulation, achieving significant progress in ensuring trajectory smoothness and efficiency. However, a syst…
CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection
Ziyang Cheng, Haoyu Wei, Hang Yin +4
While decoupled control schemes for legged mobile manipulators have shown robustness, learning holistic whole-body control policies for tracking global end-effector poses remains f…
NeXT-IMDL: Build Benchmark for NeXT-Generation Image Manipulation Detection & Localization
Yifei Li, Haoyuan He, Yu Zheng +5
The accessibility surge and abuse risks of user-friendly image editing models have created an urgent need for generalizable, up-to-date methods for Image Manipulation Detection and…
QE: Learning Discrete Distribution Discrepancy-aware Quantization Error for Autoregressive-Generated Image Detection
Yanran Zhang, Bingyao Yu, Yu Zheng +5
The emergence of visual autoregressive (AR) models has revolutionized image generation while presenting new challenges for synthetic image detection. Unlike previous GAN or diffusi…
R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation
Xiuwei Xu, Angyuan Ma, Hankun Li +4
Towards the aim of generalized robotic manipulation, spatial generalization is the most fundamental capability that requires the policy to work robustly under different spatial dis…
Learning Counterfactually Decoupled Attention for Open-World Model Attribution
Yu Zheng, Boyang Gong, Fanye Kong +6
In this paper, we propose a Counterfactually Decoupled Attention Learning (CDAL) method for open-world model attribution. Existing methods rely on handcrafted design of region part…
Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions
Jinyi Chang, Dongliang Chang, Lei Chen +2
In recent years, Fine-Grained Visual Classification (FGVC) has achieved impressive recognition accuracy, despite minimal inter-class variations. However, existing methods heavily r…
Multimodal Conditional Information Bottleneck for Generalizable AI-Generated Image Detection
Haotian Qin, Dongliang Chang, Yueying Gao +3
Although existing CLIP-based methods for detecting AI-generated images have achieved promising results, they are still limited by severe feature redundancy, which hinders their gen…
Toward Generalizable Forgery Detection and Reasoning
Yueying Gao, Dongliang Chang, Bingyao Yu +5
Accurate and interpretable detection of AI-generated images is essential for mitigating risks associated with AI misuse. However, the substantial domain gap among generative models…