9 papers
Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval
Zelong Sun, Jun Wang, Kaicheng Yang +3
Unified multimodal retrieval aims to identify candidates that satisfy complex user intent expressed through heterogeneous inputs. Although Large Vision-Language Model (LVLM)-based…
TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies
Dong Jing, Jingchen Nie, Tianqi Zhang +4
Robot manipulation alternates between low-risk transit phases that call for fast execution and high-risk contact stages that demand slow, precise motion. Yet existing Vision-Langua…
Learning Action Priors for Cross-embodiment Robot Manipulation
Dong Jing, Tianqi Zhang, Jiaqi Liu +5
Most Vision-Language-Action (VLA) models build on a Vision-Language Model (VLM) backbone by attaching an action module and optimizing the full policy jointly. This design inherits…
UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards
Jun Wang, Shuo Tan, Zelong Sun +5
Retrieval-Augmented Generation (RAG) extends Large Vision-Language Models (LVLMs) with external visual knowledge. However, existing visual RAG systems typically rely on generic ret…
STEAR: Layer-Aware Spatiotemporal Evidence Intervention for Hallucination Mitigation in Video Large Language Models
Linfeng Fan, Yuan Tian, Ziwei Li +1
Video Large Language Models (Video-LLMs) remain prone to spatiotemporal hallucinations, often generating visually unsupported details or incorrect temporal relations. Existing miti…
Say Cheese! Detail-Preserving Portrait Collection Generation via Natural Language Edits
Zelong Sun, Jiahui Wu, Ying Ba +2
As social media platforms proliferate, users increasingly demand intuitive ways to create diverse, high-quality portrait collections. In this work, we introduce Portrait Collection…
Mixture of Horizons in Action Chunking
Dong Jing, Gang Wang, Jiaqi Liu +7
Vision-language-action (VLA) models have shown remarkable capabilities in robotic manipulation, but their performance is sensitive to the used during…
CoTMR: Chain-of-Thought Multi-Scale Reasoning for Training-Free Zero-Shot Composed Image Retrieval
Zelong Sun, Dong Jing, Zhiwu Lu
Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images by integrating information from a composed query (reference image and modification text) without training…
Leveraging Large Vision-Language Model as User Intent-aware Encoder for Composed Image Retrieval
Zelong Sun, Dong Jing, Guoxing Yang +2
Composed Image Retrieval (CIR) aims to retrieve target images from candidate set using a hybrid-modality query consisting of a reference image and a relative caption that describes…