collaborators

12 papers

cs.CV2026

PIGEONS: Incremental Long-Video Captioning with Persistent Identity Gallery and Evolving Overarching Narrative Summary

Xinlong Chen, Weihong Lin, Yuanxing Zhang +9

Detailed video captioning has become an essential semantic interface for multi-modal understanding and generation. While existing methods mainly target short videos, extending them…

cs.RO2026

BridgeVLA++: A Data-Efficient, Generalizable, and Memory-Augmented Vision-Language-Action Framework for 3D Manipulation

Peiyan Li, Yuze Zhu, Yixiang Chen +10

Leveraging pre-trained vision-language models (VLMs) to construct vision-language-action (VLA) models has emerged as a promising paradigm for 3D robot manipulation. However, existi…

cs.CV2026

CapRiCorn-1K: A Comprehensive Benchmark for Video Captioning and Subject Referential Consistency Across Temporal Scales

Xinlong Chen, Jiafu Tang, Yue Ding +12

Accurate and comprehensive video captions with consistent subject references are critical for downstream understanding and generation tasks. However, few existing benchmarks can ob…

cs.AI2026

RC-GRPO: Reward-Conditioned Group Relative Policy Optimization for Multi-Turn Tool Calling Agents

Haitian Zhong, Jixiu Zhai, Lei Song +3

Multi-turn tool calling is challenging for Large Language Models (LLMs) because rewards are sparse and exploration is expensive. A common recipe, SFT followed by GRPO, can stall wh…

cs.CL2026

DiaDem: Advancing Dialogue Descriptions in Audiovisual Video Captioning for Multimodal Large Language Models

Xinlong Chen, Weihong Lin, Jingyun Hua +10

Accurate dialogue description in audiovisual video captioning is crucial for downstream understanding and generation tasks. However, existing models generally struggle to produce f…

cs.CV2025

AVoCaDO: An Audiovisual Video Captioner Driven by Temporal Orchestration

Xinlong Chen, Yue Ding, Weihong Lin +9

Audiovisual video captioning aims to generate semantically rich descriptions with temporal alignment between visual and auditory events, thereby benefiting both video understanding…

cs.CV2025

Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing

Junfei Wu, Jian Guan, Kaituo Feng +5

As textual reasoning with large language models (LLMs) has advanced significantly, there has been growing interest in enhancing the multimodal reasoning capabilities of large visio…

cs.CL2025

Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models

Xinlong Chen, Yuanxing Zhang, Qiang Liu +3

Large Vision-Language Models (LVLMs) have exhibited impressive capabilities across various visual tasks, yet they remain hindered by the persistent challenge of hallucinations. To…

cs.CV2025

VidBridge-R1: Bridging QA and Captioning for RL-based Video Understanding Models with Intermediate Proxy Tasks

Xinlong Chen, Yuanxing Zhang, Yushuo Guan +9

The "Reason-Then-Respond" paradigm, enhanced by Reinforcement Learning, has shown great promise in advancing Multimodal Large Language Models. However, its application to the video…

cs.RO2025

BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models

Peiyan Li, Yixiang Chen, Hongtao Wu +6

Recently, leveraging pre-trained vision-language models (VLMs) for building vision-language-action (VLA) models has emerged as a promising approach to effective robot manipulation…

cs.AI2025

REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing

Haitian Zhong, Yuhuan Liu, Ziyang Xu +6

Large language model editing methods frequently suffer from overfitting, wherein factual updates can propagate beyond their intended scope, overemphasizing the edited target even w…

cs.AI2025

VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation

Xinlong Chen, Yuanxing Zhang, Chongling Rao +7

The training of controllable text-to-video (T2V) models relies heavily on the alignment between videos and captions, yet little existing research connects video caption evaluation…