collaborators

26 papers

cs.CV2026

TimeThink: Reasoning with Time for Video LLMs

Handong Li, Longteng Guo, Zikang Liu +8

Video reasoning requires models to identify and verify temporally localized evidence within long video sequences. Recent Video Large Language Models (Video-LLMs) have shown promisi…

cs.CV2026

Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigation

Xin Zou, Haolin Deng, Yibo Yan +5

Multimodal Large Language Models (MLLMs) are prone to hallucination as their generation preferences are insufficiently calibrated to visual evidence, causing them to fall back on l…

cs.CV2026

Consistency as Inductive Bias: Learning Cross-View Invariance for Robust Multimodal Reasoning

Xin Zou, Haolin Deng, Yibo Yan +6

Inductive biases steer learning toward generalizable solutions by encoding task structure. In this work, we identify a crucial missing bias in MLLMs: cross-view consistency, \texti…

cs.CV2026

ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering

ZhengXian Wu, Hangrui Xu, Kai Shi +8

Knowledge-based Visual Question Answering (KB-VQA) requires models to combine image understanding with external knowledge. Most prior methods use a fixed retrieve-then-generate pip…

cs.CV2026

Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization

Haolin Deng, Xin Zou, Zhiwei Jin +3

Multimodal hallucination remains a persistent challenge for Vision-Language Models (VLMs). Standard textual Direct Preference Optimization (DPO) often fails to mitigate it due to a…

cs.CV2026

MergeTok: Unified Continuous and Discrete Visual Tokenization via Token Merging

Luyuan Zhang, Siyuan Li, Zedong Wang +7

Most visual tokenizers for image generation are bifurcated into two families with complementary limitations: continuous VAEs offer high-fidelity reconstruction but suffer from dens…