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cs.CV2026

Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs

Guangtao Lyu, Qi Liu, Chenghao Xu +5

LVLMs have achieved strong multimodal reasoning capabilities but remain prone to hallucinations, producing outputs inconsistent with visual inputs or user instructions. Existing tr…

cs.CV2026

Beyond Global Alignment: Fine-Grained Motion-Language Retrieval via Pyramidal Shapley-Taylor Learning

Hanmo Chen, Guangtao Lyu, Chenghao Xu +3

As a foundational task in human-centric cross-modal intelligence, motion-language retrieval aims to bridge the semantic gap between natural language and human motion, enabling intu…

cs.CV2026

Past- and Future-Informed KV Cache Policy with Salience Estimation in Autoregressive Video Diffusion

Hanmo Chen, Chenghao Xu, Xu Yang +2

Video generation is pivotal to digital media creation, and recent advances in autoregressive video generation have markedly enhanced the efficiency of real-time video synthesis. Ho…

cs.CV2026

Towards Interpretable Hallucination Analysis and Mitigation in LVLMs via Contrastive Neuron Steering

Guangtao Lyu, Xinyi Cheng, Qi Liu +5

LVLMs achieve remarkable multimodal understanding and generation but remain susceptible to hallucinations. Existing mitigation methods predominantly focus on output-level adjustmen…

cs.CV2025

Towards Arbitrary Motion Completing via Hierarchical Continuous Representation

Chenghao Xu, Guangtao Lyu, Qi Liu +3

Physical motions are inherently continuous, and higher camera frame rates typically contribute to improved smoothness and temporal coherence. For the first time, we explore continu…

cs.CV2025

A Turn Toward Better Alignment: Few-Shot Generative Adaptation with Equivariant Feature Rotation

Chenghao Xu, Qi Liu, Jiexi Yan +2

Few-shot image generation aims to effectively adapt a source generative model to a target domain using very few training images. Most existing approaches introduce consistency cons…