collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2026

MotionCraft: Latent World Modeling with Sparse Attention for Visual Upscaling

Rong Fu, Chunlei Meng, Yangchen Zeng +9

Video super-resolution (VSR) aims to recover high-fidelity high-resolution videos from low-resolution inputs and is central to applications ranging from mobile capture to streaming…

cs.CV2026

Do Vision Models Truly Forget? New Findings from Representation-Level Certification of Visual Unlearning in Vertical Federated Learning

Zhenyu Yu, Yangchen Zeng, Chunlei Meng +2

Machine unlearning in Vertical Federated Learning (VFL) has attracted growing interest, yet existing methods certify forgetting solely using output-level metrics. We challenge thes…

cs.CV2026

SpaMEM: Benchmarking Dynamic Spatial Reasoning via Perception-Memory Integration in Embodied Environments

Chih-Ting Liao, Xi Xiao, Chunlei Meng +6

Multimodal large language models (MLLMs) have advanced static visual--spatial reasoning, yet they often fail to preserve long-horizon spatial coherence in embodied settings where b…

cs.CV2026

CoDA: Exploring Chain-of-Distribution Attacks and Post-Hoc Token-Space Repair for Medical Vision-Language Models

Xiang Chen, Fangfang Yang, Chunlei Meng +6

Medical vision--language models (MVLMs) are increasingly used as perceptual backbones in radiology pipelines and as the visual front end of multimodal assistants, yet their reliabi…

cs.CV2026

DIVER: Dynamic Iterative Visual Evidence Reasoning for Multimodal Fake News Detection

Weilin Zhou, Zonghao Ying, Chunlei Meng +6

Multimodal fake news detection is crucial for mitigating adversarial misinformation. Existing methods, relying on static fusion or LLMs, face computational redundancy and hallucina…

cs.CV2025

Adversarial Robustness for Unified Multi-Modal Encoders via Efficient Calibration

Chih-Ting Liao, Zhangquan Chen, Chunlei Meng +3

Recent unified multi-modal encoders align a wide range of modalities into a shared representation space, enabling diverse cross-modal tasks. Despite their impressive capabilities,…