activity
20182026
most citedDeep Learning for Single Image Super-Resolution: A Brief Review

1.2k citations · 1.5k across the 81 of their papers we have counts for

collaborators

84 papers

cs.CV2026

CoverPrune: Coverage-Driven Token Pruning for 3D VLMs via Optimal Transport

Peng Ling, Yingda Yin, Lingting Zhu +5

While 3D Vision-Language Models (3D VLMs) have demonstrated remarkable spatial reasoning capabilities, they suffer from massive visual token counts that create severe computational…

cs.CV2026

EffectLearner: World-Aware Object-Effect Reasoning for Real-World Video Object Removal

Feier Wu, Wanke Xia, Xu He +8

Video object removal must eliminate not only the target object but also its induced effects while maintaining high-fidelity and spatiotemporally coherent restoration. Existing meth…

cs.CV2026

LoTA-N2N: Local Trace Adaptation for Zero-Shot Self-Supervised Image Denoising

Jintong Hu, Bin Xia, Junlin Liu +2

Single-image self-supervised denoising replaces unavailable clean targets with surrogate targets constructed from noisy observations. Its effectiveness therefore depends on how clo…

cs.CV2026

MOPDA: Mixed-Trajectory On-Policy Distillation for Language-Guided Industrial Anomaly Detection

Shuimu Chen, Jing Jin, Nan Su +5

Large vision-language models (LVLMs) have shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgments and interpretable reasoning. Howe…

cs.CV2026

Reflect-R1: Evidence-Driven Reflection for Self-Correction in Long Video Understanding

Shuimu Chen, Yuteng Chen, Yuanshen Guan +7

Current multimodal reflection mechanisms for long video understanding predominantly rely on closed-loop self-reflection within internal parameters. Lacking objective external evide…

cs.CV2026

Focus When Necessary: Adaptive Routing and Collaborative Grounding for Training-Free Visual Grounding

Yifan Wang, Peiming Li, Shiyu Li +5

While Multimodal Large Language Models (MLLMs) excel in cross-modal reasoning, they often struggle to perceive fine-grained details in complex high-resolution images. Recent traini…