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20212025
most citedMM-ViT: Multi-Modal Video Transformer for Compressed Video Action Recognition

8 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

F2T2-HiT: A U-Shaped FFT Transformer and Hierarchical Transformer for Reflection Removal

Jie Cai, Kangning Yang, Ling Ouyang +5

Single Image Reflection Removal (SIRR) technique plays a crucial role in image processing by eliminating unwanted reflections from the background. These reflections, often caused b…

cs.CV2025

TokenMotion: Decoupled Motion Control via Token Disentanglement for Human-centric Video Generation

Ruineng Li, Daitao Xing, Huiming Sun +3

Human-centric motion control in video generation remains a critical challenge, particularly when jointly controlling camera movements and human poses in scenarios like the iconic G…

cs.CV2025

VIP: Video Inpainting Pipeline for Real World Human Removal

Huiming Sun, Yikang Li, Kangning Yang +11

Inpainting for real-world human and pedestrian removal in high-resolution video clips presents significant challenges, particularly in achieving high-quality outcomes, ensuring tem…

cs.CV2025

Survey on Single-Image Reflection Removal using Deep Learning Techniques

Kangning Yang, Huiming Sun, Jie Cai +5

The phenomenon of reflection is quite common in digital images, posing significant challenges for various applications such as computer vision, photography, and image processing. T…

cs.CV2022

Real-Time Super-Resolution for Real-World Images on Mobile Devices

Jie Cai, Zibo Meng, Jiaming Ding +1

Image Super-Resolution (ISR), which aims at recovering High-Resolution (HR) images from the corresponding Low-Resolution (LR) counterparts. Although recent progress in ISR has been…

cs.CV2021★ 8 cited

MM-ViT: Multi-Modal Video Transformer for Compressed Video Action Recognition

Jiawei Chen, Chiu Man Ho

This paper presents a pure transformer-based approach, dubbed the Multi-Modal Video Transformer (MM-ViT), for video action recognition. Different from other schemes which solely ut…