activity
20242026
most citedMS-YOLO: Infrared Object Detection for Edge Deployment via MobileNetV4 and SlideLoss

1 citations · 1 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CV2026

TextShield-R1: Reinforced Reasoning for Tampered Text Detection

Chenfan Qu, Yiwu Zhong, Jian Liu +3

The growing prevalence of tampered images poses serious security threats, highlighting the urgent need for reliable detection methods. Multimodal large language models (MLLMs) demo…

cs.CV2026

Edge-Optimized Multimodal Learning for UAV Video Understanding via BLIP-2

Yizhan Feng, Hichem Snoussi, Jing Teng +4

The demand for real-time visual understanding and interaction in complex scenarios is increasingly critical for unmanned aerial vehicles. However, a significant challenge arises fr…

cs.CV2025

FerretNet: Efficient Synthetic Image Detection via Local Pixel Dependencies

Shuqiao Liang, Jian Liu, Renzhang Chen +1

The increasing realism of synthetic images generated by advanced models such as VAEs, GANs, and LDMs poses significant challenges for synthetic image detection. To address this iss…

cs.CV2025

GeoPurify: A Data-Efficient Geometric Distillation Framework for Open-Vocabulary 3D Segmentation

Weijia Dou, Xu Zhang, Yi Bin +5

Recent attempts to transfer features from 2D Vision-Language Models (VLMs) to 3D semantic segmentation expose a persistent trade-off. Directly projecting 2D features into 3D yields…

cs.CV20251 cited

MS-YOLO: Infrared Object Detection for Edge Deployment via MobileNetV4 and SlideLoss

Jiali Zhang, Thomas S. White, Haoliang Zhang +3

Infrared imaging has emerged as a robust solution for urban object detection under low-light and adverse weather conditions, offering significant advantages over traditional visibl…

cs.CV2025

SoccerNet 2025 Challenges Results

Silvio Giancola, Anthony Cioppa, Marc Gutiérrez-Pérez +115

The SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in football video understandi…