activity
20152026
most citedDeeperGCN: All You Need to Train Deeper GCNs

260 citations · 666 across the 94 of their papers we have counts for

collaborators
Showing cs.CVShow all

132 papers · 1 filter

cs.CV2026

LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation

Karen Sanchez, Carlos Hinojosa, Albert A. Ávila +5

Quantifying wound tissue composition is essential for monitoring chronic ulcer progression and guiding treatment decisions. However, pixel-level annotations are costly, and multi-t…

cs.CV2026

B-MIM: Biased Masked Image Modeling for Generalizable Segmentation of Fine-Grained Anatomical Structures

Sebastián González, Karen Sanchez, José M. Saavedra +2

Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic understanding, limiting their sensit…

cs.CV2026

HyperGS: Fast and Generalizable Gaussian Video Representation

Fatimah Zohra, Chen Zhao, Shuming Liu +2

Gaussian Splatting has emerged as an effective representation for video, but existing methods rely on per-video optimization. This leads to slow encoding and limits generalization…

cs.CV2026

EgoPlay: Event-Triggered Video Editing for Egocentric Streams

Jinjie Mai, Gordon Guocheng Qian, Willi Menapace +8

We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer on event-conditioned data buil…

cs.CV2026

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP

Fatimah Zohra, Chen Zhao, Shuming Liu +1

Contrastive Language-Image Pre-training (CLIP) relies on softmax-based self-attention, a strictly positive distribution that assigns probability mass to every pair of tokens-even s…

cs.CV2026

SoccerNet 2026 Challenges Results

Anthony Cioppa, Silvio Giancola, Håkan Ardö +102

The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video underst…