1 citations · 1 across the 9 of their papers we have counts for
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Advancing Video-Text Pretraining with Multi-View Captions
Fida M. Thoker, Renaud Vandeghen, Karen Sanchez +2
Video-text pretraining has achieved remarkable progress through the scaling of models and datasets, yet the quality of language supervision remains underexplored. Existing web-scal…
Last Translation Benchmark
Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…
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…
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…
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…
Skill-Aligned Annotation for Reliable Evaluation in Text-to-Image Generation
Abdelrahman Eldesokey, Merey Ramazanova, Ahmad Sait +4
Text-to-image (T2I) generation has advanced rapidly, making reliable evaluation critical as performance differences between models narrow. Existing evaluation practices typically a…
CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation
Pablo Messina, Andrés Villa, Juan León Alcázar +5
Medical vision-language models can automate the generation of radiology reports but struggle with accurate visual grounding and factual consistency. Existing models often misalign…