activity
20232026
most citedSoccerNet 2023 Challenges Results

28 citations · 29 across the 18 of their papers we have counts for

collaborators

19 papers

cs.CL2026

Last Translation Benchmark

Vilém Zouhar, Niyati Bafna, Mukund Choudhary +241

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…

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.AI2026

DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization

Tong Zhang, Motasem Alfarra, Carlos Hinojosa +2

As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, furthe…

cs.LG2026

HyperSafe: Inference-Time Safety Recovery for Fine-Tuned Language Models

Aznaur Aliev, Carlos Hinojosa, Abdelrahman Eldesokey +3

Safety alignment in large language models can be fragile under fine-tuning, as even benign task adaptation may increase harmful compliance. Existing defenses mainly follow two dire…

cs.AI2026

SGA: Plug&Play Geometric Verification for Educational Video Synthesis

Jhon Lopez, Carlos Hinojosa, Bernard Ghanem

Recent work leverages Large Language Models (LLMs) to generate executable code for pedagogical animations using libraries such as Manim. However, ensuring spatial correctness and v…

cs.CR2026

Defending Against Harmful Supervision Hidden in Benign Samples

Bang An, Yibo Yang, Dandan Guo +3

Existing defenses are effective when harmful content is explicitly mixed into downstream fine-tuning data, but crafted samples can instead hide harmful supervision inside benign ta…