2 papers
cs.CL2026
What's the Catch? Evaluating Temporal Consistency in Vision-Language Models
Marek Hradil, Danae Sánchez Villegas
Vision-language models (VLMs) achieve strong performance on video and image-sequence benchmarks, yet it remains unclear whether they capture temporal structure. To study this quest…
cs.CV2025
LLEXICORP: End-user Explainability of Convolutional Neural Networks
VojtÄch Kůr, Adam Bajger, Adam KukuÄka +3
Convolutional neural networks (CNNs) underpin many modern computer vision systems. With applications ranging from common to critical areas, a need to explain and understand the mod…