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cs.CV2026
Divide, Deliberate, Decide: A Multi-Agent Framework for Fine-Grained Egocentric Action Recognition
Alessandro Sottovia, Alessandro Torcinovich, Oswald Lanz
Fine-grained action recognition in egocentric video is challenging for Vision-Language Models (VLMs): actions often differ only in small visual cues, and a single model tends to be…
cs.CV2025
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers
Sofia Casarin, Sergio Escalera, Oswald Lanz
Training-free Neural Architecture Search (NAS) efficiently identifies high-performing neural networks using zero-cost (ZC) proxies. Unlike multi-shot and one-shot NAS approaches, Z…
cs.CV2025
Gate-Shift-Pose: Enhancing Action Recognition in Sports with Skeleton Information
Edoardo Bianchi, Oswald Lanz
This paper introduces Gate-Shift-Pose, an enhanced version of Gate-Shift-Fuse networks, designed for athlete fall classification in figure skating by integrating skeleton pose data…