202 citations · 278 across the 38 of their papers we have counts for
51 papers
FLaRA: Predicting Future Latent Representations for Accident Anticipation
Lorenzo Caselli, Tomaso Trinci, Tommaso Bianconcini +4
Anticipating traffic accidents from dashcam videos is a critical challenge in intelligent transportation systems. Existing methods typically map visual context directly to a collis…
ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders
Carlo Romeo, Andrew D. Bagdanov
Reinforcement learning for legged locomotion has matured into a stack of multi-component reward functions and physics-engine benchmarks whose morphologies are uniformly derived fro…
SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data
Carlo Romeo, Girolamo Macaluso, Alessandro Sestini +1
Incorporating prior data into online reinforcement learning accelerates training but typically forces a difficult trade-off between high computational costs and long, multi-stage t…
Intermediate Layers Encode Optimal Biological Representations in Single-Cell Foundation Models
Vincenzo Yuto Civale, Roberto Semeraro, Andrew David Bagdanov +1
Current single-cell foundation model benchmarks universally extract final layer embeddings, assuming these represent optimal feature spaces. We systematically evaluate layer-wise r…
Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR
Giacomo Cignoni, Simone Magistri, Andrew D. Bagdanov +1
This paper explores Online Continual Self-Supervised Learning (OCSSL), a scenario in which models learn from continuous streams of unlabeled, non-stationary data, where methods typ…
Cross-Modal Prototype Alignment and Mixing for Training-Free Few-Shot Classification
Dipam Goswami, Simone Magistri, Gido M. van de Ven +4
Vision-language models (VLMs) like CLIP are trained with the objective of aligning text and image pairs. To improve CLIP-based few-shot image classification, recent works have obse…