4 citations
3 papers
cs.CV2026★ 1 cited
Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data
Yiming Zhou, Xuenjie Xie, Panfeng Li +3
Segment Anything Models (SAM) achieve impressive universal segmentation performance but require massive datasets (e.g., 11M images) and rely solely on RGB inputs. Recent efficient…
cs.LG2022
Reinforcement Learning Agent Design and Optimization with Bandwidth Allocation Model
Rafael F. Reale, Joberto S. B. Martins
Reinforcement learning (RL) is currently used in various real-life applications. RL-based solutions have the potential to generically address problems, including the ones that are…
cs.FL2014★ 4 cited
Cost Preserving Bisimulations for Probabilistic Automata
Andrea Turrini, Holger Hermanns
Probabilistic automata constitute a versatile and elegant model for concurrent probabilistic systems. They are equipped with a compositional theory supporting abstraction, enabled…