Hugging Face Trending Papers
LiAM-SAM: Lifecycle-Aware Memory for Robust SAM2-Based MOT
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arXiv Computer Vision
Sep 24
LiAM-SAM: Lifecycle-Aware Memory for Robust SAM2-Based MOT
LiAM‑SAM is a lifecycle‑aware memory framework designed to improve segmentation‑based multi‑object tracking (MOT) with the SAM2 foundation video model. It addresses three common failure modes—faulty track initiation, memory drift during close interactions, and unreliable re‑identification after occlusion—by introducing contrastive track initiation, motion‑ and geometry‑grounded memory correction, and adaptive context memory. The system achieves state‑of‑the‑art HOTA and IDF1 scores, with ablations showing significant gains in association metrics and a 96% reduction in identity switches.
arXiv Computer Vision
Aug 25
Competitive Memory Readout for Robust Video Object Segmentation: 2nd Place Technical Report for the MOSEv2 Track of the 8th LSVOS Challenge
arXiv:2608.22064v1 Announce Type: new Abstract: We present our solution for the MOSEv2 track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge at ECCV 2026. The challenge evaluates r...
arXiv Computer Vision
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Event-Driven Refresh and Recurrence Memory to Reduce Stale Grounding in Referring Video Object Segmentation
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arXiv Computer Vision
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LayerRecall: A State-Conditioned Memory Router for Long-Horizon Consistency in Video Generation
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arXiv AI
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Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking
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arXiv Machine Learning
Jul 3
COVTrack++: Learning Open-Vocabulary Multi-Object Tracking from Continuous Videos via a Synergistic Paradigm
arXiv:2603. 24016v2 Announce Type: replace-cross Abstract: Multi-Object Tracking (MOT) has traditionally focused on a few specific categories, restricting its applicability to real-world scenarios involving diverse objects.