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PointPiT is proposed, a partition-invariant tuning framework for scene-level point clouds that integrates local geometric patterns with global scene context to mitigate partition-induced representation shifts, and achieves consistent state-of-the-art performance among representative PEFT methods.
AdaSAM2 is introduced, a framework that formulates adaptive search-region selection as a sequential decision-making problem that employs an event-driven reinforcement learning policy that selects the cropping scale only during initialization and unreliable tracking states, while reusing the previous configuration durin...
This paper reviews recent progress from the perspectives of dataset evolution, model architectures, and downstream adaptation strategies, covering parameter-efficient fine-tuning, prompt engineering, few-shot and zero-shot learning, open-vocabulary segmentation, and domain adaptation.
SpikingMOT is proposed as a spike-driven tracker that adaptively models sparse trajectory dynamics with spiking neural networks (SNNs) and brings SNNs into MOT, opening a promising direction for efficient tracking.
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