AI RESEARCH
Group Orthogonal Low-Rank Adaptation for RGB-T Tracking
arXiv CS.CV
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ArXi:2512.05359v2 Announce Type: replace Parameter-efficient fine-tuning has emerged as a promising paradigm in RGB-T tracking, enabling downstream task adaptation by freezing pretrained parameters and fine-tuning only a small set of parameters. This set forms a rank space made up of multiple individual ranks, whose expressiveness directly shapes the model's adaptability. However, quantitative analysis reveals low-rank adaptation exhibits significant redundancy in the rank space, with many ranks contributing almost no practical information.