Pt}: Anti-Associative Prompt Tuning for Open Set Visual Recognition

Multi-modality pre-trained models (PTMs) have considerably boosted the performance on a broad range of computer vision topics. Still, they have not been explored purposefully in open set recognition (OSR) scenarios when applying PTMs to downstream recognition tasks. Directly fine/prompt tuning PTMs...

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Veröffentlicht in:	IEEE transactions on multimedia 2024, Vol.26, p.8419-8431
Hauptverfasser:	Ren, Hairui, Tang, Fan, Pan, Xingjia, Cao, Juan, Dong, Weiming, Lin, Zhiwen, Yan, Ke, Xu, Changsheng
Format:	Artikel
Sprache:	eng
Schlagworte:	anti-associative prompt tuning (<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> A^{2} Pt</tex-math> </inline-formula> </named-content>) Calibration class-aware representation Image recognition Multi-modality Pre-trained models (PTMs) Neck open set recognition (OSR) Task analysis Training Tuning Visualization
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