Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations

Fine-tuning pre-trained vision-language models, like CLIP, has yielded success on diverse downstream tasks. However, several pain points persist for this paradigm: (i) directly tuning entire pre-trained models becomes both time-intensive and computationally costly. Additionally, these tuned models t...

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Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Jg. 2024; S. 26140 - 26150
Hauptverfasser:	You, Chenyu, Mint, Yifei, Dai, Weicheng, Sekhon, Jasjeet S., Staib, Lawrence, Duncan, James S.
Format:	Tagungsbericht Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2024
Schlagworte:	Annotations Benchmark testing Computational modeling Contrastive learning Group Robustness; Multi-Modal Learning; Spurious Correlations Refining Training data Visualization
ISSN:	1063-6919, 1063-6919
Online-Zugang:	Volltext
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