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- W4385570215 abstract "Recently, Contrastive Visual-Language Pre-training (CLIP) has demonstrated remarkable capability in various Visual Language Understanding (VLU) tasks. Yet, most CLIP-based methods require tasks-specific designs and sufficient training data. In this paper, we introduce a simple yet efficient paradigm for low-resource VLU named XtremeCLIP, which involves very few trainable parameters to improve the generalization ability of the trained models. In our XtremeCLIP framework, we reformulate a series of VLU tasks as a unified open-book affinity-matching problem. Furthermore, to handle the insufficient supervised signals in small datasets, we adopt contrastive learning to utilize the implicit sorting information of ground-truth labels to provide more supervised cues. Extensive experiments over multiple datasets on visual entailment, visual question answering, and image classification show that XtremeCLIP consistently outperforms existing baselines in low-resource settings." @default.
- W4385570215 created "2023-08-05" @default.
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- W4385570215 date "2023-01-01" @default.
- W4385570215 modified "2023-09-24" @default.
- W4385570215 title "XtremeCLIP: Extremely Parameter-efficient Tuning for Low-resource Vision Language Understanding" @default.
- W4385570215 doi "https://doi.org/10.18653/v1/2023.findings-acl.397" @default.
- W4385570215 hasPublicationYear "2023" @default.
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