Recent research has introduced Targeted-Prompting (TAP), a novel method that enhances the performance of Vision and Language Models (VLMs) like CLIP. TAP utilizes the extensive knowledge of Large Language Models to produce text-only samples that highlight specific visual attributes of tasks. This allows for a text classifier to train on these samples, eliminating the need for paired image-text data. When tested on datasets such as UCF-101 and ImageNet-Rendition, TAP showcased remarkable improvements. A key element of this study is the efficient cross-modal transfer between text and image, signaling a shift towards leveraging text data for advanced visual recognition systems, potentially reducing the dependence on vast visual datasets.
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Targeted-Prompting (TAP): Unlock Potential of Text Data in Training Advanced Visual Recognition Systems
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