When CLIP meets cross-modal hashing retrieval: A new strong baseline?
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Pre-trained vision-language models have become the foundation for various downstream tasks, but their application in scene text recognition (STR) has been limited. However, CLIP, a vision-language model, has the potential to be a powerful scene text reader. CLIP4STR is a new STR method that utilizes the image and text encoders of CLIP. It consists of two encoder-decoder branches: a visual branch and a cross-modal branch. The visual branch provides an initial prediction based on visual features, while the cross-modal branch refines this prediction by addressing the discrepancy between visual features and text semantics. CLIP4STR achieves state-of-the-art performance on 11 STR benchmarks and establishes a strong baseline for future STR research with vision-language models . Additionally, CLIP-Hash is a lightweight hashing retrieval network that utilizes the pre-trained CLIP model to obtain better hash features. It outperforms other hashing methods and requires only a few training samples . The Self Attentive CLIP Hashing (SACH) model focuses on unsupervised cross-modal hashing tasks and utilizes the pre-trained CLIP model to construct a feature extraction network. It achieves superior performance compared to other unsupervised hashing methods .
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| Papers (5) | Insight |
|---|---|
13 Dec 2022 | The provided paper is about a Self Attentive CLIP Hashing (SACH) model for unsupervised cross-modal retrieval. It does not mention anything about CLIP meeting cross-modal hashing retrieval as a new strong baseline. |
23 May 2023 | The provided paper is about CLIP4STR, a simple baseline for scene text recognition with pre-trained vision-language models. It does not mention anything about CLIP meeting cross-modal hashing retrieval or a new strong baseline. |
24 May 2023 | The paper proposes a lightweight hashing retrieval network called CLIP-Hash, which utilizes the pre-trained CLIP model to obtain better hash features and outperforms state-of-the-art hashing methods. |
1 Citations | The provided paper is not about "CLIP meets cross-modal hashing retrieval". |
24 May 2023 | The paper does not mention the specific phrase "When CLIP meets cross-modal hashing retrieval: A new strong baseline." |
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