Papers with CLIP text encoder
Visual Zero-Shot E-Commerce Product Attribute Value Extraction (2025.naacl-industry)
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| Challenge: | Existing zero-shot product attribute value extraction approaches require sellers to manually provide product descriptions. |
| Approach: | They propose a cross-modal zero-shot attribute value generation framework based on CLIP that uses product images as inputs for zero- shot inference. |
| Outcome: | The proposed framework significantly outperforms other vision-language models for zero-shot attribute value extraction. |
Fine-grained Image Captioning with CLIP Reward (2022.findings-naacl)
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| Challenge: | Modern image captioning models are usually trained with text similarity objectives . reference captions often describe only the most salient objects in images . |
| Approach: | They propose to use CLIP to calculate multi-modal similarity and use it as a reward function . they propose a simple finetuning strategy to improve grammar that does not require extra text annotation. |
| Outcome: | The proposed model generates more distinctive captions than the CIDEroptimized model on text-to-image retrieval and fineCapEval. |
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing (2024.findings-eacl)
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| Challenge: | Contrastive language-image pre-training models have demonstrated considerable success across various vision-language tasks, such as text-to-image retrieval. |
| Approach: | They propose a fine-tuning approach to enhance the representations of CLIP models for paraphrases by leveraging large language models. |
| Outcome: | The proposed model improves on baseline models across paraphrased retrieval, visual genome relation and attribution, and seven semantic textual similarity tasks. |