Papers with vision and language model
Efficient OCR for Building a Diverse Digital History (2024.acl-long)
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| Challenge: | Current optical character recognition (OCR) systems are poorly extensible to low-resource document collections, as learning a language-vision model requires extensive labeled sequences and compute. |
| Approach: | They propose to model optical character recognition as a character level image retrieval problem using a contrastively trained vision encoder. |
| Outcome: | The proposed model is more sample efficient and extensible than existing architectures, enabling accurate OCR in settings where existing solutions fail. |
Generative Multimodal Entity Linking (2024.lrec-main)
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| Challenge: | Existing Entity Linking methods focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters. |
| Approach: | They propose a framework for multimodal entity linking based on Large Language Models (LLMs) that trains a feature mapper to enable cross-modal interactions. |
| Outcome: | The proposed framework achieves state-of-the-art on two well-established datasets with a performance gain of 7.7% on WikiDiverse and 8.8% on Wikileaks. |