Papers with Vision-language tasks
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding (2023.findings-acl)
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| Challenge: | supervised methods for vision-language tasks have been well-studied, but they lack the fine-grained information needed for semantics understanding. |
| Approach: | They propose a framework to take advantage of fine-grained information for zero-shot vision-language learning, covering multiple tasks such as VQA, SNLI-VE, and VCR. |
| Outcome: | The proposed framework outperforms previous zero-shot methods on VQA and achieves substantial improvement on SNLI-VE and VCR. |