Papers with SNLI-VE
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. |
An Anchor-based Relative Position Embedding Method for Cross-Modal Tasks (2022.emnlp-main)
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| Challenge: | Position Embedding (PE) is essential for transformer to capture the sequence ordering of input tokens. |
| Approach: | They propose a unified position embedding method that bridges the semantic gap between modalities and embeds the anchor-based distance to guide computation of cross-attention. |
| Outcome: | The proposed method obtains new SOTA results on a wide range of benchmarks. |
IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models (2023.findings-emnlp)
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Haoxuan You, Rui Sun, Zhecan Wang, Long Chen, Gengyu Wang, Hammad Ayyubi, Kai-Wei Chang, Shih-Fu Chang
| Challenge: | Existing approaches to decompose VL reasoning rely on domain-specific sub-question decomposing models. |
| Approach: | They propose a framework that iteratively decomposes VL reasoning using large language models. |
| Outcome: | The proposed framework outperforms existing models on multiple VL reasoning tasks. |