Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning (2021.findings-emnlp)
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| Challenge: | Existing approaches fail to generalize well to concepts that are not observed during training. |
| Approach: | They propose a framework that revolves around probing several similar image caption training instances and performing analogical reasoning over relevant entities in retrieved prototypes. |
| Outcome: | The proposed framework improves on the widely used image captioning benchmarks and on composition-related evaluation metrics. |
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| Approach: | They propose to use a set of image captioning models to benchmark their compositional generalization properties. |
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| Challenge: | Combining visual modality with pretrained language models has been effective for descriptive tasks such as image captioning. |
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| Challenge: | Existing benchmarks for vision-language models treat compositionality and long-caption understanding in isolation. |
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What Makes for Good Image Captions? (2025.findings-emnlp)
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| Challenge: | a formal information-theoretic framework is developed for image captioning . the pyramid of captions is a method that generates enriched captions by integrating local and global visual information. |
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| Challenge: | Existing studies show that neural networks struggle with compositional generalization . prior work asserts that there are fundamental differences between cognitive and connectionist architectures that make compositional globalization unlikely. |
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| Challenge: | Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components. |
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