| Challenge: | Text-image matching is one of the most popular methods for training text-image embeddings. |
| Approach: | They propose to use a kNN-margin loss that utilizes hard negatives and is robust to noise . they advocate using Inverted Softmax and Cross-modal Local Scaling during inference . |
| Outcome: | The proposed loss function is robust to noise and pseudo negatives are tolerable . the proposed loss functions improve scores of all metrics by a large margin . |
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
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Uncovering Limitations in Text-to-Image Generation: A Contrastive Approach with Structured Semantic Alignment (2023.findings-emnlp)
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Misalignment Attack on Text-to-Image Models via Text Embedding Optimization and Inversion (2025.findings-emnlp)
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Simple and Effective Text Matching with Richer Alignment Features (P19-1)
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Understanding the Influence of Synthetic Data for Text Embedders (2025.findings-acl)
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