Papers with TEMA
TEMA: Token Embeddings Mapping for Enriching Low-Resource Language Models (2024.emnlp-main)
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| Challenge: | Low-resource languages, that is, languages that do not have a massive amount of text, risk being almost excluded from the possibility of having good NLP applications. |
| Approach: | They propose an algorithm that maps token embeddings of a richly pre-trained model to a poorly trained model and creates a more complex model. |
| Outcome: | The proposed model reduces perplexity and is competitive or better for the most semantic tasks. |
TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval (2026.acl-long)
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| Challenge: | Composed Image Retrieval (CIR) is an image retrieval paradigm that enables users to retrieve a target image using a multimodal query that consists of a reference image and modification text. |
| Approach: | They propose a text-oriented entity mapping architecture that allows users to use a reference image and modification text to retrieve a target image. |
| Outcome: | The proposed framework is superior in both original and multi-modification scenarios while maintaining an optimal balance between retrieval accuracy and computational efficiency. |