Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO (2021.eacl-main)
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| Challenge: | Existing image captioning datasets have limited cross-modal associations, preventing researchers from examining how inter-modal learning impacts intra-modal tasks. |
| Approach: | They propose to use image captioning data to support multi-modal retrieval training and evaluation to assess the impact of inter-modality learning. |
| Outcome: | The proposed model is able to measure the influence of intra- and inter-modality learning. |
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| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
| Approach: | They propose a new approach to learn multimodal multilingual embeddings for matching images and captions in two languages by combing two existing objective functions and adapting alignment between existing languages. |
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Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (D19-64)
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| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
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| Challenge: | Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. |
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| Challenge: | Existing methods for image captioning do not guarantee consistent image-text relations . current models do not provide enough data for training robust captioning models . |
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Improving Image Captioning with Better Use of Caption (2020.acl-main)
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| Challenge: | Existing approaches to image captioning focus on visual attention, but many do not. |
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Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment (2024.lrec-main)
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