LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval (2021.naacl-main)
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| Challenge: | Existing pre-trained models suffer from slow inference speed due to cross-modal attention in transformer architecture. |
| Approach: | They propose a multimodal approach that accelerates the inference time of ITR by thousands of times . they extract pre-cached feature indexes offline and employ instant dot-product matching online . |
| Outcome: | The proposed approach outperforms existing models that consume 1000 times magnitude of computational hours using the same features. |
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| Challenge: | Existing vision-and-language pre-training models suffer from long visual sequences . experimental results show that TRIPS gains a speedup of 40% over previous similar VLP models . |
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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 approaches to improve pre-trained language models lack visual commonsense and semantics. |
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| Challenge: | Existing vision-language pre-training models use multi-modal encoders to encode image and text, causing noisy training corpora. |
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| Challenge: | Large-scale two-stream pre-trained models like CLIP have achieved tremendous success in image-text retrieval. |
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E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning (2021.acl-long)
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| Challenge: | Existing vision-language pre-training methods use a two-step training procedure to learn visual features from image-text pairs. |
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| Challenge: | Existing models that perform explicit on-task training of graph embeddings are inadequate. |
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