Papers with text-image retrieval
CocaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval (2023.acl-industry)
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| Challenge: | Existing methods for text-image retrieval are limited to edge devices and real-time situations due to the substantial indexing and inference time. |
| Approach: | They propose a fully-Connected knowledge interaction graph technique for cross-modal pre-training distillation. |
| Outcome: | The proposed method achieves SOTA performances on the widely-used Flickr30K and MSCOCO benchmarks under the lightweight setting. |
Towards Multi-Modal Text-Image Retrieval to improve Human Reading (2021.naacl-srw)
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| Challenge: | In primary school, children's books, as well as in modern language learning apps, multi-modal learning strategies like illustrations of terms and phrases are used to support reading comprehension. |
| Approach: | They propose to use multi-modal transformers to train multi-dimensional models on text-image retrieval to support a user's reading comprehension of arbitrary text. |
| Outcome: | The proposed model performs poorly because of the short and relatively simple textual data that the current models are trained with. |
Visualizing Dialogues: Enhancing Image Selection through Dialogue Understanding with Large Language Models (2024.findings-acl)
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| Challenge: | Existing methods for dialogue-to-image retrieval are constrained by pre-trained vision language models. |
| Approach: | They leverage the reasoning capabilities of large language models to predict potential features in images to be shared based on dialogue context. |
| Outcome: | The proposed method outperforms existing methods significantly in terms of Recall@k. |
VisRet: Visualization Improves Knowledge-Intensive Text-to-Image Retrieval (2026.acl-long)
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| Challenge: | Text-to-image retrieval is challenging because of cross-modal embeddings are bags of concepts, underrepresenting structured visual relationships. |
| Approach: | They propose a retrieval paradigm that embeds textual queries into the image modality via T2I generation and performs retrieval within the image mode to bypass weaknesses of cross-modal retrievers in recognizing subtle visual-spatial features. |
| Outcome: | The proposed retrieval paradigm outperforms previous approaches in visual-spatial retrieval benchmarks. |
Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning (2024.lrec-main)
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| Challenge: | Existing approaches to learn multi-modal tasks are based on chain-of-thought . however, human thought processes are non-linear and employ dynamic adjustment and updating mechanisms. |
| Approach: | They propose a chain-of-thought technique that adjusts the length of the chain to improve the performance of generated prompts. |
| Outcome: | The proposed model improves multi-modal representation learning in visual, visual, and audio-visual tasks and also has good domain generalization performance due to better reasoning. |