Vision-Free Retrieval: Rethinking Multimodal Search with Textual Scene Descriptions (2025.emnlp-main)
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| Challenge: | Contrastively trained Vision-Language Models exhibit shallow language understanding, manifesting bag-of-words behaviour. |
| Approach: | They propose a vision-free, single-encoder retrieval pipeline to replace traditional text-to-image retrieval paradigm with structured image descriptions. |
| Outcome: | The proposed approach reduces the modality gap and improves compositionality and performance on short and long caption queries. |
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| Challenge: | Visual Document Retrieval (VDR) relies on text-to-image retrieval using specialized bi-encoders . et al., 2022, 2024, 2021, 2023, 2026, 2030, 2040, 2050, 2060) document retrieval bridges human or artificial agents to the most relevant information, authors say . |
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Exploring How Generative MLLMs Perceive More Than CLIP with the Same Vision Encoder (2025.acl-long)
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| Challenge: | Existing studies on image and text retrieval using a dual-encoder model have not shown their effectiveness for fast inferences. |
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| Challenge: | Large-scale pretraining and task-specific fine-tuning are now the standard methodology for many tasks in computer vision and natural language processing. |
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Junjie Zhou, Yongping Xiong, Zheng Liu, Ze Liu, Shitao Xiao, Yueze Wang, Bo Zhao, Chen Jason Zhang, Defu Lian
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| Challenge: | Combining visual modality with pretrained language models has been effective for descriptive tasks such as image captioning. |
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Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image Retrieval (2022.naacl-main)
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| Challenge: | Existing text-image approaches use pre-trained vision-language representations for text retrieval . however, these models pose non-trivial memory requirements and substantial indexing time . |
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