CodeBind: Decoupled Representation Learning for Multimodal Alignment with Unified Compositional Codebook (2026.findings-acl)
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| Challenge: | Multimodal representation alignment is crucial for large language models and robotics. |
| Approach: | They propose a framework that optimizes multimodal representation spaces through a modality-shared-specific codebook design. |
| Outcome: | The proposed framework achieves state-of-the-art performance in multimodal classification and retrieval tasks. |
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Hai Huang, Yan Xia, Shengpeng Ji, Shulei Wang, Hanting Wang, Minghui Fang, Jieming Zhu, Zhenhua Dong, Sashuai Zhou, Zhou Zhao
| Challenge: | Existing studies on discrete unified representations overlook important distinctions between different dimensions of features. |
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MSCode: Advancing Human Motion-Language Understanding via Modality-Shared Codebook (2026.findings-acl)
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| Challenge: | Existing methods for motion understanding lack precise alignment between motion and modalities . existing methods lack precise semantics and a mismatch between motion, text . |
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| Challenge: | Multimodal embedding models encode multimedia inputs into latent vector representations. |
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Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks (2026.findings-acl)
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| Challenge: | Survey aims to identify challenges of multimodal unlearning for vision, language, audio and video . retraining after deletion requests or policy updates is often impractical, survey finds . |
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Cross-Modal Discrete Representation Learning (2022.acl-long)
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| Challenge: | a new framework for learning representations from multimodal data is proposed . the proposed framework uses discretized embedding vectors to capture finer levels of granularity . |
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Deep Generative Model for Joint Alignment and Word Representation (N18-1)
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| Challenge: | EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. |
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| Challenge: | Recent studies show that deep vision-only and language-only models project inputs into a partially aligned representational space. |
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JW-SVD: Bridging the Cross-Modal Mismatch in Post-Training MLLM Compression (2026.acl-long)
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| Challenge: | Existing methods for compression of Multimodal Large Language Models lack multimodal adaptation to preserve cross-modal synergy. |
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UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets (2025.emnlp-main)
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Pengyu Wang, Shaojun Zhou, Chenkun Tan, Xinghao Wang, Wei Huang, Zhen Ye, Zhaowei Li, Botian Jiang, Dong Zhang, Xipeng Qiu
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