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balanced representation space

A balanced representation space is a shared multimodal embedding space in which data from diverse modalities are mapped equitably without disproportionate bias or semantic dominance toward any single central modality. Unlike multimodal alignment frameworks that designate one modality, such as vision or text, as the primary anchor around which all other modalities are organized, a balanced representation space employs modality-agnostic alignment targets to project every input type into a neutral, harmonized coordinate system. This symmetric distribution prevents information bottlenecks and representational skew, ensuring that semantic relationships, metric distances, and transferability are maintained consistently across all represented data formats.

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UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All

UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All

Yuanhuiyi Lyu, Xu Zheng, Jiazhou Zhou, Lin Wang

OrganizationsThe Hong Kong University of Science and Technology

Why you should read this

Presents UniBind, a framework that uses large language models and multi-modal LLMs to construct modality-agnostic text embedding centers, overcoming single-modality bias to unify seven distinct sensory modalities while reducing learnable fine-tuning parameters by 90%.

We present UniBind, a flexible and efficient approach that learns a unified representation space for seven diverse modalities – image, text, audio, point cloud, thermal, video, and event data. Existing works, e.g., ImageBind [13], treat the image as the central modality and build an image-centered representation space; however, the space may be sub-optimal as it leads to an unbalanced representation space among all modalities. Moreover, the category names are directly used to extract text embeddings for the downstream tasks, making it hardly possible to represent the semantics of multi-modal data. The ‘out-of-the-box’ insight of our UniBind is to make the alignment centers modality-agnostic and further learn a unified and balanced representation space, empowered by the large language models (LLMs). UniBind is superior in its flexible application to all CLIP-style models and delivers remarkable performance boosts. To make this possible, we 1) construct a knowledge base of text with the help of LLMs and multi-modal LLMs; 2) adaptively build LLM-augmented class-wise embedding centers on top of the knowledge base and encoded visual embeddings; 3) align all the embeddings to the LLM-augmented embedding centers via contrastive learning to achieve a unified and balanced representation space. UniBind shows strong zero-shot recognition performance gains over prior arts by an average of 6.36%. Finally, we achieve new state-of-the-art performance, e.g., a 6.75% gain on ImageNet, on the multi-modal fine-tuning setting while reducing 90% of the learnable parameters.

Added

2026-09-26