SceneBind: Binding What and Where Across Vision, Audio and Language
Score
77提出全新表示解决现有方法缺少空间结构的问题，信息增量明显。
方法技术路线清晰，实验设计严谨，数据与代码可能公开，质量较高。
对 AR/VR、机器人等需要融合语义与空间的应用有直接帮助，实用性强。
2025年7月论文，紧跟多模态与大模型前沿，时效性极佳。
arXiv预印本，但来自高质量AI/CL/LG分类，且包含实验细节，可信度较高。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
This work innovatively incorporates explicit spatial structure into omni-modal representation, with solid methodology and thorough experiments, showing significant academic value.
AI commentary
「SceneBind takes a significant step in multimodal representation by unifying 'what' and 'where', enabling machines to not only recognize objects but also understand spatial relationships. This paper is particularly valuable for developers working on multimodal retrieval, embodied intelligence, and scene understanding—its lightweight design also facilitates practical deployment. I look forward to seeing its application on real robotic platforms.」
Quick overview
SceneBind introduces an omni-modal representation that captures both global semantics and 3D spatial structure of real-world scenes. Each scene is represented as a semantic-spatial entity combining a global semantic embedding with object-centric semantic-spatial slots, explicitly modeling object attributes, spatial attributes, and uncertainty. A semantic-spatial matching scheme enables cross-modal scene retrieval and object grounding. The method trains on a newly curated binaural audio-visual dataset, adds lightweight spatial tokens to pretrained encoders, achieves SOTA on scene and spatial retrieval, and demonstrates strong zero-shot transfer to tasks like audio-visual localization.
- Proposes SceneBind, an omni-modal representation fusing semantics and 3D spatial info.
- Each scene is represented by a global semantic embedding and object-centric semantic-spatial slots.
- Curates a new binaural audio-visual dataset for training and evaluation.
- Achieves SOTA on scene/spatial retrieval and zero-shot transfer to audio-visual localization.
Full text
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