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Source note3 min read

SceneBind: Binding What and Where Across Vision, Audio and Language

Source

arXiv

arxiv.org/abs/2607.15265v1

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Source

Plain-Language Brief

The researchers introduce SceneBind, a system that combines visual, auditory, and linguistic data to create a detailed representation of realistic scenes with both semantic (what is present) and spatial (where it is) understanding. The system addresses the issue of lacking explicit spatial structure in existing omni-modal encoders by representing each scene as a semantic-spatial entity, which includes global semantic embedding and object-centric semantic-spatial slots. SceneBind Matching, a matching scheme, is also proposed to support cross-modal scene retrieval and object grounding. The system uses a novel real-world binaural audio-visual dataset for training and evaluation.

What the Document Says

  • The authors introduce SceneBind, an omni-modal representation of realistic scenes with joint semantic and 3D spatial understanding across vision, audio, and language.
  • SceneBind addresses the gap in existing omni-modal encoders that lack explicit spatial structure by representing each scene as a semantic-spatial entity.
  • Each scene is represented by a global semantic embedding and object-centric semantic-spatial slots, which capture object-level semantics, spatial attributes, and uncertainty.
  • SceneBind Matching is proposed for semantic-spatial matching, integrating global scene similarity with object alignment.
  • The system supports cross-modal scene retrieval and object grounding.
  • A novel real-world binaural audio-visual dataset with structured semantic and spatial annotations is used for training and evaluation of SceneBind.
  • SceneBind is compatible with large-scale pretrained semantic encoders and adds lightweight spatial modeling with only a few additional tokens.

Key Concepts Mentioned

  • SceneBind
  • omni-modal representation
  • semantic and spatial understanding
  • binaural audio-visual dataset
  • SceneBind Matching

Claims Made by the Document

  • SceneBind achieves state-of-the-art scene and spatial retrieval.
  • SceneBind enables strong zero-shot transfer to downstream tasks such as audio-visual localization.

Evidence Used

  • The system's performance is not directly stated in the text, but it is claimed that SceneBind achieves state-of-the-art scene and spatial retrieval while enabling strong zero-shot transfer to downstream tasks.

Methods / Approach

  • Training and evaluation of SceneBind using a novel real-world binaural audio-visual dataset.
  • Proposal of SceneBind Matching for semantic-spatial matching.

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The text does not provide information on potential limitations of the system as pointed out by the agent.

Open Questions

  • What is the specific method used for training and evaluating SceneBind?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text provides a high-level overview of the system and its capabilities, but lacks detailed information on the specific methods used for training and evaluation.

Raw Abstract / Excerpt

We present SceneBind, an omni-modal representation of realistic scenes with joint semantic and 3D spatial understanding across vision, audio and language. Existing omni-modal encoders excel at instance-level semantics (i.e., what is present), but often lack explicit spatial structure (i.e., where it is). SceneBind addresses this gap by representing each scene as a semantic-spatial entity, combining a global semantic embedding with object-centric semantic-spatial slots. This representation explicitly captures object-level semantics, spatial attributes, and uncertainty. We further propose SceneBind Matching, a semantic-spatial matching scheme that integrates global scene similarity with object alignment, supporting cross-modal scene retrieval and object grounding. To train and evaluate SceneBind, we curate a novel real-world binaural audio-visual dataset with structured semantic and spatial annotations, and propose a training protocol for aligning semantic and spatial signals across modalities. SceneBind is compatible with large-scale pretrained semantic encoders, adds lightweight spatial modeling with only a few additional tokens. It achieves state-of-the-art scene and spatial retrieval while enabling strong zero-shot transfer to downstream tasks such as audio-visual localization.