# SCoPE: Training-Free Audio-Visual Event Perception via Sparse Cross-Modal Prior Exchange

arXiv: https://arxiv.org/abs/2608.07923 · HTML: https://mlsanity.com/paper/2608.07923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning](https://mlsanity.com/paper/2203.02053/) (2022)
- [LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment](https://mlsanity.com/paper/2310.01852/) (2023)
- [All-but-the-Top: Simple and Effective Postprocessing for Word Representations](https://mlsanity.com/paper/1702.01417/) (2017)
- [Unified Multisensory Perception: Weakly-Supervised Audio-Visual Video Parsing](https://mlsanity.com/paper/2007.10558/) (2020)
- [Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)](https://mlsanity.com/paper/2402.10376/) (2024)
- [Semantic Audio-Visual Navigation](https://mlsanity.com/paper/2012.11583/) (2020)
- [Self-supervised Moving Vehicle Tracking with Stereo Sound](https://mlsanity.com/paper/1910.11760/) (2019)
- [Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing](https://mlsanity.com/paper/2204.11573/) (2022)
- [Towards Open-Vocabulary Audio-Visual Event Localization](https://mlsanity.com/paper/2411.11278/) (2024)
- [Label-anticipated Event Disentanglement for Audio-Visual Video Parsing](https://mlsanity.com/paper/2407.08126/) (2024)
- [Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser](https://mlsanity.com/paper/2305.17343/) (2023)
- [Revisit Weakly-Supervised Audio-Visual Video Parsing from the Language Perspective](https://mlsanity.com/paper/2306.00595/) (2023)
- [Transformation of audio embeddings into interpretable, concept-based representations](https://mlsanity.com/paper/2504.14076/) (2025)
- [PreFM: Online Audio-Visual Event Parsing via Predictive Future Modeling](https://mlsanity.com/paper/2505.23155/) (2025)
- [Adapting to the Unknown: Training-Free Audio-Visual Event Perception with Dynamic Thresholds](https://mlsanity.com/paper/2503.13693/) (2025)
- [Hierarchical Semantic-Constrained Heterogeneous Graph for Audio-Visual Event Localization](https://mlsanity.com/paper/2606.07033/) (2026)

## Abstract (arXiv)

Audio-visual event perception (AVEP) determines which events occur in a video, when they occur, and whether they are audible, visible, or both. Training-free methods query new event vocabularies by matching frozen audio and visual features with text-encoded event names. However, related labels share evidence. An incorrect label can then score at least as high as a correct one. We call this a false co-activation (FCA). No scalar cutoff can reject the incorrect label while keeping every correct one. Class-specific thresholds may prevent that label from becoming a final prediction, but the FCA remains in the underlying score vector. We introduce SCoPE, a training-free framework in which all queried labels compete for shared evidence and each modality guides event selection in the other. We derive an exact condition for when this competition removes an FCA in a two-label fit. With identical frozen CLIP+CLAP backbones on LLP, SCoPE improves Type@seg by 7.45 points and Event@seg by 5.04 points compared with the reported AV2A values. The same fixed configuration transfers unchanged to OV-AVEBench and VGGSound-AVEL100k.