SOUPLE: Enhancing Audio-Visual Localization and Segmentation with Learnable Prompt Contexts

• URL: http://arxiv.org/abs/2603.22732v1
• Date: Tue, 24 Mar 2026 02:56:30 GMT
• Title: SOUPLE: Enhancing Audio-Visual Localization and Segmentation with Learnable Prompt Contexts
• Abstract summary: We propose Sound-aware Prompt Learning (SOUPLE) to bridge semantic correspondence between audio and visual inputs.<n> Experiments on VGGSound, SoundNet, and AVSBench demonstrate that SOUPLE improves localization and segmentation performance.
• Score: 1.2891210250935148
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large-scale pre-trained image-text models exhibit robust multimodal representations, yet applying the Contrastive Language-Image Pre-training (CLIP) model to audio-visual localization remains challenging. Replacing the classification token ([CLS]) with an audio-embedded token ([V_A]) struggles to capture semantic cues, and the prompt "a photo of a [V_A]" fails to establish meaningful connections between audio embeddings and context tokens. To address these issues, we propose Sound-aware Prompt Learning (SOUPLE), which replaces fixed prompts with learnable context tokens. These tokens incorporate visual features to generate conditional context for a mask decoder, effectively bridging semantic correspondence between audio and visual inputs. Experiments on VGGSound, SoundNet, and AVSBench demonstrate that SOUPLE improves localization and segmentation performance.

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