← 研踪 PaperTrailCVF · CVPR 2026:cvf:d7082910ae808a6860d7cc5dbfdee74c · 记录 #1
SOUPLE: Enhancing Audio-Visual Localization and Segmentation with Learnable Prompt Contexts
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:10
摘要
Large-scale pre-trained image-text models exhibit robust multimodal representation, yet applying contrastive language-image pretraining (CLIP) 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 confirm that SouPLe significantly improves localization and segmentation performance.
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记录 #1 · 2026 · 最新观测版本
SOUPLE: Enhancing Audio-Visual Localization and Segmentation with Learnable Prompt Contexts
Large-scale pre-trained image-text models exhibit robust multimodal representation, yet applying contrastive language-image pretraining (CLIP) 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 confirm that SouPLe significantly improves localization and segmentation performance.
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