Separating the “chirp” from the “chat”: self-supervised visual grounding of sound and language

Conference item

We present DenseAV, a novel dual encoder grounding architecture that learns high-resolution, semantically meaningful, and audio-visual aligned features solely through watching videos. We show that DenseAV can discover the “meaning” of words and the “location” of sounds without explicit localization supervision. Furthermore, it automatically discovers and distinguishes between these two types of associations without supervision. We show that DenseAV's localization abilities arise from a new multi-head feature aggregation operator that directly compares dense image and audio representations for contrastive learning. In contrast, many other systems that learn “global” audio and video representations cannot localize words and sound. Finally, we contribute two new datasets to improve the evaluation of AV representations through speech and sound prompted semantic segmentation. On these and other datasets we show DenseAV dramatically outperforms the prior art on speech and sound prompted semantic segmentation. DenseAV outperforms the current state-of-the-art, ImageBind, on cross-modal retrieval using fewer than half of the parameters. Project Page: https://aka.ms/denseav

Authors: Hamilton, M; Zisserman, A; Hershey, JR; Freeman, WT

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• Pages: 13117-13127
• Publication date: 2024-09-16
• Acceptance date: 2024-06-16
• Event title: IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR 2024)
• Event location: Seattle, Washington , USA
• Event website: https://cvpr.thecvf.com/Conferences/2024
• Event start date: 2024-06-17
• Event end date: 2024-06-21
• DOI: 10.1109/cvpr52733.2024.01246
• EISSN: 2575-7075
• ISSN: 1063-6919
• Language: English
• Keywords: FFR; semantic segmentation; location awareness; contrastive learning; visualization; grounding; computational modeling; computer vision