Localizing Visual Sounds the Hard Way
This work addresses the challenge of audio-visual localization for applications like robotics and surveillance, but it is incremental as it builds on existing contrastive learning methods with a novel hard sample mining mechanism.
The paper tackles the problem of localizing visible sound sources in videos without manual annotations by training a network to discriminate challenging image fragments, achieving state-of-the-art performance on the Flickr SoundNet dataset and a new VGG-SS benchmark.
The objective of this work is to localize sound sources that are visible in a video without using manual annotations. Our key technical contribution is to show that, by training the network to explicitly discriminate challenging image fragments, even for images that do contain the object emitting the sound, we can significantly boost the localization performance. We do so elegantly by introducing a mechanism to mine hard samples and add them to a contrastive learning formulation automatically. We show that our algorithm achieves state-of-the-art performance on the popular Flickr SoundNet dataset. Furthermore, we introduce the VGG-Sound Source (VGG-SS) benchmark, a new set of annotations for the recently-introduced VGG-Sound dataset, where the sound sources visible in each video clip are explicitly marked with bounding box annotations. This dataset is 20 times larger than analogous existing ones, contains 5K videos spanning over 200 categories, and, differently from Flickr SoundNet, is video-based. On VGG-SS, we also show that our algorithm achieves state-of-the-art performance against several baselines.