Learning Sight from Sound: Ambient Sound Provides Supervision for Visual Learning (1712.07271v1)

Abstract: The sound of crashing waves, the roar of fast-moving cars -- sound conveys important information about the objects in our surroundings. In this work, we show that ambient sounds can be used as a supervisory signal for learning visual models. To demonstrate this, we train a convolutional neural network to predict a statistical summary of the sound associated with a video frame. We show that, through this process, the network learns a representation that conveys information about objects and scenes. We evaluate this representation on several recognition tasks, finding that its performance is comparable to that of other state-of-the-art unsupervised learning methods. Finally, we show through visualizations that the network learns units that are selective to objects that are often associated with characteristic sounds. This paper extends an earlier conference paper, Owens et al. 2016, with additional experiments and discussion.

• The paper proposes using ambient sound as a novel supervisory signal to train visual recognition models without explicit labels.
• Researchers trained a convolutional neural network to predict sound statistics from visual data, effectively learning visual representations based on auditory cues.
• Features learned from sound-based supervision perform competitively on visual tasks and suggest ambient sound is a valuable, cost-effective resource for unsupervised learning.

Learning Sight from Sound: Utilizing Ambient Sound for Visual Learning

The paper "Learning Sight from Sound: Ambient Sound Provides Supervision for Visual Learning" presents a novel approach to visual model training by exploiting ambient sound as a supervisory signal. It posits that auditory information, such as the sound of waves or the noise of a busy street, inherently carries rich data about the visual environment and can be harnessed to improve unsupervised learning techniques in visual recognition tasks.

Methodology

At the core of this paper is a convolutional neural network (CNN) architecture designed to predict statistical summaries of sound captured alongside video frames. The network is trained to correlate visual data with corresponding audio data, effectively learning representations of objects and scenes without explicit labels. The audio signal serves as a natural complement to visual information, leveraging the semantic and environmental cues embedded in sound.

The sound-prediction task was formulated as a classification problem. Researchers defined sound categories using clustering methods applied to sound textures derived from video clips. This sound-based supervision contrasts sharply with common approaches relying heavily on human-generated labels.

Experimental Evaluation

The representation learned by the CNN was examined through object and scene recognition tasks. Results demonstrate that features trained on ambient sound perform competitively with existing unsupervised and self-supervised learning methods, even reaching comparable levels with current state-of-the-art techniques. In particular, the features generated from sound-based supervision showed notable selectivity for objects frequently associated with distinctive sounds, such as vehicles, people, and natural elements.

Visualization and Analysis

The paper employed visualization strategies to offer insights into the network's learning process. Object-selective units emerged in the CNN's internal layers, echoing findings from supervised methods where scene recognition tasks prompt latent object detectors. These visualizations affirm the hypothesis that sound-supervised models can autonomously develop an understanding of visual semantics.

Implications and Future Directions

The paper's findings suggest ambient sound as a potent tool for visual representation learning, providing a ubiquitous and cost-effective resource compared to human annotations. This approach opens theoretical discussions about multimodal learning systems where vision and sound unite to mimic human-like perception.

Practically, the integration of sound with visual models could enhance applications in autonomous systems, improve audio-visual synchronization, and contribute to more robust environmental perception technology. Moreover, there exists potential for such sound-based models to inform the development of self-supervised learning frameworks, particularly in domains demanding invariant recognition solutions amidst visual transformations.

Conclusion

The research delineates a compelling case for ambient sound as a supervisory signal in visual learning paradigms. By exploring the natural alignment between visual images and their ambient sound, this work pioneers a path toward enriched unsupervised learning techniques that are more aligned with the multimodal ways humans perceive and interpret their surroundings. As AI continues to evolve, incorporating a broader spectrum of sensory inputs could lead to more sophisticated and intuitive systems, thereby pushing the boundaries of artificial perception and cognitive modeling.