Cross-modal Supervision for Learning Active Speaker Detection in Video (1603.08907v1)

Abstract: In this paper, we show how to use audio to supervise the learning of active speaker detection in video. Voice Activity Detection (VAD) guides the learning of the vision-based classifier in a weakly supervised manner. The classifier uses spatio-temporal features to encode upper body motion - facial expressions and gesticulations associated with speaking. We further improve a generic model for active speaker detection by learning person specific models. Finally, we demonstrate the online adaptation of generic models learnt on one dataset, to previously unseen people in a new dataset, again using audio (VAD) for weak supervision. The use of temporal continuity overcomes the lack of clean training data. We are the first to present an active speaker detection system that learns on one audio-visual dataset and automatically adapts to speakers in a new dataset. This work can be seen as an example of how the availability of multi-modal data allows us to learn a model without the need for supervision, by transferring knowledge from one modality to another.

• The paper introduces a novel framework using VAD to weakly supervise video-based active speaker detection.
• It employs structured output prediction with latent SVMs to accurately identify speaker regions despite noisy labels.
• Experimental results demonstrate AUC performance similar to fully supervised methods with enhanced person-specific adaptation.

Cross-modal Supervision for Learning Active Speaker Detection in Video: An Essay

The paper presented by Chakravarty and Tuytelaars provides a systematic exploration into the domain of active speaker detection in video through cross-modal supervision using audio data. The research demonstrates an innovative approach in leveraging Voice Activity Detection (VAD) to facilitate a weakly supervised learning environment for the development of vision-based classifiers. This novel framework supports the tasks of identifying an active speaker, adapting to new datasets, and improving model effectiveness through person-specific adjustments.

Key Concepts and Methodologies

Active speaker detection is a multifaceted problem involving both audio and visual data. Traditionally, the focus has been on utilizing lip motion detection, but this research expands the horizon by incorporating facial expressions and gesticulations from the upper body to augment detection accuracy. The core methodology revolves around utilizing audio-based supervision to guide the training of a video classifier, a task executed without the dependency on direct directional audio information.

The paper efficiently utilizes structured output prediction via latent SVMs, allowing the model to iteratively determine which bounding boxes in a video correspond to an active speaker, amidst weak supervision from VAD alerts. This innovative approach reduces the reliance on clean and perfectly labeled datasets, thus opening the possibility for practical applications in environments where clean training data is scarce.

Numerical Results and Claims

The robustness of the model is emphasized through comparisons of average Area Under the Curve (AUC) scores derived from experiments within the Masters dataset and the Columbia dataset. It is significant to note that the weakly supervised condition using VAD provided AUC outcomes comparable to fully supervised methods, indicating the method's reliability. Further validation is seen through experiments demonstrating improvements in speaker-specific models, with notable enhancements in performance when temporal continuity weighting is applied.

Implications and Future Directions

The findings presented have substantial implications for advancements in AI-driven video conferencing technologies, human-computer interaction, and multimedia retrieval systems. By enabling online adaptation of generic models for new participants using person-specific classifiers, the research positions itself as a significant stepping stone towards optimizing real-time video communication processes.

Future explorations are anticipated in integrating multi-modal feedback loops where video classifiers can, in turn, refine audio-based recognition systems. The paper indicates a promising trajectory towards achieving adaptive learning capabilities suited for unstructured data in complex video environments. This inter-modality exchange has potential for further enhancing active speaker models within contexts such as media broadcast analysis.

The detailed methodologies, combined with rigorous evaluations, underscore the viability of cross-modal supervision in active speaker detection, delineating a pivotal advancement in the field of automated video understanding systems.