Toward Gaze Target Detection of Young Autistic Children

The automatic detection of gaze targets in autistic children through artificial intelligence can be impactful, especially for those who lack access to a sufficient number of professionals to improve their quality of life. This paper introduces a new, real-world AI application for gaze target detection in autistic children, which predicts a child's point of gaze from an activity image. This task is foundational for building automated systems that can measure joint attention-a core challenge in Autism Spectrum Disorder (ASD). To facilitate the study of this challenging application, we collected the first-ever Autism Gaze Target (AGT) dataset. We further propose a novel Socially Aware Coarse-to-Fine (SACF) gaze detection framework that explicitly leverages the social context of a scene to overcome the class imbalance common in autism datasets-a consequence of autistic children's tendency to show reduced gaze to faces. It utilizes a two-pathway architecture with expert models specialized in social and non-social gaze, guided by a context-awareness gate module. The results of our comprehensive experiments demonstrate that our framework achieves new state-of-the-art performance for gaze target detection in this population, significantly outperforming existing methods, especially on the critical minority class of face-directed gaze.

Robust Active Speaker Detection in Noisy Environments

This paper addresses the issue of active speaker detection (ASD) in noisy environments and formulates a robust active speaker detection (rASD) problem. Existing ASD approaches leverage both audio and visual modalities, but non-speech sounds in the surrounding environment can negatively impact performance. To overcome this, we propose a novel framework that utilizes audio-visual speech separation as guidance to learn noise-free audio features. These features are then utilized in an ASD model, and both tasks are jointly optimized in an end-to-end framework. Our proposed framework mitigates residual noise and audio quality reduction issues that can occur in a naive cascaded two-stage framework that directly uses separated speech for ASD, and enables the two tasks to be optimized simultaneously. To further enhance the robustness of the audio features and handle inherent speech noises, we propose a dynamic weighted loss approach to train the speech separator. We also collected a real-world noise audio dataset to facilitate investigations. Experiments demonstrate that non-speech audio noises significantly impact ASD models, and our proposed approach improves ASD performance in noisy environments. The framework is general and can be applied to different ASD approaches to improve their robustness. Our code, models, and data will be released.