AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
AttentiveGaze is presented, a multi-modal framework for robust gaze estimation that addresses critical challenges in real-world deployment, including adaptive feature fusion across heterogeneous visual cues and reliable uncertainty quantification.
Appearance-based gaze estimation faces several challenges in complex scenarios, including the loss of local eye details, insufficient use of multi-level visual cues, and limited task adaptability of general pre-trained features. To address these problems, this paper proposes MG-Gaze, a multi-level gaze-aware network fo...
Within-model ablations, which keep the encoder and the rest of the architecture fixed and vary only the gaze history, show that the differential formulation removes the systematic saccade bias that persists even with an absolute gaze-history prior.
We present MultiNet, a multi-branch pipeline that directly predicts the gaze vector or the point of gaze from multiple modalities derived from a single camera capture. In particular, we address the under-explored use of metric face-to-camera distance estimated from a conventional RGB web camera for appearance-based gaz...
Eye gaze is the fastest natural pointing signal available to interactive systems, but for three decades accurate gaze interaction has remained tied to dedicated infrared eyetracking hardware. The convergence of deep appearance-based gaze estimation, ubiquitous front-facing cameras, and on-device neural inference has op...
Egocentric gaze prediction enables many downstream applications but remains challenging, as human gaze is inherently stochastic. This stochasticity is constrained by structured temporal dynamics alternating between fixations and saccades, top-down influences from tasks, and bottom-up visual saliency. Based on this obse...
Gaze estimation has applications such as visual attention analysis and human-computer interaction. However, performance under free head movement conditions can be further enhanced. This study aims to improve the accuracy and robustness of appearance-based gaze estimation without head fixation. We develop a gaze-trackin...
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