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Current multi-view gaze estimation remains limited by existing datasets, insufficient exploitation of complementary cross-view information, and evaluation focused primarily on average gaze error. We address these limitations through a more systematic study of multi-view gaze estimation. First, we introduce PrismGaze, a...
Downstream RLHF experiments additionally show that using DRM as the training-time reward leads to improved policy performance, directly validating the practical benefit of diffusion-based reward modeling for RLHF training.
This work explores laughter as a naturally occurring, sparse index for constructing contextualized personal records to support later reconstruction and reflection and inform self-tracking designs that use sparse affective indices to organize contextual records for reconstruction and reflection.
In human-LLM content co-generation, AI-generated information can enter final artifacts without being adequately processed by users, creating risks when artifacts are shared or acted upon. We study whether recognition-level uptake of atomic information units can be assessed in open-ended co-generation and predicted from...
The architectural improvements and novel training recipe allow PaGE to achieve state-of-the-art performance on several gaze estimation tasks, outperforming humans in 7 out of 9 metrics while reducing the human-AI gap by at least 60% in the remaining 2.
U-Lens improves verification efficiency and effort allocation, reduced perceived workload, and strengthened support across all three stages of uncertainty management, and reframes uncertainty support for generative AI from text-centered cues to a user-centered process of interpreting, evaluating, and acting on uncertai...
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