Big Data versus the Crowd: Looking for Relationships in All the Right Places

Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words?

Motivated by human attention, computational attention mechanisms have been designed to help neural networks adjust their focus on specific parts of the input data. While attention mechanisms are claimed to achieve interp…

Crowd3D++: Robust Monocular Crowd Reconstruction with Upright Space

This paper aims to reconstruct hundreds of people's 3D poses, shapes, and locations from a single image with unknown camera parameters. Due to the small and highly varying 2D human scales, depth ambiguity, and perspectiv…

Human versus Machine Attention in Document Classification: A Dataset with Crowdsourced Annotations

Looking Is Not Picking: An Attention-Segment Account of Tool-Selection Failures in LLM Agents

LLM agents mis-call tools, and the natural guess is that the model failed to see the right tool in a crowded harness. We show the opposite through a lens concurrent work sets aside -- the model's attention to labeled too…

Human Attention Detection Using AM-FM Representations

Human activity detection from digital videos presents many challenges to the computer vision and image processing communities. Recently, many methods have been developed to detect human activities with varying degree of …