172 citations · 270 across the 34 of their papers we have counts for
9 papers · 1 filter
Localization in the Crowd with Topological Constraints
Shahira Abousamra, Minh Hoai, Dimitris Samaras +1
We address the problem of crowd localization, i.e., the prediction of dots corresponding to people in a crowded scene. Due to various challenges, a localization method is prone to…
Structural and Functional Decomposition for Personality Image Captioning in a Communication Game
Thu Nguyen, Duy Phung, Minh Hoai +1
Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait. In this work, we introduce a novel formulation for PIC based…
Distribution Matching for Crowd Counting
Boyu Wang, Huidong Liu, Dimitris Samaras +1
In crowd counting, each training image contains multiple people, where each person is annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth each anno…
Detecting Hands and Recognizing Physical Contact in the Wild
Supreeth Narasimhaswamy, Trung Nguyen, Minh Hoai
We investigate a new problem of detecting hands and recognizing their physical contact state in unconstrained conditions. This is a challenging inference task given the need to rea…
Uncertainty Estimation and Sample Selection for Crowd Counting
Viresh Ranjan, Boyu Wang, Mubarak Shah +1
We present a method for image-based crowd counting, one that can predict a crowd density map together with the uncertainty values pertaining to the predicted density map. To obtain…
A Study of Human Gaze Behavior During Visual Crowd Counting
Raji Annadi, Yupei Chen, Viresh Ranjan +3
In this paper, we describe our study on how humans allocate their attention during visual crowd counting. Using an eye tracker, we collect gaze behavior of human participants who a…
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images
Xinyi Huang, Suphanut Jamonnak, Ye Zhao +4
Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However…
Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning
Zhibo Yang, Lihan Huang, Yupei Chen +5
Being able to predict human gaze behavior has obvious importance for behavioral vision and for computer vision applications. Most models have mainly focused on predicting free-view…
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning
Gregory J. Zelinsky, Yupei Chen, Seoyoung Ahn +5
Understanding how goal states control behavior is a question ripe for interrogation by new methods from machine learning. These methods require large and labeled datasets to train…