most citedPose Embeddings: A Deep Architecture for Learning to Match Human Poses

24 citations · 49 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2016

Hierarchical Deep Temporal Models for Group Activity Recognition

Mostafa S. Ibrahim, Srikanth Muralidharan, Zhiwei Deng +2

In this paper we present an approach for classifying the activity performed by a group of people in a video sequence. This problem of group activity recognition can be addressed by…

cs.CV2016

Deep Learning of Appearance Models for Online Object Tracking

Mengyao Zhai, Mehrsan Javan Roshtkhari, Greg Mori

This paper introduces a novel deep learning based approach for vision based single target tracking. We address this problem by proposing a network architecture which takes the inpu…

cs.CV201524 cited

Pose Embeddings: A Deep Architecture for Learning to Match Human Poses

Greg Mori, Caroline Pantofaru, Nisarg Kothari +4

We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human…

cs.CV201518 cited

Deep Structured Models For Group Activity Recognition

Zhiwei Deng, Mengyao Zhai, Lei Chen +4

This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes. Deep networks are used to recogni…

cs.CV20153 cited

Learning Temporal Embeddings for Complex Video Analysis

Vignesh Ramanathan, Kevin Tang, Greg Mori +1

In this paper, we propose to learn temporal embeddings of video frames for complex video analysis. Large quantities of unlabeled video data can be easily obtained from the Internet…

cs.CV20152 cited

Discovering Human Interactions in Videos with Limited Data Labeling

Mehran Khodabandeh, Arash Vahdat, Guang-Tong Zhou +4

We present a novel approach for discovering human interactions in videos. Activity understanding techniques usually require a large number of labeled examples, which are not availa…