149 citations · 278 across the 55 of their papers we have counts for
10 papers · 1 filter
LARNet: Latent Action Representation for Human Action Synthesis
Naman Biyani, Aayush J Rana, Shruti Vyas +1
We present LARNet, a novel end-to-end approach for generating human action videos. A joint generative modeling of appearance and dynamics to synthesize a video is very challenging…
"Knights": First Place Submission for VIPriors21 Action Recognition Challenge at ICCV 2021
Ishan Dave, Naman Biyani, Brandon Clark +3
This technical report presents our approach "Knights" to solve the action recognition task on a small subset of Kinetics-400 i.e. Kinetics400ViPriors without using any extra-data.…
NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy Labels
Mohit Sharma, Raj Patra, Harshal Desai +3
Deep learning has shown remarkable progress in a wide range of problems. However, efficient training of such models requires large-scale datasets, and getting annotations for such…
Pose-guided Generative Adversarial Net for Novel View Action Synthesis
Xianhang Li, Junhao Zhang, Kunchang Li +2
We focus on the problem of novel-view human action synthesis. Given an action video, the goal is to generate the same action from an unseen viewpoint. Naturally, novel view video s…
TinyAction Challenge: Recognizing Real-world Low-resolution Activities in Videos
Praveen Tirupattur, Aayush J Rana, Tushar Sangam +3
This paper summarizes the TinyAction challenge which was organized in ActivityNet workshop at CVPR 2021. This challenge focuses on recognizing real-world low-resolution activities…
Novel View Video Prediction Using a Dual Representation
Sarah Shiraz, Krishna Regmi, Shruti Vyas +2
We address the problem of novel view video prediction; given a set of input video clips from a single/multiple views, our network is able to predict the video from a novel view. Th…
PLM: Partial Label Masking for Imbalanced Multi-label Classification
Kevin Duarte, Yogesh S. Rawat, Mubarak Shah
Neural networks trained on real-world datasets with long-tailed label distributions are biased towards frequent classes and perform poorly on infrequent classes. The imbalance in t…
Unsupervised Discriminative Embedding for Sub-Action Learning in Complex Activities
Sirnam Swetha, Hilde Kuehne, Yogesh S Rawat +1
Action recognition and detection in the context of long untrimmed video sequences has seen an increased attention from the research community. However, annotation of complex activi…
Modeling Multi-Label Action Dependencies for Temporal Action Localization
Praveen Tirupattur, Kevin Duarte, Yogesh Rawat +1
Real-world videos contain many complex actions with inherent relationships between action classes. In this work, we propose an attention-based architecture that models these action…
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat +1
The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance. However, they heavily rely o…