10 citations · 13 across the 3 of their papers we have counts for
7 papers · 1 filter
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing
Aadarsh Sahoo, Rutav Shah, Rameswar Panda +2
Unsupervised domain adaptation which aims to adapt models trained on a labeled source domain to a completely unlabeled target domain has attracted much attention in recent years. W…
Semi-Supervised Action Recognition with Temporal Contrastive Learning
Ankit Singh, Omprakash Chakraborty, Ashutosh Varshney +4
Learning to recognize actions from only a handful of labeled videos is a challenging problem due to the scarcity of tediously collected activity labels. We approach this problem by…
Mitigating Dataset Imbalance via Joint Generation and Classification
Aadarsh Sahoo, Ankit Singh, Rameswar Panda +2
Supervised deep learning methods are enjoying enormous success in many practical applications of computer vision and have the potential to revolutionize robotics. However, the mark…
Revisiting Few-shot Activity Detection with Class Similarity Control
Huijuan Xu, Ximeng Sun, Eric Tzeng +3
Many interesting events in the real world are rare making preannotated machine learning ready videos a rarity in consequence. Thus, temporal activity detection models that are able…
Two-Stream Region Convolutional 3D Network for Temporal Activity Detection
Huijuan Xu, Abir Das, Kate Saenko
We address the problem of temporal activity detection in continuous, untrimmed video streams. This is a difficult task that requires extracting meaningful spatio-temporal features…
RISE: Randomized Input Sampling for Explanation of Black-box Models
Vitali Petsiuk, Abir Das, Kate Saenko
Deep neural networks are being used increasingly to automate data analysis and decision making, yet their decision-making process is largely unclear and is difficult to explain to…