Search Results for author:
Found 8 papers, 6 papers with code
Entropic Open-set Active Learning
Active Learning (AL) aims to enhance the performance of deep models by selecting the most informative samples for annotation from a pool of unlabeled data.
Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training
In this work, we tackle the problem of unsupervised domain adaptation (UDA) for video action recognition.
Guarding Barlow Twins Against Overfitting with Mixed Samples
Self-supervised Learning (SSL) aims to learn transferable feature representations for downstream applications without relying on labeled data.
Ranked #1 on
Self-Supervised Learning
on STL-10
STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition
The model uses a hierarchical transformer with intra-frame off-set attention and inter-frame self-attention.
Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances
The dataset is composed of both real and synthetic videos from seven gesture classes, and is intended to support the study of synthetic-to-real domain shift for video-based action recognition.
AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning
We propose a novel approach for aerial video action recognition.
Ranked #1 on
Action Recognition
on RoCoG-v2
Multi-View Action Recognition Using Contrastive Learning
We present a supervised contrastive learning framework to learn a feature embedding robust to changes in viewpoint, by effectively leveraging multi-view data.
Ranked #11 on
Action Recognition
on NTU RGB+D 120
Open-Set Automatic Target Recognition
Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors.
