1 code implementation • 21 Dec 2023 • Bardia Safaei, Vibashan VS, Celso M. de Melo, Vishal M. Patel
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.
no code implementations • 5 Dec 2023 • Arun Reddy, William Paul, Corban Rivera, Ketul Shah, Celso M. de Melo, Rama Chellappa
In this work, we tackle the problem of unsupervised domain adaptation (UDA) for video action recognition.
1 code implementation • 4 Dec 2023 • Wele Gedara Chaminda Bandara, Celso M. de Melo, Vishal M. Patel
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
1 code implementation • CVPR 2023 • Xiaoyu Zhu, Po-Yao Huang, Junwei Liang, Celso M. de Melo, Alexander Hauptmann
The model uses a hierarchical transformer with intra-frame off-set attention and inter-frame self-attention.
1 code implementation • 17 Mar 2023 • Arun V. Reddy, Ketul Shah, William Paul, Rohita Mocharla, Judy Hoffman, Kapil D. Katyal, Dinesh Manocha, Celso M. de Melo, Rama Chellappa
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.
no code implementations • 2 Mar 2023 • Xijun Wang, Ruiqi Xian, Tianrui Guan, Celso M. de Melo, Stephen M. Nogar, Aniket Bera, Dinesh Manocha
We propose a novel approach for aerial video action recognition.
Ranked #1 on Action Recognition on RoCoG-v2
1 code implementation • IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 • Ketul Shah, Anshul Shah, Chun Pong Lau, Celso M. de Melo, Rama Chellappa
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
1 code implementation • 10 Nov 2022 • Bardia Safaei, Vibashan VS, Celso M. de Melo, Shuowen Hu, Vishal M. Patel
Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors.