1 code implementation • 21 Apr 2024 • Abhishek Jha, Yogesh Rawat, Shruti Vyas
We propose PV-S3 (Photovoltaic-Semi Supervised Segmentation), a Semi-Supervised Learning approach for semantic segmentation of defects in EL images that reduces reliance on extensive labeling.
1 code implementation • 7 Apr 2023 • Madeline Schiappa, Raiyaan Abdullah, Shehreen Azad, Jared Claypoole, Michael Cogswell, Ajay Divakaran, Yogesh Rawat
In this work we focus on conceptual understanding of these large V+L models.
1 code implementation • 4 Jul 2022 • Madeline Chantry Schiappa, Naman Biyani, Prudvi Kamtam, Shruti Vyas, Hamid Palangi, Vibhav Vineet, Yogesh Rawat
In this work, we perform a large-scale robustness analysis of these existing models for video action recognition.
no code implementations • NeurIPS 2021 • Alec Kerrigan, Kevin Duarte, Yogesh Rawat, Mubarak Shah
Given a video and a set of action classes, our method predicts a set of confidence scores for each class independently.
no code implementations • 14 Oct 2021 • Ishan Dave, Naman Biyani, Brandon Clark, Rohit Gupta, Yogesh Rawat, Mubarak Shah
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.
1 code implementation • 13 Oct 2021 • Mohit Sharma, Raj Patra, Harshal Desai, Shruti Vyas, Yogesh Rawat, Rajiv Ratn Shah
We present this as a benchmark dataset in noisy learning for video understanding.
1 code implementation • CVPR 2021 • Praveen Tirupattur, Kevin Duarte, Yogesh Rawat, Mubarak Shah
We propose to improve action localization performance by modeling these action dependencies in a novel attention-based Multi-Label Action Dependency (MLAD)layer.
Ranked #1 on
Action Detection
on Multi-THUMOS
1 code implementation • 1 Apr 2020 • Erik Quintanilla, Yogesh Rawat, Andrey Sakryukin, Mubarak Shah, Mohan Kankanhalli
We demonstrate the effectiveness of the proposed model on two different large-scale and publicly available datasets, YFCC100M and NUS-WIDE.