1 code implementation • ECCV 2020 • Niamul Quader, Md Mafijul Islam Bhuiyan, Juwei Lu, Peng Dai, Wei Li
We propose novel approaches for simultaneously identifying important weights of a convolutional neural network (ConvNet) and providing more attention to the important weights during training.
no code implementations • ECCV 2020 • Niamul Quader, Juwei Lu, Peng Dai, Wei Li
State-of-the-art approaches to video-based action and gesture recognition often employ two key concepts: First, they employ multistream processing; second, they use an ensemble of convolutional networks.
Ranked #1 on Action Classification on Jester test
no code implementations • 11 Dec 2021 • Hanwen Liang, Niamul Quader, Zhixiang Chi, Lizhe Chen, Peng Dai, Juwei Lu, Yang Wang
Recent self-supervised video representation learning methods have found significant success by exploring essential properties of videos, e. g. speed, temporal order, etc.
1 code implementation • 30 Nov 2021 • Lingdong Kong, Niamul Quader, Venice Erin Liong
We present ConDA, a concatenation-based domain adaptation framework for LiDAR segmentation that: 1) constructs an intermediate domain consisting of fine-grained interchange signals from both source and target domains without destabilizing the semantic coherency of objects and background around the ego-vehicle; and 2) utilizes the intermediate domain for self-training.
no code implementations • ICCV 2021 • Deepak Sridhar, Niamul Quader, Srikanth Muralidharan, Yaoxin Li, Peng Dai, Juwei Lu
Our attention mechanism outperforms prior self-attention modules such as the squeeze-and-excitation in action detection task.