94 citations · 261 across the 15 of their papers we have counts for
19 papers
Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks
Matthew Kowal, Mennatullah Siam, Md Amirul Islam +3
There is limited understanding of the information captured by deep spatiotemporal models in their intermediate representations. For example, while evidence suggests that action rec…
A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information
Matthew Kowal, Mennatullah Siam, Md Amirul Islam +3
Deep spatiotemporal models are used in a variety of computer vision tasks, such as action recognition and video object segmentation. Currently, there is a limited understanding of…
Simpler Does It: Generating Semantic Labels with Objectness Guidance
Md Amirul Islam, Matthew Kowal, Sen Jia +2
Existing weakly or semi-supervised semantic segmentation methods utilize image or box-level supervision to generate pseudo-labels for weakly labeled images. However, due to the lac…
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness
Md Amirul Islam, Matthew Kowal, Konstantinos G. Derpanis +1
In this paper, we present a strategy for training convolutional neural networks to effectively resolve interference arising from competing hypotheses relating to inter-categorical…
Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs
Md Amirul Islam, Matthew Kowal, Sen Jia +2
In this paper, we challenge the common assumption that collapsing the spatial dimensions of a 3D (spatial-channel) tensor in a convolutional neural network (CNN) into a vector via…
Position, Padding and Predictions: A Deeper Look at Position Information in CNNs
Md Amirul Islam, Matthew Kowal, Sen Jia +2
In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent. An…