94 citations · 261 across the 15 of their papers we have counts for
5 papers · 2 filters
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…
Shape or Texture: Understanding Discriminative Features in CNNs
Md Amirul Islam, Matthew Kowal, Patrick Esser +4
Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs act…