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…
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…
Feature Binding with Category-Dependant 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…
Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve
Sen Jia, Neil D. B. Bruce
Saliency detection has been widely studied because it plays an important role in various vision applications, but it is difficult to evaluate saliency systems because each measure…
How Much Position Information Do Convolutional Neural Networks Encode?
Md Amirul Islam, Sen Jia, Neil D. B. Bruce
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…
Distributed Iterative Gating Networks for Semantic Segmentation
Rezaul Karim, Md Amirul Islam, Neil D. B. Bruce
In this paper, we present a canonical structure for controlling information flow in neural networks with an efficient feedback routing mechanism based on a strategy of Distributed…
Richer and Deeper Supervision Network for Salient Object Detection
Sen Jia, Neil D. B. Bruce
Recent Salient Object Detection (SOD) systems are mostly based on Convolutional Neural Networks (CNNs). Specifically, Deeply Supervised Saliency (DSS) system has shown it is very u…
SMILER: Saliency Model Implementation Library for Experimental Research
Calden Wloka, Toni Kunić, Iuliia Kotseruba +4
The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintainin…
Recurrent Iterative Gating Networks for Semantic Segmentation
Rezaul Karim, Md Amirul Islam, Neil D. B. Bruce
In this paper, we present an approach for Recurrent Iterative Gating called RIGNet. The core elements of RIGNet involve recurrent connections that control the flow of information i…
Relative Saliency and Ranking: Models, Metrics, Data, and Benchmarks
Mahmoud Kalash, Md Amirul Islam, Neil D. B. Bruce
Salient object detection is a problem that has been considered in detail and \textcolor{black}{many solutions have been proposed}. In this paper, we argue that work to date has add…
Semantics Meet Saliency: Exploring Domain Affinity and Models for Dual-Task Prediction
Md Amirul Islam, Mahmoud Kalash, Neil D. B. Bruce
Much research has examined models for prediction of semantic labels or instances including dense pixel-wise prediction. The problem of predicting salient objects or regions of an i…
Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image Labeling
Md Amirul Islam, Mrigank Rochan, Shujon Naha +2
Effective integration of local and global contextual information is crucial for semantic segmentation and dense image labeling. We develop two encoder-decoder based deep learning a…
Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects
Md Amirul Islam, Mahmoud Kalash, Neil D. B. Bruce
Salient object detection is a problem that has been considered in detail and many solutions proposed. In this paper, we argue that work to date has addressed a problem that is rela…
Label Refinement Network for Coarse-to-Fine Semantic Segmentation
Md Amirul Islam, Shujon Naha, Mrigank Rochan +2
We consider the problem of semantic image segmentation using deep convolutional neural networks. We propose a novel network architecture called the label refinement network that pr…