Neil D. B. Bruce — accepted AI-conference papers

2022

A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information

Matthew Kowal, Mennatullah Siam, Md Amirul Islam, Neil D. B. Bruce, Richard P. Wildes, Konstantinos G. Derpanis

CVPR 2022poster

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 what information is captured by these models in their intermediate representations. For example, while it has been obser…

Cited by 22 PDF ↗code ↗Scholar ↗

2021

Global Pooling, More Than Meets the Eye: Position Information Is Encoded Channel-Wise in CNNs

Md Amirul Islam, Matthew Kowal, Sen Jia, Konstantinos G. Derpanis, Neil D. B. Bruce

ICCV 2021poster

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 global pooling removes all spatial information. Specifically, we demonstrate that positional information is encoded base…

Cited by 46 PDF ↗code ↗Scholar ↗

2020

How much Position Information Do Convolutional Neural Networks Encode?

Md Amirul Islam*, Sen Jia*, Neil D. B. Bruce

ICLR 2020spotlight

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 implication of this is that a filter may know what it is looking at, but not where it is positioned in the image. Inform…

Cited by 462 Source ↗Scholar ↗

2020

Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve

Sen Jia, Neil D. B. Bruce

CVPR 2020oral

In this paper, we propose a new metric to address the long-standing problem of center bias in saliency evaluation. We first show that distribution-based metrics cannot measure saliency performance across datasets due to ambiguity in the choice of standard deviation, especially for Convolutional Neur…

Cited by 6 PDF ↗Scholar ↗

2018

Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects

Md Amirul Islam, Mahmoud Kalash, Neil D. B. Bruce

CVPR 2018poster

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 relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple o…

Cited by 149 Source ↗PDF ↗Scholar ↗

2017

Gated Feedback Refinement Network for Dense Image Labeling

Md Amirul Islam, Mrigank Rochan, Neil D. B. Bruce, Yang Wang

CVPR 2017poster

Effective integration of local and global contextual information is crucial for dense labeling problems. Most existing methods based on an encoder-decoder architecture simply concatenate features from earlier layers to obtain higher-frequency details in the refinement stages. However, there are limi…

Cited by 286 PDF ↗Scholar ↗

2016

A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond

Neil D. B. Bruce, Christopher Catton, Sasa Janjic

CVPR 2016poster

In this paper we consider the problem of visual saliency modeling, including both human gaze prediction and salient object segmentation. The overarching goal of the paper is to identify high level considerations relevant to deriving more sophisticated visual saliency models. A deep learning model ba…

Cited by 75 PDF ↗Scholar ↗