Neil Bruce
- Deep Learning
- Computer Vision
- Machine Learning
- Artificial Intelligence
- Computational Neuroscience
- Brain Computer Interfaces
My research addresses problems in computer vision, deep-learning, human perception, neuroscience and visual computing. A common theme across these projects is the application of machine learning and data science to understand and draw inferences from complex data. This serves to produce a stronger understanding of the basic science underlying different phenomena, and to help engineer intelligent and/or application specific solutions to problems.
Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve
S Jia, NDB Bruce, In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
EML-net: An expandable multi-layer network for saliency prediction
S Jia, NDB Bruce, Image and Vision Computing, 103887, 2020
How much Position Information Do Convolutional Neural Networks Encode?
MA Islam, S Jia, NDB Bruce
International Conference on Learning Representations (ICLR), 2020
Distributed Iterative Gating Networks for Semantic Segmentation
R Karim, MA Islam, NDB Bruce
The IEEE Winter Conference on Applications of Computer Vision (WACV) , 2844-2853, 2020
In-depth Evaluation and Experimental Analysis of a Weight Pruning Genetic Algorithm
S Janjic, P Thulasiraman, N Bruce
2019 IEEE Congress on Evolutionary Computation (CEC), 1814-1821, 2019
Recurrent Iterative Gating Networks for Semantic Segmentation
R Karim, MA Islam, NDB Bruce
2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 1070-1079, 2019
Relative Saliency and Ranking: Models, Metrics, Data, and Benchmarks
M Kalash, MA Islam, NDB Bruce
In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2018
Redundancy in convolutional neural networks: Insights on model compression and structure
S Janjic, P Thulasiraman, N Bruce
2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018
Capturing real-world gaze behaviour: live and unplugged
K Singh, M Kalash, N Bruce
Proceedings of the 2018 ACM Symposium on Eye Tracking Research and Applications (ETRA), 2018
Revisiting salient object detection: Simultaneous detection, ranking, and subitizing of multiple salient objects
M Amirul Islam, M Kalash, NDB Bruce
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 29, 2018
Salient Object Detection using a Context-Aware Refinement Network
MA Islam, M Kalash, M Rochan, N Bruce, Y Wang
2017 British Machine Vision Conference (BMVC 2017), 2017
Gated feedback refinement network for dense image labeling
M Amirul Islam, M Rochan, NDB Bruce, Y Wang
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Predicting task from eye movements: On the importance of spatial distribution, dynamics, and image features
JFG Boisvert, NDB Bruce
Neurocomputing 207, 653-668, 2016
A deeper look at saliency: Feature contrast, semantics, and beyond
NDB Bruce, C Catton, S Janjic
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
Saliency, Scale and Information: Towards a Unifying Theory
S Rahman, N Bruce
Advances in Neural Information Processing Systems (NIPS 2015), 2179-2187, 2016
Sparse coding in early visual representation: From specific properties to general principles
NDB Bruce, S Rahman, D Carrier
Neurocomputing 171, 1085-1098, 2016
On computational modeling of visual saliency: Examining what’s right, and what’s left
NDB Bruce, C Wloka, N Frosst, S Rahman, JK Tsotsos
Vision research 116, 95-112, 2015
Expoblend: Information preserving exposure blending based on normalized log-domain entropy
NDB Bruce, Computers & Graphics 39, 12-23, 2014
Saliency, attention, and visual search: An information theoretic approach
NDB Bruce, JK Tsotsos
Journal of vision 9 (3), 5-5, 2009
Saliency based on information maximization
N Bruce, J Tsotsos
Advances in neural information processing systems (NIPS), 155-162, 2006