Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Bruce, Neil D. B."'
Autor:
Kowal, Matthew, Siam, Mennatullah, Islam, Md Amirul, Bruce, Neil D. B., Wildes, Richard P., Derpanis, Konstantinos G.
There is limited understanding of the information captured by deep spatiotemporal models in their intermediate representations. For example, while evidence suggests that action recognition algorithms are heavily influenced by visual appearance in sin
Externí odkaz:
http://arxiv.org/abs/2211.01783
Autor:
Kowal, Matthew, Siam, Mennatullah, Islam, Md Amirul, Bruce, Neil D. B., Wildes, Richard P., Derpanis, Konstantinos G.
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 rep
Externí odkaz:
http://arxiv.org/abs/2206.02846
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 lack of strong supervision, the generated pseudo-labels are often noisy n
Externí odkaz:
http://arxiv.org/abs/2110.10335
In this paper, we present a strategy for training convolutional neural networks to effectively resolve interference arising from competing hypotheses relating to inter-categorical information throughout the network. The premise is based on the notion
Externí odkaz:
http://arxiv.org/abs/2108.09929
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 demon
Externí odkaz:
http://arxiv.org/abs/2108.07884
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, b
Externí odkaz:
http://arxiv.org/abs/2101.12322
In this paper, we present a strategy for training convolutional neural networks to effectively resolve interference arising from competing hypotheses relating to inter-categorical information throughout the network. The premise is based on the notion
Externí odkaz:
http://arxiv.org/abs/2008.05667
Autor:
Jia, Sen, Bruce, Neil D. B.
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 has its own bias. In this paper, we first revisit the problem of apply
Externí odkaz:
http://arxiv.org/abs/2002.10540
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, b
Externí odkaz:
http://arxiv.org/abs/2001.08248
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 Iterative Gating (DIGNet). The structure of this mechanism derives fro
Externí odkaz:
http://arxiv.org/abs/1909.12996