94 citations · 201 across the 10 of their papers we have counts for
11 papers
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…
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…
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications
Qing Li, Jiasong Zhu, Jun Liu +4
Estimating depth from RGB images can facilitate many computer vision tasks, such as indoor localization, height estimation, and simultaneous localization and mapping (SLAM). Recent…
Deep Low-rank plus Sparse Network for Dynamic MR Imaging
Wenqi Huang, Ziwen Ke, Zhuo-Xu Cui +6
In dynamic magnetic resonance (MR) imaging, low-rank plus sparse (L+S) decomposition, or robust principal component analysis (PCA), has achieved stunning performance. However, the…
Deep Low-rank Prior in Dynamic MR Imaging
Ziwen Ke, Wenqi Huang, Jing Cheng +8
The deep learning methods have achieved attractive performance in dynamic MR cine imaging. However, all of these methods are only driven by the sparse prior of MR images, while the…
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…
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…
Right for the Right Reason: Training Agnostic Networks
Sen Jia, Thomas Lansdall-Welfare, Nello Cristianini
We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should…
EML-NET:An Expandable Multi-Layer NETwork for Saliency Prediction
Sen Jia, Neil D. B. Bruce
Saliency prediction can benefit from training that involves scene understanding that may be tangential to the central task; this may include understanding places, spatial layout, o…