259 citations · 803 across the 48 of their papers we have counts for
9 papers · 1 filter
Uncertainty-Aware Deep Calibrated Salient Object Detection
Jing Zhang, Yuchao Dai, Xin Yu +3
Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. However, those methods overlook the gap between network ac…
Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration
Liyuan Pan, Shah Chowdhury, Richard Hartley +3
The dual-pixel (DP) hardware works by splitting each pixel in half and creating an image pair in a single snapshot. Several works estimate depth/inverse depth by treating the DP pa…
Refining Semantic Segmentation with Superpixel by Transparent Initialization and Sparse Encoder
Zhiwei Xu, Thalaiyasingam Ajanthan, Richard Hartley
Although deep learning greatly improves the performance of semantic segmentation, its success mainly lies in object central areas without accurate edges. As superpixels are a popul…
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Zhiwei Xu, Thalaiyasingam Ajanthan, Vibhav Vineet +1
Although 3D Convolutional Neural Networks (CNNs) are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory…
Calibration of Neural Networks using Splines
Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan +3
Calibrating neural networks is of utmost importance when employing them in safety-critical applications where the downstream decision making depends on the predicted probabilities.…
Single Image Optical Flow Estimation with an Event Camera
Liyuan Pan, Miaomiao Liu, Richard Hartley
Event cameras are bio-inspired sensors that asynchronously report intensity changes in microsecond resolution. DAVIS can capture high dynamics of a scene and simultaneously output…
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
Amir Rahimi, Amirreza Shaban, Thalaiyasingam Ajanthan +2
Weakly Supervised Object Localization (WSOL) methods only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms. We st…
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
Amir Rahimi, Amirreza Shaban, Ching-An Cheng +2
Predicting calibrated confidence scores for multi-class deep networks is important for avoiding rare but costly mistakes. A common approach is to learn a post-hoc calibration funct…
Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization
Shihao Jiang, Dylan Campbell, Miaomiao Liu +2
We address the problem of joint optical flow and camera motion estimation in rigid scenes by incorporating geometric constraints into an unsupervised deep learning framework. Unlik…