259 citations · 942 across the 44 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…
Set Augmented Triplet Loss for Video Person Re-Identification
Pengfei Fang, Pan Ji, Lars Petersson +1
Modern video person re-identification (re-ID) machines are often trained using a metric learning approach, supervised by a triplet loss. The triplet loss used in video re-ID is usu…
Hierarchical Neural Architecture Search for Deep Stereo Matching
Xuelian Cheng, Yiran Zhong, Mehrtash Harandi +5
To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classific…
Channel Recurrent Attention Networks for Video Pedestrian Retrieval
Pengfei Fang, Pan Ji, Jieming Zhou +2
Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks. In this work, we prop…
Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss
Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar
Metric learning is an important problem in machine learning. It aims to group similar examples together. Existing state-of-the-art metric learning approaches require class labels t…
MTL2L: A Context Aware Neural Optimiser
Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier +3
Learning to learn (L2L) trains a meta-learner to assist the learning of a task-specific base learner. Previously, it was shown that a meta-learner could learn the direct rules to u…