3.5k citations · 14.1k across the 436 of their papers we have counts for
7 papers · 1 filter
Staple: Complementary Learners for Real-Time Tracking
Luca Bertinetto, Jack Valmadre, Stuart Golodetz +2
Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.…
Prototypical Priors: From Improving Classification to Zero-Shot Learning
Saumya Jetley, Bernardino Romera-Paredes, Sadeep Jayasumana +1
Recent works on zero-shot learning make use of side information such as visual attributes or natural language semantics to define the relations between output visual classes and th…
Recurrent Instance Segmentation
Bernardino Romera-Paredes, Philip H. S. Torr
Instance segmentation is the problem of detecting and delineating each distinct object of interest appearing in an image. Current instance segmentation approaches consist of ensemb…
Higher Order Conditional Random Fields in Deep Neural Networks
Anurag Arnab, Sadeep Jayasumana, Shuai Zheng +1
We address the problem of semantic segmentation using deep learning. Most segmentation systems include a Conditional Random Field (CRF) to produce a structured output that is consi…
Joint Training of Generic CNN-CRF Models with Stochastic Optimization
Alexander Kirillov, Dmitrij Schlesinger, Shuai Zheng +3
We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Rand…
Sequential Optimization for Efficient High-Quality Object Proposal Generation
Ziming Zhang, Yun Liu, Xi Chen +4
We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and compu…
Conditional Random Fields as Recurrent Neural Networks
Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes +5
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learnin…