research
∙
10/25/2022
From colouring-in to pointillism: revisiting semantic segmentation supervision
The prevailing paradigm for producing semantic segmentation training dat...
share
research
∙
03/26/2019
Large-scale interactive object segmentation with human annotators
Manually annotating object segmentation masks is very time consuming. In...
research
∙
10/09/2017
Person Recognition in Social Media Photos
People nowadays share large parts of their personal lives through social...
research
∙
05/08/2017
Learning non-maximum suppression
Object detectors have hugely profited from moving towards an end-to-end ...
research
∙
03/28/2017
Lucid Data Dreaming for Multiple Object Tracking
Convolutional networks reach top quality in pixel-level object tracking ...
research
∙
01/28/2017
Exploiting saliency for object segmentation from image level labels
There have been remarkable improvements in the semantic labelling task i...
research
∙
12/08/2016
Learning Video Object Segmentation from Static Images
Inspired by recent advances of deep learning in instance segmentation an...
research
∙
07/28/2016
Faceless Person Recognition; Privacy Implications in Social Media
As we shift more of our lives into the virtual domain, the volume of dat...
research
∙
04/06/2016
The Cityscapes Dataset for Semantic Urban Scene Understanding
Visual understanding of complex urban street scenes is an enabling facto...
research
∙
03/24/2016
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Semantic labelling and instance segmentation are two tasks that require ...
research
∙
02/03/2016
How Far are We from Solving Pedestrian Detection?
Encouraged by the recent progress in pedestrian detection, we investigat...
research
∙
09/11/2015
Person Recognition in Personal Photo Collections
Recognising persons in everyday photos presents major challenges (occlud...
research
∙
02/17/2015
What makes for effective detection proposals?
Current top performing object detectors employ detection proposals to gu...
research
∙
01/23/2015
Taking a Deeper Look at Pedestrians
In this paper we study the use of convolutional neural networks (convnet...
research
∙
01/23/2015
Filtered Channel Features for Pedestrian Detection
This paper starts from the observation that multiple top performing pede...
research
∙
11/16/2014
Ten Years of Pedestrian Detection, What Have We Learned?
Paper-by-paper results make it easy to miss the forest for the trees.We ...
research
∙
06/26/2014