Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Benenson, Rodrigo"'
Autor:
Benenson, Rodrigo, Ferrari, Vittorio
The prevailing paradigm for producing semantic segmentation training data relies on densely labelling each pixel of each image in the training set, akin to colouring-in books. This approach becomes a bottleneck when scaling up in the number of images
Externí odkaz:
http://arxiv.org/abs/2210.14142
Manually annotating object segmentation masks is very time consuming. Interactive object segmentation methods offer a more efficient alternative where a human annotator and a machine segmentation model collaborate. In this paper we make several contr
Externí odkaz:
http://arxiv.org/abs/1903.10830
People nowadays share large parts of their personal lives through social media. Being able to automatically recognise people in personal photos may greatly enhance user convenience by easing photo album organisation. For human identification task, ho
Externí odkaz:
http://arxiv.org/abs/1710.03224
Object detectors have hugely profited from moving towards an end-to-end learning paradigm: proposals, features, and the classifier becoming one neural network improved results two-fold on general object detection. One indispensable component is non-m
Externí odkaz:
http://arxiv.org/abs/1705.02950
Convolutional networks reach top quality in pixel-level video object segmentation but require a large amount of training data (1k~100k) to deliver such results. We propose a new training strategy which achieves state-of-the-art results across three e
Externí odkaz:
http://arxiv.org/abs/1703.09554
Convnets have enabled significant progress in pedestrian detection recently, but there are still open questions regarding suitable architectures and training data. We revisit CNN design and point out key adaptations, enabling plain FasterRCNN to obta
Externí odkaz:
http://arxiv.org/abs/1702.05693
Autor:
Oh, Seong Joon, Benenson, Rodrigo, Khoreva, Anna, Akata, Zeynep, Fritz, Mario, Schiele, Bernt
There have been remarkable improvements in the semantic labelling task in the recent years. However, the state of the art methods rely on large-scale pixel-level annotations. This paper studies the problem of training a pixel-wise semantic labeller n
Externí odkaz:
http://arxiv.org/abs/1701.08261
Autor:
Khoreva, Anna, Perazzi, Federico, Benenson, Rodrigo, Schiele, Bernt, Sorkine-Hornung, Alexander
Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce video object segmentation problem as a concept of guided instance segmentation. Our model proceeds on a per-frame basis, guided by the output of t
Externí odkaz:
http://arxiv.org/abs/1612.02646
As we shift more of our lives into the virtual domain, the volume of data shared on the web keeps increasing and presents a threat to our privacy. This works contributes to the understanding of privacy implications of such data sharing by analysing h
Externí odkaz:
http://arxiv.org/abs/1607.08438
Graph-based video segmentation methods rely on superpixels as starting point. While most previous work has focused on the construction of the graph edges and weights as well as solving the graph partitioning problem, this paper focuses on better supe
Externí odkaz:
http://arxiv.org/abs/1605.03718