21 research outputs found
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Convolutional CRFs for semantic segmentation
For the challenging semantic image segmentation task the best performing models
have traditionally combined the structured modelling capabilities of Conditional Random
Fields (CRFs) with the feature extraction power of CNNs. In more recent works however,
CRF post-processing has fallen out of favour. We argue that this is mainly due to the slow
training and inference speeds of CRFs, as well as the difficulty of learning the internal
CRF parameters. To overcome both issues we propose to add the assumption of conditional
independence to the framework of fully-connected CRFs. This allows us to reformulate the
inference in terms of convolutions, which can be implemented highly efficiently on GPUs.
Doing so speeds up inference and training by two orders of magnitude. All parameters of
the convolutional CRFs can easily be optimized using backpropagation. Towards the goal
of facilitating further CRF research we have made our implementations publicly available
Position Paper: On the Role of Abductive Reasoning in Semantic Image Segmentation
This position paper provides insights aiming at resolving the most pressing needs and issues of computer vision algorithms. Specifically, these problems relate to the scarcity of data, the inability of such algorithms to adapt to never-seen-before conditions, and the challenge of developing explainable and trustworthy algorithms.
This work proposes the incorporation of reasoning systems, and in particular of abductive reasoning, into image segmentation algorithms as a potential solution to the aforementioned issues
An Abstraction Model for Semantic Segmentation Algorithms
Semantic segmentation is a process of classifying each pixel in the image.
Due to its advantages, sematic segmentation is used in many tasks such as
cancer detection, robot-assisted surgery, satellite image analysis,
self-driving car control, etc. In this process, accuracy and efficiency are the
two crucial goals for this purpose, and there are several state of the art
neural networks. In each method, by employing different techniques, new
solutions have been presented for increasing efficiency, accuracy, and reducing
the costs. The diversity of the implemented approaches for semantic
segmentation makes it difficult for researches to achieve a comprehensive view
of the field. To offer a comprehensive view, in this paper, an abstraction
model for the task of semantic segmentation is offered. The proposed framework
consists of four general blocks that cover the majority of majority of methods
that have been proposed for semantic segmentation. We also compare different
approaches and consider the importance of each part in the overall performance
of a method.Comment: 6 pages 2 figure
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie