Open AccessPosted Content
CNN-aware Binary Map for General Semantic Segmentation
TL;DR: A novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN) and is the first attempt on general semantic image segmentation using CNN.
read more
Abstract: In this paper we introduce a novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary codes are very robust against noise and non-semantic changes in the image. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN. All the previous papers were limited to few number of category of the images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm outperform the state-of-the-art non-semantic segmentation methods by large margin.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection
Mahdyar Ravanbakhsh,Moin Nabi,Hossein Mousavi,Enver Sangineto,Nicu Sebe +4 more
- 12 Mar 2018
TL;DR: In this article, the authors proposed to measure local abnormality by combining semantic information (inherited from existing CNN models) with low-level optical flow, which can be used without the fine-tuning phase.
276
Weakly Supervised One Shot Segmentation
Hasnain Raza,Mahdyar Ravanbakhsh,Tassilo Klein,Moin Nabi +3 more
- 01 Oct 2019
TL;DR: This paper shows that exploiting information from the base training classes in the current one-shot segmentation set-up allows for weak supervision to be easily used, and can be leveraged to achieve nearly the same results as full supervision but with no pixel annotations, allowing fully automated segmentation.
•Posted Content
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection
TL;DR: In this article, the authors proposed a measure-based method which allows measuring the local abnormality in a video by combining semantic information (inherited from existing CNN models) with low-level optical-flow.
23
Top-down sampling convolution network for face segmentation
Yisu Zhou
- 01 Dec 2017
TL;DR: The paper adopts two different convolution sampling paths: from large scale to small scale sampling (top-down) and small scale to large Scale sampling (bottom-up), and proposes the top-down sampling convolution neural network for face segmentation (TDNN).
7
Design of Knowledge Map Construction Based on Convolutional Neural Network
TL;DR: Traditional text sentiment analysis methods are mainly based on lexicon and machine learning methods, and their application to textual data has exploded with the advent of the Web era.
4
References
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
102.6K
•Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- 03 Dec 2012
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
ImageNet: A large-scale hierarchical image database
Jia Deng,Wei Dong,Richard Socher,Li-Jia Li,Kai Li,Li Fei-Fei +5 more
- 20 Jun 2009
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
- 07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 01 Jan 2015
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
51.9K