Pixel-Level Matching for Video Object Segmentation Using Convolutional Neural Networks
@article{Yoon2017PixelLevelMF, title={Pixel-Level Matching for Video Object Segmentation Using Convolutional Neural Networks}, author={Jae Shin Yoon and François Rameau and Junsik Kim and Seokju Lee and Seunghak Shin and In-So Kweon}, journal={2017 IEEE International Conference on Computer Vision (ICCV)}, year={2017}, pages={2186-2195} }
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN). Our network aims to distinguish the target area from the background on the basis of the pixel-level similarity between two object units. The proposed network represents a target object using features from different depth layers in order to take advantage of both the spatial details and the category-level semantic information. Furthermore, we propose a feature…
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References
SHOWING 1-10 OF 36 REFERENCES
Learning Video Object Segmentation from Static Images
- Computer Science2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2017
It is demonstrated that highly accurate object segmentation in videos can be enabled by using a convolutional neural network (convnet) trained with static images only, and a combination of offline and online learning strategies are used.
One-Shot Video Object Segmentation
- Computer Science2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2017
One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot).
Visual Tracking with Fully Convolutional Networks
- Computer Science2015 IEEE International Conference on Computer Vision (ICCV)
- 2015
An in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet shows that the proposed tacker outperforms the state-of-the-art significantly.
Fully Connected Object Proposals for Video Segmentation
- Computer Science2015 IEEE International Conference on Computer Vision (ICCV)
- 2015
A novel approach to video segmentation using multiple object proposals that combines appearance with long-range point tracks to ensure robustness with respect to fast motion and occlusions over longer video sequences is presented.
Hierarchical Convolutional Features for Visual Tracking
- Computer Science2015 IEEE International Conference on Computer Vision (ICCV)
- 2015
This paper adaptively learn correlation filters on each convolutional layer to encode the target appearance and hierarchically infer the maximum response of each layer to locate targets.
Key-segments for video object segmentation
- Computer Science2011 International Conference on Computer Vision
- 2011
The method first identifies object-like regions in any frame according to both static and dynamic cues and compute a series of binary partitions among candidate “key-segments” to discover hypothesis groups with persistent appearance and motion.
Learning Multi-domain Convolutional Neural Networks for Visual Tracking
- Computer Science2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2016
A novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network using a large set of videos with tracking ground-truths to obtain a generic target representation.
Transferring Rich Feature Hierarchies for Robust Visual Tracking
- Computer ScienceArXiv
- 2015
This work pre-training a CNN offline and then transferring the rich feature hierarchies learned to online tracking, and proposes to generate a probability map instead of producing a simple class label to fit the characteristics of object tracking.
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
- Computer Science2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2016
This work presents a new benchmark dataset and evaluation methodology for the area of video object segmentation, named DAVIS (Densely Annotated VIdeo Segmentation), and provides a comprehensive analysis of several state-of-the-art segmentation approaches using three complementary metrics.
Fully Convolutional Networks for Semantic Segmentation
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2017
It is shown that convolutional networks by themselves, trained end- to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.