VideoMatch: Matching based Video Object Segmentation

@article{Hu2018VideoMatchMB,
  title={VideoMatch: Matching based Video Object Segmentation},
  author={Yuan-Ting Hu and Jia-Bin Huang and Alexander G. Schwing},
  journal={ArXiv},
  year={2018},
  volume={abs/1809.01123}
}
Video object segmentation is challenging yet important in a wide variety of applications for video analysis. Recent works formulate video object segmentation as a prediction task using deep nets to achieve appealing state-of-the-art performance. Due to the formulation as a prediction task, most of these methods require fine-tuning during test time, such that the deep nets memorize the appearance of the objects of interest in the given video. However, fine-tuning is time-consuming and… 
Discriminative Online Learning for Fast Video Object Segmentation
TLDR
This work proposes a novel approach to video object segmentation, based on a dedicated target appearance model that is exclusively learned online to discriminate between the target and background image regions, and designs a specialized loss and customized optimization techniques to enable highly efficient online training.
Fast Pixel-Matching for Video Object Segmentation
Real Time Compressed Video Object Segmentation
TLDR
A propagation based video object segmentation method in compressed domain to accelerate inference speed and achieves comparable accuracy while much faster inference speed compared with recent state-of-the-art algorithms.
Siamese Dynamic Mask Estimation Network for Fast Video Object Segmentation
TLDR
An efficient siamese dynamic mask estimation network for fast video object segmentation using Siamese neural network as a feature extractor and directly predict masks after correlation is proposed, making the framework more simple and efficient.
Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation
TLDR
This work proposes a proposed unified framework consisting of object proposal, tracking and segmentation components that achieves the state-of-the-art performance on several video object segmentation benchmarks.
Mask-Ranking Network for Semi-supervised Video Object Segmentation
TLDR
A novel architecture named Mask-Ranking Network(MRNet), which takes advantage of both the propagation- based method and the matching-based method to address the above problem of video object segmentation and can better handle the deformation of the objects, and make the segmentation result more accurate.
Video Object Segmentation Using Global and Instance Embedding Learning
TLDR
The proposed network learns to differentiate multiple instances and associate them properly in one feed-forward manner through using the relation among different instances per-frame as well as temporal relation across different frames.
Learning Fast and Robust Target Models for Video Object Segmentation
TLDR
This work proposes a novel VOS architecture consisting of two network components, exclusively trained offline, designed to process the coarse scores into high quality segmentation masks, and achieves favorable performance, while operating at higher frame-rates compared to state-of-the-art.
DMVOS: Discriminative Matching for Real-time Video Object Segmentation
TLDR
This work proposes Discriminative Matching for real-time Video Object Segmentation (DMVOS), a real- time VOS framework with high-accuracy to fill this gap in segmentation accuracy.
PPML: Metric Learning with Prior Probability for Video Object Segmentation
TLDR
A prior probability based metric learning (PPML) method for faster inference speed and higher segmentation accuracy is proposed and Experimental results on DAVIS datasets demonstrate that the proposed method reaches the state-of-the-art competitive performance and is more efficient in time consumption.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 63 REFERENCES
Fast and Accurate Online Video Object Segmentation via Tracking Parts
TLDR
This paper proposes a fast and accurate video object segmentation algorithm that can immediately start the segmentation process once receiving the images, and performs favorably against state-of-the-art algorithms in accuracy on the DAVIS benchmark dataset, while achieving much faster runtime performance.
Efficient Video Object Segmentation via Network Modulation
TLDR
This work proposes a novel approach that uses a single forward pass to adapt the segmentation model to the appearance of a specific object and is 70× faster than fine-tuning approaches and achieves similar accuracy.
Learning Video Object Segmentation from Static Images
TLDR
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.
Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
TLDR
Online Adaptive Video Object Segmentation (OnAVOS) is proposed which updates the network online using training examples selected based on the confidence of the network and the spatial configuration and adds a pretraining step based on objectness, which is learned on PASCAL.
Pixel-Level Matching for Video Object Segmentation Using Convolutional Neural Networks
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
Video SnapCut: robust video object cutout using localized classifiers
TLDR
Video SnapCut is presented, a robust video object cutout system that significantly advances the state-of-the-art in segmentation and is completed with a novel coherent video matting technique.
One-Shot Video Object Segmentation
TLDR
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).
Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
TLDR
The proposed method supports different kinds of user input such as segmentation mask in the first frame (semi-supervised scenario), or a sparse set of clicked points (interactive scenario), and reaches comparable quality to competing methods with much less interaction.
Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation
TLDR
A novel saliency estimation technique as well as a novel neighborhood graph, based on optical flow and edge cues are developed, which leads to significantly better initial foreground-background estimates and their robust aswell as accurate diffusion across time.
Fast Video Object Segmentation by Reference-Guided Mask Propagation
TLDR
A deep Siamese encoder-decoder network is proposed that is designed to take advantage of mask propagation and object detection while avoiding the weaknesses of both approaches, and achieves accuracy competitive with state-of-the-art methods while running in a fraction of time compared to others.
...
1
2
3
4
5
...