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LVOS: A Benchmark for Long-term Video Object Segmentation
[article]
2023
arXiv
pre-print
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Existing video object segmentation (VOS) benchmarks focus on short-term videos which just last about 3-5 seconds and where objects are visible most of the time. ...
Each video includes various attributes, especially challenges deriving from the wild, such as long-term reappearing and cross-temporal similar objeccts.Based on LVOS, we assess existing video object segmentation ...
To this end, we propose the first long-term video object segmentation benchmark dataset, named Long-term Video Object Segmentation (LVOS). ...
arXiv:2211.10181v2
fatcat:xkqdlpdctbhgxhcvhrc5li6ohu
LVOS: A Benchmark for Large-scale Long-term Video Object Segmentation
[article]
2024
arXiv
pre-print
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Video object segmentation (VOS) aims to distinguish and track target objects in a video. ...
Each video includes various attributes, especially challenges deriving from the wild, such as long-term reappearing and cross-temporal similar objects. ...
LVOS: LARGE-SCALE LONG-TERM VIDEO OBJECT SEGMENTATION BENCHMARK DATASET Our principal objective in developing the LVOS is to establish an expansive benchmark explicitly designed for the needs of long-term ...
arXiv:2404.19326v2
fatcat:2kad62cnvzd3hfr4aavm56w7xe
Efficient Video Object Segmentation via Modulated Cross-Attention Memory
[article]
2024
arXiv
pre-print
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Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation. ...
demands without any degradation in segmentation accuracy on long videos. ...
Long-term Video Object Segmentation (LVOS): This benchmark addresses the limitations of existing short-video benchmarks by introducing a more challenging long-video object segmentation dataset. ...
arXiv:2403.17937v1
fatcat:74fk3ls4bngexekexabunizcbm
A Survey on Deep Learning Technique for Video Segmentation
[article]
2022
arXiv
pre-print
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Video segmentation -- partitioning video frames into multiple segments or objects -- plays a critical role in a broad range of practical applications, from enhancing visual effects in movie, to understanding ...
In this survey, we comprehensively review two basic lines of research -- generic object segmentation (of unknown categories) in videos, and video semantic segmentation -- by introducing their respective ...
FUTURE RESEARCH DIRECTIONS Based on the reviewed research, we list several future research directions that we believe should be pursued. • Long-Term Video Segmentation: Long-term video segmentation is ...
arXiv:2107.01153v4
fatcat:j3xlctjkwbg2hevjrkp5ztggcq
Putting the Object Back into Video Object Segmentation
[article]
2024
arXiv
pre-print
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2023 pre-print arXiv:2310.12982v1 fatcat:r5rki4tr6nesxlenui56a7hszu
We present Cutie, a video object segmentation (VOS) network with object-level memory reading, which puts the object representation from memory back into the video object segmentation result. ...
The object queries act as a high-level summary of the target object, while high-resolution feature maps are retained for accurate segmentation. ...
LVOS [87] is a recently proposed long-term video object segmentation benchmark, with 50 videos in its validation set and test set respectively. ...
arXiv:2310.12982v2
fatcat:t76cfqwqkjg2dmjmks5v4kpg4q
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
[article]
2018
arXiv
pre-print
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As a result, we propose to prepare inexpensive, yet high quality pseudo ground truth corrected with motion cue for video object segmentation training. ...
One major technique debt in video object segmentation is to label the object masks for training instances. ...
Introduction Video object segmentation (VOS) is the task to segment foreground objects from background across all frames in a video clip. ...
arXiv:1812.05206v1
fatcat:frprtbqbqbeofd34mzjkiozqma
RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation
[article]
2020
arXiv
pre-print
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The task of video object segmentation with referring expressions (language-guided VOS) is to, given a linguistic phrase and a video, generate binary masks for the object to which the phrase refers. ...
We leverage this data to analyze the results of RefVOS, a novel neural network that obtains competitive results for the task of language-guided image segmentation and state of the art results for language-guided ...
[34] collected Refer-Youtube-VOS, a large-scale benchmark for language-guided video object segmentation built on top of Youtube-VOS [39] . ...
arXiv:2010.00263v1
fatcat:iz2c2wrcrjfhbdfz4jsfdab34i
Pop‐net: A self‐growth network for popping out the salient object in videos
2021
IET Computer Vision
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It is a big challenge for unsupervised video segmentation without any object annotation or prior knowledge. ...
In this article, we formulate a completely unsupervised video object segmentation network which can pop out the most salient object in an input video by self-growth, called Pop-Net. ...
To explore the long-term motion cues of the object, long-term point trajectory methods were proposed [19] [20] [21] . ...
doi:10.1049/cvi2.12032
fatcat:nyxhdnlu3zdyfggsgcy7mvkjny
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
[article]
2018
arXiv
pre-print
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Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. ...
In this paper, a novel unsupervised video object segmentation approach via distractor-aware online adaptation (DOA) is proposed. ...
Conclusion A distractor-aware online adaptation for unsupervised video object segmentation is proposed. ...
arXiv:1812.07712v1
fatcat:sztkyovztfdjlgeto4tu6j5kuu
Anchor Diffusion for Unsupervised Video Object Segmentation
[article]
2019
arXiv
pre-print
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Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow. ...
Motivated by these observations, in this paper we explore simple yet effective strategies to model long-term temporal dependencies. ...
Benchmarks Datasets. DAVIS [51] is a benchmark and yearly challenge for video object segmentation (VOS). ...
arXiv:1910.10895v1
fatcat:htt65zrbivhc5fuqeptcfloii4
Anchor Diffusion for Unsupervised Video Object Segmentation
2019
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Preserved Fulltext
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The file type is application/pdf.
Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow. ...
Motivated by these observations, in this paper we explore simple yet effective strategies to model long-term temporal dependencies. ...
Benchmarks Datasets. DAVIS [51] is a benchmark and yearly challenge for video object segmentation (VOS). ...
doi:10.1109/iccv.2019.00102
dblp:conf/iccv/YangWBBHT19
fatcat:s7nitjbksfdsdmfedery72dybi
Making a Case for 3D Convolutions for Object Segmentation in Videos
[article]
2023
arXiv
pre-print
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The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the ...
In this work, we show that 3D CNNs can be effectively applied to dense video prediction tasks such as salient object segmentation. ...
Computing resources for several experiments were granted by RWTH Aachen University under project 'rwth0519'. We thank Paul Voigtlaender and István Sárándi for helpful discussions. ...
arXiv:2008.11516v2
fatcat:evzjcwrxjnd4lojijfsagtcbkq
Flow Guided Recurrent Neural Encoder for Video Salient Object Detection
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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It can be considered as a universal framework to extend any FCN based static saliency detector to video salient object detection. ...
In this paper, we present flow guided recurrent neural encoder (FGRNE), an accurate and end-to-end learning framework for video salient object detection. ...
Performance comparison on unsupervised video object segmentation in terms of mean IoU
Comparison with Unsupervised Video Object Segmentation Methods The problem setting of video salient object detection ...
doi:10.1109/cvpr.2018.00342
dblp:conf/cvpr/Li0WWL18
fatcat:fjsbfglqfrh2xcwdxewjheu3la
Fast and Accurate Online Video Object Segmentation via Tracking Parts
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
To segment a target object through the video, numerous CNN-based methods have been developed by heavily finetuning on the object mask in the first frame, which is time-consuming for online applications ...
Online video object segmentation is a challenging task as it entails to process the image sequence timely and accurately. ...
However, these approaches may propagate segmentation errors after tracking for a long period of time. ...
doi:10.1109/cvpr.2018.00774
dblp:conf/cvpr/ChengTHW018
fatcat:q3y6metp4zdbvnon7hogg77bra
Fast and Accurate Online Video Object Segmentation via Tracking Parts
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
To segment a target object through the video, numerous CNN-based methods have been developed by heavily finetuning on the object mask in the first frame, which is time-consuming for online applications ...
Online video object segmentation is a challenging task as it entails to process the image sequence timely and accurately. ...
However, these approaches may propagate segmentation errors after tracking for a long period of time. ...
arXiv:1806.02323v1
fatcat:trtf3jkxqrhfvcmthsfolaial4
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