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Video salient object detection (VSOD) is significantly essential for understanding the underlying mechanism behind HVS during free-viewing in general and instrumental to a wide range of real-world applications, e.g., video segmentation, video captioning, video compression, autonomous driving, robotic interaction, weakly supervised attention. Besides its academic value and practical significance, VSOD presents great difficulties due to the challenges carried by video data (diverse motion patterns, occlusions, blur, large object deformations, etc.) and the inherent complexity of human visual attention behavior (i.e., selective attention allocation, attention shift) during dynamic scenes. Online benchmark: http://dpfan.net/davsod.
<span style="color:grey; opacity: 0.6">( Image credit: Shifting More Attention to Video Salient Object Detection, CVPR2019-Best Paper Finalist )</span>
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is an extension of the BMS dataset with 33 additional video sequences. A total of 720 frames is annotated. It has pixel-accurate segmentation annotations of moving objects. FBMS-59 comes with a split into a training set and a test set. Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/ Image Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/
SegTrack v2 is a video segmentation dataset with full pixel-level annotations on multiple objects at each frame within each video. Source: https://web.engr.oregonstate.edu/~lif/SegTrack2/dataset.html Image Source: https://www.researchgate.net/publication/325842926_Semantic_Video_Segmentation_A_Review_on_Recent_Approaches
The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is a dataset for motion segmentation, which extends the BMS-26 dataset with 33 additional video sequences. A total of 720 frames is annotated. FBMS-59 comes with a split into a training set and a test set. Typical challenges appear in both sets. Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html Image Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html
DataViSal.rar (including the ground truth data) is our new collected dataset for the following paper. =========================================================================== W. Wang, J. Shen, and L. Shao, Consistent video saliency using local gradient flow optimization and global refinement, IEEE Trans. on Image Processing, 24(11):4185-4196, 2015 =========================================================================== The related source code can be downloaded from: https://github.com/shenjianbing/videosal =========================================================================== Note: =========================================================================== The data and code files are free to use for research purposes. If you use them for research purposes, you should cite above paper in any resulting publication. This code also uses some publicly available functions. =========================================================================== Contact Information =========================================================================== Email: wenguanwang@bit.edu.cn shenjianbing@bit.edu.cn shenjianbingcg@gmail.com
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.