1. Shifting More Attention to Video Salient Object Detection Deng-Ping Fan1, Wenguan Wang2, Ming-Ming Cheng1, and Jianbing Shen2 1Nankai University 2Inception Institute of Artificanial Intelligence Dataset, Code, and Result:
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1.Introduction
2.Densely Annotated VSOD (DAVSOD) dataset
4.Largest-scale benchmark
Problem:
Existing video salient object detection (VSOD) datasets:
Limited to small scales (dozes of videos).
Without real human attention when annotation.
Its diversity and generality are quite limited.
Ignore the saliency shift phenomenon.
Contribution:
Based on the new problem——saliency shift, we
Presented a new DAVSOD dataset.
Proposed a strong SSAV model.
Provided the largest-scale benchmark.
Suggested some potential research, eg., saliency-aware video captioning, video salient object subitizing.
Vi. : #videos.
AF.: #annotated frames.
DL: densely labeling.
AS: attention shift.
FP: annotate objects
according to eye fixation.
EF: eye fixation records
IL: instance annotation.
5.Result
3.Saliency-Shift Aware VSOD (SSAV) model
6.Take-away
Extensive experiments verified that even considering top performing
models, VSOD remain seems far from being solved.
Promising new avenue for video salient object detection.