Video Salient Object Detection
20 papers with code • 10 benchmarks • 4 datasets
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.
( Image credit: Shifting More Attention to Video Salient Object Detection, CVPR2019-Best Paper Finalist )
Benchmarks
These leaderboards are used to track progress in Video Salient Object Detection
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FBMS
SegTrack-v2
FBMS-59
ViSal
Latest papers with no code
Confidence-guided Adaptive Gate and Dual Differential Enhancement for Video Salient Object Detection
Video salient object detection (VSOD) aims to locate and segment the most attractive object by exploiting both spatial cues and temporal cues hidden in video sequences.
Fast Video Salient Object Detection via Spatiotemporal Knowledge Distillation
In this paper, to simplify the network and maintain the accuracy, we present a lightweight network tailored for video salient object detection through the spatiotemporal knowledge distillation.
TENet: Triple Excitation Network for Video Salient Object Detection
In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, spatial, temporal, and online excitations.
Time Masking: Leveraging Temporal Information in Spoken Dialogue Systems
Much of the previous work has relied on modeling the natural order of the conversation, using distance based offsets as an approximation of time.
Salient Object Detection in Video using Deep Non-Local Neural Networks
Detection of salient objects in image and video is of great importance in many computer vision applications.
Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation
We even demonstrate competitive results comparable to deep learning based methods in the semi-supervised setting on the DAVIS dataset.
Unsupervised Video Object Segmentation with Motion-based Bilateral Networks
First, we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions.
Sequential Clique Optimization for Video Object Segmentation
A novel algorithm to segment out objects in a video sequence is proposed in this work.
Flow Guided Recurrent Neural Encoder for Video Salient Object Detection
Image saliency detection has recently witnessed significant progress due to deep convolutional neural networks.
Video Salient Object Detection Using Spatiotemporal Deep Features
STCRF is our extension of CRF to the temporal domain and describes the relationships among neighboring regions both in a frame and over frames.
