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Found 5 papers, 5 papers with code
Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation
To achieve this objective, we present a unified self-supervised approach to learn visual representations of static-dynamic feature similarity.
VideoMAC: Video Masked Autoencoders Meet ConvNets
In this paper, we propose a new approach termed as \textbf{VideoMAC}, which combines video masked autoencoders with resource-friendly ConvNets.
Hierarchical Graph Pattern Understanding for Zero-Shot VOS
However, existing optical flow-based methods have a significant dependency on optical flow, which results in poor performance when the optical flow estimation fails for a particular scene.
Co-attention Propagation Network for Zero-Shot Video Object Segmentation
Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects.
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS).
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