Unsupervised Video Object Segmentation
50 papers with code • 6 benchmarks • 8 datasets
The unsupervised scenario assumes that the user does not interact with the algorithm to obtain the segmentation masks. Methods should provide a set of object candidates with no overlapping pixels that span through the whole video sequence. This set of objects should contain at least the objects that capture human attention when watching the whole video sequence i.e objects that are more likely to be followed by human gaze.
Datasets
Latest papers with no code
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention
This is enabled by deformable attention mechanism, where the keys and values capturing the memory of a video sequence in the attention module have flexible locations updated across frames.
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation
We evaluate our method on several benchmark datasets and achieve state-of-the-art results.
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation
Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge.
UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model
Unsupervised video object segmentation has made significant progress in recent years, but the manual annotation of video mask datasets is expensive and limits the diversity of available datasets.
Guided Slot Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence.
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation
In recent works, two approaches for UVOS have been discussed that can be divided into: appearance and appearance-motion-based methods, which have limitations respectively.
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation
On the related problem of video instance segmentation, our method shows competitive performance with the previous best algorithm that requires joint training with the VOS algorithm.
Flow-guided Semi-supervised Video Object Segmentation
A model to extract the combined information from optical flow and the image is proposed, which is then used as input to the target model and the decoder network.
Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning
We integrate our offline training and online fine-tuning in a unified framework for unsupervised video object segmentation and dub our method Online Adversarial Self-Tuning (OAST).
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy
Previous methods in unsupervised video object segmentation (UVOS) have demonstrated the effectiveness of motion as either input or supervision for segmentation.