Unsupervised Video Object Segmentation

49 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.

Most implemented papers

EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency

mfaisal59/EpONet WACV 2020

To handle the nonrigid background like a sea, we also propose a robust fusion mechanism between motion and appearance-based features.

Tukey-Inspired Video Object Segmentation

griffbr/TIS 19 Nov 2018

We investigate the problem of strictly unsupervised video object segmentation, i. e., the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset.

Joint-task Self-supervised Learning for Temporal Correspondence

Liusifei/UVC NeurIPS 2019

Our learning process integrates two highly related tasks: tracking large image regions \emph{and} establishing fine-grained pixel-level associations between consecutive video frames.

MAST: A Memory-Augmented Self-supervised Tracker

zlai0/MAST CVPR 2020

Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods.

D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos

schmiddo/d2conv3d WACV 2021

We further show that D2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D.

Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation

suhwan-cho/tmo 4 Sep 2022

Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level.

Dual Prototype Attention for Unsupervised Video Object Segmentation

hydragon516/dpa 22 Nov 2022

Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos.

Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering

xilin1991/CluterNet 21 Jun 2023

Online unsupervised video object segmentation (UVOS) uses the previous frames as its input to automatically separate the primary object(s) from a streaming video without using any further manual annotation.

Video Object Segmentation using Supervoxel-Based Gerrymandering

griffbr/supervoxel-gerrymandering 18 Apr 2017

Focusing on the problem of strictly unsupervised video object segmentation, we devise a method called supervoxel gerrymandering that links masks of foregroundness and backgroundness via local and non-local consensus measures.

SegFlow: Joint Learning for Video Object Segmentation and Optical Flow

JingchunCheng/SegFlow ICCV 2017

This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos.