Semi-Supervised Video Object Segmentation
94 papers with code • 15 benchmarks • 13 datasets
The semi-supervised scenario assumes the user inputs a full mask of the object(s) of interest in the first frame of a video sequence. Methods have to produce the segmentation mask for that object(s) in the subsequent frames.
Benchmarks
These leaderboards are used to track progress in Semi-Supervised Video Object Segmentation
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Libraries
Use these libraries to find Semi-Supervised Video Object Segmentation models and implementationsDatasets
DAVIS
DAVIS 2017
DAVIS 2016
YouTube-VOS 2018
Referring Expressions for DAVIS 2016 & 2017
VOTChallenge
MOSE
BURST
BL30K
VOT2020
Latest papers with no code
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.
Alignment Before Aggregation: Trajectory Memory Retrieval Network for Video Object Segmentation
In this work, we reconcile the inherent tension of spatial and temporal information to retrieve memory frame information along the object trajectory, and propose a novel and coherent Trajectory Memory Retrieval Network (TMRN) to equip with the trajectory information, including a spatial alignment module and a temporal aggregation module.
Look Before You Match: Instance Understanding Matters in Video Object Segmentation
Towards this goal, we present a two-branch network for VOS, where the query-based instance segmentation (IS) branch delves into the instance details of the current frame and the VOS branch performs spatial-temporal matching with the memory bank.
Pixel-Level Equalized Matching for Video Object Segmentation
Feature similarity matching, which transfers the information of the reference frame to the query frame, is a key component in semi-supervised video object segmentation.
BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation
It captures object motion in the video via a novel optical flow calibration module that fuses the segmentation mask with optical flow estimation to improve within-object optical flow smoothness and reduce noise at object boundaries.
Region Aware Video Object Segmentation with Deep Motion Modeling
Current semi-supervised video object segmentation (VOS) methods usually leverage the entire features of one frame to predict object masks and update memory.
The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation
The referring video object segmentation task (RVOS) aims to segment object instances in a given video referred by a language expression in all video frames.
Collaborative Attention Memory Network for Video Object Segmentation
In order to overcome the shortcomings , we propose Collaborative Attention Memory Network with an enhanced segmentation head.
MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation
The task of semi-supervised video object segmentation (VOS) has been greatly advanced and state-of-the-art performance has been made by dense matching-based methods.
FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation
In contrast to prior work that uses full optical flow, we introduce a new foreground-targeted visual warping approach that learns flow fields from VOS data.
