Video Semantic Segmentation
326 papers with code • 5 benchmarks • 8 datasets
Libraries
Use these libraries to find Video Semantic Segmentation models and implementationsMost implemented papers
Learning Video Object Segmentation from Static Images
Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce video object segmentation problem as a concept of guided instance segmentation.
Fast Video Object Segmentation by Reference-Guided Mask Propagation
We validate our method on four benchmark sets that cover single and multiple object segmentation.
Tukey-Inspired Video Object Segmentation
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.
Rethinking the Evaluation of Video Summaries
Video summarization is a technique to create a short skim of the original video while preserving the main stories/content.
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
We introduce multigrid Predictive Filter Flow (mgPFF), a framework for unsupervised learning on videos.
RANet: Ranking Attention Network for Fast Video Object Segmentation
Specifically, to integrate the insights of matching based and propagation based methods, we employ an encoder-decoder framework to learn pixel-level similarity and segmentation in an end-to-end manner.
MAST: A Memory-Augmented Self-supervised Tracker
Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods.
Learning Fast and Robust Target Models for Video Object Segmentation
The target appearance model consists of a light-weight module, which is learned during the inference stage using fast optimization techniques to predict a coarse but robust target segmentation.
Collaborative Video Object Segmentation by Foreground-Background Integration
This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation.