Video Segmentation
100 papers with code • 1 benchmarks • 9 datasets
Datasets
Subtasks
Most implemented papers
Generic Event Boundary Detection: A Benchmark for Event Segmentation
This paper presents a novel task together with a new benchmark for detecting generic, taxonomy-free event boundaries that segment a whole video into chunks.
D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
We further show that D2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D.
RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation
Given an input image or video, our framework first conducts multi-label classification over the complete label, then sorts the complete label and selects a small subset according to their class confidence scores.
Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation
This paper presents a deep learning framework for medical video segmentation.
Tube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation
Our framework is a near-online approach that takes a short subclip as input and outputs the corresponding spatial-temporal tube masks.
Semantic Video Segmentation : Exploring Inference Efficiency
We explore the efficiency of the CRF inference beyond image level semantic segmentation and perform joint inference in video frames.
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
The dataset, named DAVIS (Densely Annotated VIdeo Segmentation), consists of fifty high quality, Full HD video sequences, spanning multiple occurrences of common video object segmentation challenges such as occlusions, motion-blur and appearance changes.
Feature Space Optimization for Semantic Video Segmentation
We present an approach to long-range spatio-temporal regularization in semantic video segmentation.
STFCN: Spatio-Temporal FCN for Semantic Video Segmentation
Current work on convolutional neural networks(CNNs) has shown that CNNs provide advanced spatial features supporting a very good performance of solutions for both image and video analysis, especially for the semantic segmentation task.
Efficient Hierarchical Graph-Based Segmentation of RGBD Videos
We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach.