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Video Mask2former Swin Small Youtubevis 2021 Instance

Developed by shivalikasingh
Video Mask2Former model trained on the YouTubeVIS-2021 dataset for video instance segmentation tasks, using Swin Transformer as the backbone network.
Downloads 18
Release Time : 3/22/2023

Model Overview

This model is an extension of Mask2Former for video instance segmentation tasks, handling instance segmentation, semantic segmentation, and panoptic segmentation tasks through a unified paradigm, predicting a set of masks and their corresponding labels.

Model Features

Unified Segmentation Paradigm
Treats instance segmentation, semantic segmentation, and panoptic segmentation uniformly as instance segmentation problems.
Efficient Attention Mechanism
Uses multi-scale deformable attention Transformer to replace traditional pixel decoders.
Masked Attention Decoder
Employs a Transformer decoder with masked attention to improve performance without increasing computational load.
Efficient Training Strategy
Significantly enhances training efficiency by computing loss on subsampled points rather than entire masks.

Model Capabilities

Video Instance Segmentation
Multi-object Tracking
Dynamic Scene Analysis

Use Cases

Video Analysis
Autonomous Driving Scene Understanding
Identifies and tracks dynamic objects on the road.
Accurately segments moving vehicles and pedestrians.
Video Surveillance
Real-time analysis of multi-object movements in surveillance videos.
Supports simultaneous tracking and segmentation of multiple objects.
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