https://github.com/Ali2500/TarViS
공식 코드TarViS: A Unified Approach for Target-based Video Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Video Panoptic Segmentation is a computer vision task that extends panoptic segmentation by incorporating temporal dimension. That is, given a video sequence, the goal is to predict the semantic class of each pixel while consistently tracking object instances. Here, the pixels belonging to the same object instance should be assigned the same instance ID throughout the video sequence.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
TarViS: A Unified Approach for Target-based Video Segmentation
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation
An Integrated Framework for Multi-Granular Explanation of Video Summarization
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
Tube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation
Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation
UniVS: Unified and Universal Video Segmentation with Prompts as Queries
A Simple Video Segmenter by Tracking Objects Along Axial Trajectories
A Simple Video Segmenter by Tracking Objects Along Axial Trajectories
DVIS++: Improved Decoupled Framework for Universal Video Segmentation
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.