arXiv:2110.10239
공식 코드 · Papers with Code
1st Place Solution for the UVO Challenge on Image-based Open-World Segmentation 2021
원문 초록
We describe our two-stage instance segmentation framework we use to compete in the challenge. The first stage of our framework consists of an object detector, which generates object proposals in the format of bounding boxes. Then, the images and the detected bounding boxes are fed to the second stage, where a segmentation network is applied to segment the objects in the bounding boxes. We train all our networks in a class-agnostic way. Our approach achieves the first place in the UVO 2021 Image-based Open-World Segmentation Challenge.
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NOCTIS: Novel Object Cyclic Threshold based Instance Segmentation
No time to train! Training-Free Reference-Based Instance Segmentation
SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation
MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
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