Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity

• URL: http://arxiv.org/abs/2204.06107v1
• Date: Tue, 12 Apr 2022 22:37:49 GMT
• Title: Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity
• Authors: Weiyao Wang, Matt Feiszli, Heng Wang, Jitendra Malik, Du Tran
• Abstract summary: We propose a novel approach for mask proposals, Generic Grouping Networks (GGNs) Our approach combines a local measure of pixel affinity with instance-level mask supervision, producing a training regimen designed to make the model as generic as the data diversity allows.
• Score: 59.1823948436411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-world instance segmentation is the task of grouping pixels into object instances without any pre-determined taxonomy. This is challenging, as state-of-the-art methods rely on explicit class semantics obtained from large labeled datasets, and out-of-domain evaluation performance drops significantly. Here we propose a novel approach for mask proposals, Generic Grouping Networks (GGNs), constructed without semantic supervision. Our approach combines a local measure of pixel affinity with instance-level mask supervision, producing a training regimen designed to make the model as generic as the data diversity allows. We introduce a method for predicting Pairwise Affinities (PA), a learned local relationship between pairs of pixels. PA generalizes very well to unseen categories. From PA we construct a large set of pseudo-ground-truth instance masks; combined with human-annotated instance masks we train GGNs and significantly outperform the SOTA on open-world instance segmentation on various benchmarks including COCO, LVIS, ADE20K, and UVO. Code is available on project website: https://sites.google.com/view/generic-grouping/.

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