SOGNet: Scene Overlap Graph Network for Panoptic Segmentation

By Yibo Yang, Hongyang Li, Xia Li, Qijie Zhao, Jianlong Wu, Zhouchen Lin. 2019

64 citations recorded by the source corpus.

The panoptic segmentation task requires a unified result from semantic and instance segmentation outputs that may contain overlaps. However, current studies widely ignore modeling overlaps. In this study, we aim to model overlap relations among instances and resolve them for panoptic segmentation. Inspired by scene graph representation, we formulate the overlapping problem as a simplified case, named scene overlap graph. We leverage each object's category, geometry and appearance features to perform relational embedding, and output a relation matrix that encodes overlap relations. In order to overcome the lack of supervision, we introduce a differentiable module to resolve the overlap between any pair of instances. The mask logits after removing overlaps are fed into per-pixel instance id classification, which leverages the panoptic supervision to assist in the modeling of overlap relations. Besides, we generate an approximate ground truth of overlap relations as the weak supervision, to quantify the accuracy of overlap relations predicted by our method. Experiments on COCO and Cityscapes demonstrate that our method is able to accurately predict overlap relations, and outperform the state-of-the-art performance for panoptic segmentation. Our method also won the Innovation Award in COCO 2019 challenge.

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

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Citations in the corpus

MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers (2020, methodology): “Then, the instance segments (‘thing’) and semantic segments (‘stuff’) [13] are fused by merging modules [54, 56, 74, 67, 101] to generate panoptic segmentation.”
Object-Contextual Representations for Semantic Segmentation (2019, neutral): “In general, the performance of “Panoptic-FPN + OCR” is very competitive compared to various recent methods [66,44,69].”
k-means Mask Transformer (2022, methodology): “…box-based methods [54] that decompose panoptic segmentation into multiple surrogate tasks ( e.g ., predicting masks for each detected object bounding box [40], followed by fusing the instance segments (‘thing’) and semantic segments (‘stuﬀ’) [14] with merging modules [62,79,67,105,101,60]).”
Fully Convolutional Networks for Panoptic Segmentation (2020, background): “Foreground things are usually separated and represented with boxes [18, 52, 5, 24] or aggregated according to center offsets [51], while background stuff is often predicted with a parallel FCN [33] branch.”
Scaling Wide Residual Networks for Panoptic Segmentation (2020, unknown): “9 SOGNet [97] DCN-101 [17] 3 47.”
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation (2020, unknown): “Some later approaches [40, 28, 23, 22, 38, 43] try to explicitly model this relationship in a learnable fashion.”