Semantic Segmentation With Context Encoding and Multi-Path Decoding

By Henghui Ding, Xudong Jiang, Bing Shuai, Ai Qun Liu, Gang Wang. 2020

148 citations recorded by the source corpus.

Semantic image segmentation aims to classify every pixel of a scene image to one of many classes. It implicitly involves object recognition, localization, and boundary delineation. In this paper, we propose a segmentation network called CGBNet to enhance the segmentation performance by context encoding and multi-path decoding. We first propose a context encoding module that generates context-contrasted local feature to make use of the informative context and the discriminative local information. This context encoding module greatly improves the segmentation performance, especially for inconspicuous objects. Furthermore, we propose a scale-selection scheme to selectively fuse the segmentation results from different-scales of features at every spatial position. It adaptively selects appropriate score maps from rich scales of features. To improve the segmentation performance results at boundary, we further propose a boundary delineation module that encourages the location-specific very-low-level features near the boundaries to take part in the final prediction and suppresses them far from the boundaries. The proposed segmentation network achieves very competitive performance in terms of all three different evaluation metrics consistently on the six popular scene segmentation datasets, Pascal Context, SUN-RGBD, Sift Flow, COCO Stuff, ADE20K, and Cityscapes.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Representation Compensation Networks for Continual Semantic Segmentation (2022, background): “However, these fully-supervised models [16, 23, 93] can only handle a fixed number of classes.”
Transformer-Based Visual Segmentation: A Survey (2023, methodology): “These methods can be divided into the following aspects, including better encoder-decoder frameworks [58], [59], larger kernels [60], [61], multiscale pooling [11], [62], multiscale feature fusion [12], [63], [64], [65], non-local modeling [18], [66], [67], efﬁcient modeling [68], [69], [70], and…”
Is Context-Aware CNN Ready for the Surroundings? Panoramic Semantic Segmentation in the Wild (2021, background): “Pyramid pooling [14], atrous spatial pyramid pooling [3], [13] and multi-scale feature representations [15]–[17] helped to sense different levels of surrounding context.”
PhraseClick: Toward Achieving Flexible Interactive Segmentation by Phrase and Click (2020, background): “Driven by the signiﬁcant success of CNN, many deep-learning-based works have been proposed for semantic segmentation [8, 12– 15, 22, 52, 71] and instance segmentation [7, 20, 23, 37].”
Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation (2020, methodology): “With the research efforts in the ﬁeld, the recent methods can be classiﬁed into two categories according to the network architecture, including atrous convolution based methods [4], [22]–[24], and encoder-decoder based methods [25]–[30].”