Papers › Context Contrasted Feature and Gated Multi-Scale Aggregation for Scene Segmentation

Context Contrasted Feature and Gated Multi-Scale Aggregation for Scene Segmentation

1 Jun 2018CVPR 2018 6archive 2025-07-28

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

Scene segmentation is a challenging task as it need label every pixel in the image. It is crucial to exploit discriminative context and aggregate multi-scale features to achieve better segmentation. In this paper, we first propose a novel context contrasted local feature that not only leverages the informative context but also spotlights the local information in contrast to the context. The proposed context contrasted local feature greatly improves the parsing performance, especially for inconspicuous objects and background stuff. Furthermore, we propose a scheme of gated sum to selectively aggregate multi-scale features for each spatial position. The gates in this scheme control the information flow of different scale features. Their values are generated from the testing image by the proposed network learnt from the training data so that they are adaptive not only to the training data, but also to the specific testing image. Without bells and whistles, the proposed approach achieves the state-of-the-arts consistently on the three popular scene segmentation datasets, Pascal Context, SUN-RGBD and COCO Stuff.

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Code

henghuiding/CCL report

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Tasks

Scene Segmentation Segmentation Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Semantic Segmentation	COCO-Stuff test	CCL (ResNet-101)	mIoU	35.7%	#18 of 21	Archive leaderboard	report
Semantic Segmentation	SUN-RGBD	EMSANet (2x ResNet-34 NBt1D, PanopticNDT version, finetuned)	Mean IoU	47.1%	#36 of 44	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Convolution Global Average Pooling Kaiming Initialization Max Pooling ReLU Residual Block Residual Connection

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