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Papers/Causal Intervention for Weakly-Supervised Semantic Segment...

Causal Intervention for Weakly-Supervised Semantic Segmentation

Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun

2020-09-26NeurIPS 2020 12Weakly-Supervised Semantic SegmentationAttributeWeakly supervised Semantic SegmentationSegmentationSemantic SegmentationCausal Inference
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Abstract

We present a causal inference framework to improve Weakly-Supervised Semantic Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by using only image-level labels -- the most crucial step in WSSS. We attribute the cause of the ambiguous boundaries of pseudo-masks to the confounding context, e.g., the correct image-level classification of "horse" and "person" may be not only due to the recognition of each instance, but also their co-occurrence context, making the model inspection (e.g., CAM) hard to distinguish between the boundaries. Inspired by this, we propose a structural causal model to analyze the causalities among images, contexts, and class labels. Based on it, we develop a new method: Context Adjustment (CONTA), to remove the confounding bias in image-level classification and thus provide better pseudo-masks as ground-truth for the subsequent segmentation model. On PASCAL VOC 2012 and MS-COCO, we show that CONTA boosts various popular WSSS methods to new state-of-the-arts.

Results

TaskDatasetMetricValueModel
Semantic SegmentationCOCO 2014 valmIoU33.4IRNet+CONTA
Semantic SegmentationPASCAL VOC 2012 valMean IoU66.1SEAM+CONTA
10-shot image generationCOCO 2014 valmIoU33.4IRNet+CONTA
10-shot image generationPASCAL VOC 2012 valMean IoU66.1SEAM+CONTA

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