Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2304.01114v1
• Date: Mon, 3 Apr 2023 16:24:39 GMT
• Title: Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation
• Authors: Yabo Zhang, Zihao Wang, Jun Hao Liew, Jingjia Huang, Manyu Zhu, Jiashi Feng, Wangmeng Zuo
• Abstract summary: We introduce self-supervised spatially-consistent grouping with text-supervised semantic segmentation. Considering the part-like grouped results, we further adapt a text-supervised model from image-level to region-level recognition. Our method achieves 59.2% mIoU and 32.4% mIoU on Pascal VOC and Pascal Context benchmarks.
• Score: 117.36746226803993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs. Due to the lack of dense annotations, existing text-supervised methods can only learn to group an image into semantic regions via pixel-insensitive feedback. As a result, their grouped results are coarse and often contain small spurious regions, limiting the upper-bound performance of segmentation. On the other hand, we observe that grouped results from self-supervised models are more semantically consistent and break the bottleneck of existing methods. Motivated by this, we introduce associate self-supervised spatially-consistent grouping with text-supervised semantic segmentation. Considering the part-like grouped results, we further adapt a text-supervised model from image-level to region-level recognition with two core designs. First, we encourage fine-grained alignment with a one-way noun-to-region contrastive loss, which reduces the mismatched noun-region pairs. Second, we adopt a contextually aware masking strategy to enable simultaneous recognition of all grouped regions. Coupled with spatially-consistent grouping and region-adapted recognition, our method achieves 59.2% mIoU and 32.4% mIoU on Pascal VOC and Pascal Context benchmarks, significantly surpassing the state-of-the-art methods.

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