Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing

By Zilong Huang, Xinggang Wang, Jiasi Wang, Jingdong Wang. 2018

589 citations recorded by the source corpus.

This paper studies the problem of learning image semantic segmentation networks only using image-level labels as supervision, which is important since it can significantly reduce human annotation efforts. Recent state-of-the-art methods on this problem first infer the sparse and discriminative regions for each object class using a deep classification network, then train semantic a segmentation network using the discriminative regions as supervision. Inspired by the traditional image segmentation methods of seeded region growing, we propose to train a semantic segmentation network starting from the discriminative regions and progressively increase the pixel-level supervision using by seeded region growing. The seeded region growing module is integrated in a deep segmentation network and can benefit from deep features. Different from conventional deep networks which have fixed/static labels, the proposed weakly-supervised network generates new labels using the contextual information within an image. The proposed method significantly outperforms the weakly-supervised semantic segmentation methods using static labels, and obtains the state-of-the-art performance, which are 63.2% mIoU score on the PASCAL VOC 2012 test set and 26.0% mIoU score on the COCO dataset.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Incremental Learning in Semantic Segmentation from Image Labels (2021, methodology): “To address this issue, Weakly Supervised Semantic Segmentation (WSSS) methods aim to obtain effective segmentation models using cheaper supervisions such as bounding boxes [18, 32, 49], scribbles [39, 60], points [6, 53], and image-level labels [31, 34, 35, 59].”
Simultaneously Localize, Segment and Rank the Camouflaged Objects (2021, background): “[63] introduced the class activation map (CAM) to estimate the discriminative region of each class, which is the basis of many weakly supervised methods [1, 51, 17, 50, 24, 43, 47].”
Causal Intervention for Weakly-Supervised Semantic Segmentation (2020, methodology): “To this end, existing methods mainly focus on generating better seed areas [25, 57, 59, 58] and expanding these seed areas [1, 2, 20, 23, 55].”
Weakly-Supervised Semantic Segmentation via Sub-Category Exploration (2020, methodology): “Numerous approaches [1, 12, 13, 18, 22, 38, 40] are proposed to refine the initial cue via expanding the region of attention map.”
Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation (2021, background): “Some other works (Ahn and Kwak 2018; Huang et al. 2018; Shimoda and Yanai 2019) adopt a regiongrowing technique to expand initial regions.”
FickleNet: Weakly and Semi-Supervised Semantic Image Segmentation Using Stochastic Inference (2019, methodology): “Segmentation is performed by DSRG [12].”
Weakly Supervised Segmentation with Maximum Bipartite Graph Matching (2020, methodology): “Although these methods adopt various additional information, such as saliency masks[5, 17, 18, 30], instance saliency map [11] and web images [46], our method still outperforms all previous results and achieves new state-of-the-art performance.”
Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation (2021, methodology): “[20, 25] train the segmentation network by utilizing a boundary loss.”
Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization (2020, unknown): “In addition to the human user, the “computer user” can also benefit from such methods, which can seed image segmentation techniques (Ahn, Cho, and Kwak 2019; Huang et al. 2018; Wang et al. 2019; Hoyer et al. 2019), or help focus generative image models, among other tasks.”
Weakly-Supervised Salient Object Detection via Scribble Annotations (2020, background): “[11] introduced a seeded region growing technique to learn semantic segmentation with image-level labels.”
Survey on semantic segmentation using deep learning techniques (2019, background): “Huang et al. [124] proposed a weakly supervised network, which produces labels using the contextual information within an image.”
Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation (2020, methodology): “2 †DSRG [22] CVPR18 61.”
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation (2021, methodology): “Method Extra Info val test COCO DSRG [14] MSRA-B 61.”
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning (2020, unknown): “[18] and Huang et al.”
Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation (2019, methodology): “Region growing techniques have been recently applied to domain adaptation in semantic segmentation [26], [27].”
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification (2022, background): “For example, one notable capability of CAM is the target localization of a model trained only with image labels; therefore, it prevails in weakly supervised tasks, such as segmentation [4, 13, 16, 43] and detection [38,45,50], or even knowledge distillation [39].”
Unsupervised Domain Adaptation for Semantic Segmentation of Urban Scenes (2019, unknown): “To overcome this issue, semi-supervised methods are emerging, trying to exploit weakly annotated data (e.g., with only image labels or only bounding boxes) [25, 31, 37, 39, 13, 6, 14, 32] or completely unlabeled [24, 29, 15, 31, 19] data after a ﬁrst stage of supervised training.”