Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2103.14581v1
• Date: Fri, 26 Mar 2021 16:44:03 GMT
• Title: Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation
• Authors: Yazhou Yao, Tao Chen, Guosen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, and Jian Zhang
• Abstract summary: We propose a non-salient region object mining approach for weakly supervised semantic segmentation. A potential object mining module is proposed to reduce the false-negative rate in pseudo labels. Our non-salient region masking module helps further discover the objects in the non-salient region.
• Score: 64.2719590819468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels to alleviate the annotation burden of segmentation. However, existing works mainly concentrate on expanding the seed of pseudo labels within the image's salient region. In this work, we propose a non-salient region object mining approach for weakly supervised semantic segmentation. We introduce a graph-based global reasoning unit to strengthen the classification network's ability to capture global relations among disjoint and distant regions. This helps the network activate the object features outside the salient area. To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability. Specifically, a potential object mining module is proposed to reduce the false-negative rate in pseudo labels. Moreover, we propose a non-salient region masking module for complex images to generate masked pseudo labels. Our non-salient region masking module helps further discover the objects in the non-salient region. Extensive experiments on the PASCAL VOC dataset demonstrate state-of-the-art results compared to current methods.

Related papers

• Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation [84.62067728093358]
  Weakly supervised object localization and semantic segmentation aim to localize objects using only image-level labels. New paradigm has emerged by generating a foreground prediction map to achieve pixel-level localization. This paper presents two astonishing experimental observations on the object localization learning process.
  arXiv  Detail & Related papers  (2023-09-22T15:44:10Z)
• Semi-Supervised Semantic Segmentation With Region Relevance [28.92449538610617]
  Semi-supervised semantic segmentation aims to learn from a small amount of labeled data and plenty of unlabeled ones. The most common approach is to generate pseudo-labels for unlabeled images to augment the training data. This paper proposes a Region Relevance Network (RRN) to alleviate the problem mentioned above.
  arXiv  Detail & Related papers  (2023-04-23T04:51:27Z)
• Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation [66.87777732230884]
  We propose a saliency guided Inter- and Intra-Class Relation Constrained (I$2$CRC) framework to assist the expansion of the activated object regions. We also introduce an object guided label refinement module to take a full use of both the segmentation prediction and the initial labels for obtaining superior pseudo-labels.
  arXiv  Detail & Related papers  (2022-06-20T03:40:56Z)
• Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization [98.46318529630109]
  We take inspiration from traditional spectral segmentation methods by reframing image decomposition as a graph partitioning problem. We find that these eigenvectors already decompose an image into meaningful segments, and can be readily used to localize objects in a scene. By clustering the features associated with these segments across a dataset, we can obtain well-delineated, nameable regions.
  arXiv  Detail & Related papers  (2022-05-16T17:47:44Z)
• Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization [31.69344455448125]
  We present an attribution map of an image that is manipulated to increase the classification score produced by a classifier before the final softmax or sigmoid layer. This manipulation is realized in an anti-adversarial manner, so that the original image is perturbed along pixel gradients in directions opposite to those used in an adversarial attack. In addition, we introduce a new regularization procedure that inhibits the incorrect attribution of regions unrelated to the target object and the excessive concentration of attributions on a small region of the target object.
  arXiv  Detail & Related papers  (2022-04-11T06:18:02Z)
• Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation [45.39679291105364]
  We propose a region network RPNet to explore the cross-image object diversity of the training set. Similar object parts across images are identified via region feature comparison. Experiments show that the proposed method generates more complete and accurate pseudo object masks.
  arXiv  Detail & Related papers  (2021-08-17T02:51:02Z)
• Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation [27.50216933606052]
  We address the problem of weakly-supervised semantic segmentation using bounding box annotations. Background regions are perceptually consistent in part within an image, and this can be leveraged to discriminate foreground and background regions inside object bounding boxes. We introduce a noise-aware loss (NAL) that makes the networks less susceptible to incorrect labels.
  arXiv  Detail & Related papers  (2021-04-02T06:38:41Z)
• Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation [24.4472594401663]
  AdvCAM is an attribution map of an image that is manipulated to increase the classification score. It forces regions initially considered not to be discriminative to become involved in subsequent classifications. We achieve mIoUs of 68.0 and 76.9 for weakly and semi-supervised semantic segmentation respectively.
  arXiv  Detail & Related papers  (2021-03-16T07:39:06Z)
• Weakly-Supervised Semantic Segmentation via Sub-category Exploration [73.03956876752868]
  We propose a simple yet effective approach to enforce the network to pay attention to other parts of an object. Specifically, we perform clustering on image features to generate pseudo sub-categories labels within each annotated parent class. We conduct extensive analysis to validate the proposed method and show that our approach performs favorably against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-08-03T20:48:31Z)
• Multi-Miner: Object-Adaptive Region Mining for Weakly-Supervised Semantic Segmentation [96.85164033097524]
  Object region mining is a critical step for weakly-supervised semantic segmentation. We propose a novel multi-miner framework to perform a region mining process that adapts to diverse object sizes. Experiment results demonstrate that the multi-miner offers better region mining results and helps achieve better segmentation performance.
  arXiv  Detail & Related papers  (2020-06-14T08:00:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.