Weakly Supervised Scene Parsing with Point-based Distance Metric Learning

By Yunchao Wei, Humphrey Shi, Jiachen Li, Jiaying Liu, Thomas S. Huang. 2018

83 citations recorded by the source corpus.

Semantic scene parsing is suffering from the fact that pixellevel annotations are hard to be collected. To tackle this issue, we propose a Point-based Distance Metric Learning (PDML) in this paper. PDML does not require dense annotated masks and only leverages several labeled points that are much easier to obtain to guide the training process. Concretely, we leverage semantic relationship among the annotated points by encouraging the feature representations of the intra- and intercategory points to keep consistent, i.e. points within the same category should have more similar feature representations compared to those from different categories. We formulate such a characteristic into a simple distance metric loss, which collaborates with the point-wise cross-entropy loss to optimize the deep neural networks. Furthermore, to fully exploit the limited annotations, distance metric learning is conducted across different training images instead of simply adopting an image-dependent manner. We conduct extensive experiments on two challenging scene parsing benchmarks of PASCALContext and ADE 20K to validate the effectiveness of our PDML, and competitive mIoU scores are achieved.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

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].”
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation (2018, methodology): “For example, previous works have implemented image-level labels [4]–[6]; scribbles [7]–[9]; bounding boxes [10], [11]; and points [12]–[14]”
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision (2021, methodology): “A number of methods have been proposed to learn semantic segmentation with image-level class labels [69–73], points [74,75], scribbles [76–79] and bounding boxes [46, 80–83].”
Interactive Object Segmentation With Inside-Outside Guidance (2020, methodology): “Particularly, image-level labels [50, 61, 63, 62, 27, 32], points [3, 51], bounding boxes [18, 33] and scribbles [39, 58, 59] have been employed as guidance to supervise the training of semantic segmentation networks.”
Interactive Object Segmentation With Inside-Outside Guidance (2022, methodology): “Particularly, image-level labels [67], [68], [69], [70], [71], [72], points [73], [74], bounding boxes [75], [76], scribbles [77], [78], [79] have been employed as guidance to supervise the training of semantic segmentation networks.”
Pointly-Supervised Instance Segmentation (2021, background): “Point-based supervision has been studied in a variety of tasks, including action localization [37, 38], object detection [42, 46], object counting [27], and semantic segmentation [4, 44].”
AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem? (2020, background): “erable research has been done in the computer vision community for image segmentation for different weak annotations, such as bounding boxes [70], points [71], [72], scribbles [73], [74], image-level labels [75], [76], [77].”