Not All Pixels are Equal: Learning Pixel Hardness for Semantic Segmentation.
Saved in:
| Title: | Not All Pixels are Equal: Learning Pixel Hardness for Semantic Segmentation. |
|---|---|
| Authors: | Xiao, Xin1 (AUTHOR) xinxiao@whu.edu.cn, Zhou, Daiguo2 (AUTHOR) zhoudaiguo@xiaomi.com, Hu, Jiagao2 (AUTHOR) hujiagao@xiaomi.com, Hu, Yi3 (AUTHOR) huyi@whmit.cn, Xu, Yongchao1 (AUTHOR) yongchao.xu@whu.edu.cn |
| Source: | International Journal of Computer Vision. Jul2025, Vol. 133 Issue 7, p4669-4689. 21p. |
| Subjects: | Direct costing, Source code, Values (Ethics), Hardness, Whistles |
| Abstract: | Semantic segmentation has witnessed great progress. Despite the impressive overall results, the segmentation performance in some hard areas (e.g., small objects or thin parts) is still not promising. A straightforward solution is hard sample mining. Yet, most existing hard pixel mining strategies for semantic segmentation often rely on pixel's loss value, which tends to decrease during training. Intuitively, the pixel hardness for segmentation mainly depends on image structure and is expected to be stable. In this paper, we propose to learn pixel hardness for semantic segmentation by leveraging hardness information contained in global and historical loss values. More precisely, we add a gradient-independent branch for learning a hardness level (HL) map by maximizing hardness-weighted segmentation loss, which is minimized for the segmentation head. This encourages large hardness values in difficult areas, leading to appropriate and stable HL map. Despite its simplicity, the proposed method can be applied to most segmentation methods with no and marginal extra cost during inference and training, respectively. Without bells and whistles, the proposed method achieves consistent improvement (1.37% mIoU on average) over most popular semantic segmentation methods on the Cityscapes dataset, and demonstrates good generalization ability across domains. The source codes are available at this link. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer Vision is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 185781657 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Not All Pixels are Equal: Learning Pixel Hardness for Semantic Segmentation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiao%2C+Xin%22">Xiao, Xin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xinxiao@whu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Daiguo%22">Zhou, Daiguo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zhoudaiguo@xiaomi.com</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Jiagao%22">Hu, Jiagao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hujiagao@xiaomi.com</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Yi%22">Hu, Yi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> huyi@whmit.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Yongchao%22">Xu, Yongchao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yongchao.xu@whu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer+Vision%22">International Journal of Computer Vision</searchLink>. Jul2025, Vol. 133 Issue 7, p4669-4689. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Direct+costing%22">Direct costing</searchLink><br /><searchLink fieldCode="DE" term="%22Source+code%22">Source code</searchLink><br /><searchLink fieldCode="DE" term="%22Values+%28Ethics%29%22">Values (Ethics)</searchLink><br /><searchLink fieldCode="DE" term="%22Hardness%22">Hardness</searchLink><br /><searchLink fieldCode="DE" term="%22Whistles%22">Whistles</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Semantic segmentation has witnessed great progress. Despite the impressive overall results, the segmentation performance in some hard areas (e.g., small objects or thin parts) is still not promising. A straightforward solution is hard sample mining. Yet, most existing hard pixel mining strategies for semantic segmentation often rely on pixel's loss value, which tends to decrease during training. Intuitively, the pixel hardness for segmentation mainly depends on image structure and is expected to be stable. In this paper, we propose to learn pixel hardness for semantic segmentation by leveraging hardness information contained in global and historical loss values. More precisely, we add a gradient-independent branch for learning a hardness level (HL) map by maximizing hardness-weighted segmentation loss, which is minimized for the segmentation head. This encourages large hardness values in difficult areas, leading to appropriate and stable HL map. Despite its simplicity, the proposed method can be applied to most segmentation methods with no and marginal extra cost during inference and training, respectively. Without bells and whistles, the proposed method achieves consistent improvement (1.37% mIoU on average) over most popular semantic segmentation methods on the Cityscapes dataset, and demonstrates good generalization ability across domains. The source codes are available at this link. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Computer Vision is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=185781657 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11263-025-02416-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 4669 Subjects: – SubjectFull: Direct costing Type: general – SubjectFull: Source code Type: general – SubjectFull: Values (Ethics) Type: general – SubjectFull: Hardness Type: general – SubjectFull: Whistles Type: general Titles: – TitleFull: Not All Pixels are Equal: Learning Pixel Hardness for Semantic Segmentation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiao, Xin – PersonEntity: Name: NameFull: Zhou, Daiguo – PersonEntity: Name: NameFull: Hu, Jiagao – PersonEntity: Name: NameFull: Hu, Yi – PersonEntity: Name: NameFull: Xu, Yongchao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09205691 Numbering: – Type: volume Value: 133 – Type: issue Value: 7 Titles: – TitleFull: International Journal of Computer Vision Type: main |
| ResultId | 1 |
