Knowledge Consultation for Semi-Supervised Semantic Segmentation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917956660756480 |
|---|---|
| author | Than, Thuan Nguyen-Dang, Nhat-Anh Nguyen, Dung Khatib, Salwa K. Al Elhagry, Ahmed Phan, Hai He, Yihui Shen, Zhiqiang Savvides, Marios Huynh, Dang |
| author_facet | Than, Thuan Nguyen-Dang, Nhat-Anh Nguyen, Dung Khatib, Salwa K. Al Elhagry, Ahmed Phan, Hai He, Yihui Shen, Zhiqiang Savvides, Marios Huynh, Dang |
| contents | Semi-Supervised Semantic Segmentation reduces reliance on extensive annotations by using unlabeled data and state-of-the-art models to improve overall performance. Despite the success of deep co-training methods, their underlying mechanisms remain underexplored. This work revisits Cross Pseudo Supervision with dual heterogeneous backbones and introduces Knowledge Consultation (SegKC) to further enhance segmentation performance. The proposed SegKC achieves significant improvements on Pascal and Cityscapes benchmarks, with mIoU scores of 87.1%, 89.2%, and 89.8% on Pascal VOC with the 1/4, 1/2, and full split partition, respectively, while maintaining a compact model architecture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10693 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge Consultation for Semi-Supervised Semantic Segmentation Than, Thuan Nguyen-Dang, Nhat-Anh Nguyen, Dung Khatib, Salwa K. Al Elhagry, Ahmed Phan, Hai He, Yihui Shen, Zhiqiang Savvides, Marios Huynh, Dang Computer Vision and Pattern Recognition Image and Video Processing Semi-Supervised Semantic Segmentation reduces reliance on extensive annotations by using unlabeled data and state-of-the-art models to improve overall performance. Despite the success of deep co-training methods, their underlying mechanisms remain underexplored. This work revisits Cross Pseudo Supervision with dual heterogeneous backbones and introduces Knowledge Consultation (SegKC) to further enhance segmentation performance. The proposed SegKC achieves significant improvements on Pascal and Cityscapes benchmarks, with mIoU scores of 87.1%, 89.2%, and 89.8% on Pascal VOC with the 1/4, 1/2, and full split partition, respectively, while maintaining a compact model architecture. |
| title | Knowledge Consultation for Semi-Supervised Semantic Segmentation |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2503.10693 |