The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Segmenting Transparent Objects in the Wild with Transformer
Open accessAbstract
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset. Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained categories of transparent objects, commonly occurring in the human domestic environment, making it more practical for real-world application. (2) Trans10K-v2 brings more challenges for the current advanced segmentation methods than its former version. Furthermore, a novel Transformer-based segmentation pipeline termed Trans2Seg is proposed. Firstly, the Transformer encoder of Trans2Seg provides the global receptive field in contrast to CNN's local receptive field, which shows excellent advantages over pure CNN architectures. Secondly, by formulating semantic segmentation as a problem of dictionary look-up, we design a set of learnable prototypes as the query of Trans2Seg's Transformer decoder, where each prototype learns the statistics of one category in the whole dataset. We benchmark more than 20 recent semantic segmentation methods, demonstrating that Trans2Seg significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation.Code is available in https://github.com/xieenze/Trans2Seg.
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“In addition, our PVT-Small+Trans2Seg performs better than ResNet50-d16+Trans2Seg (mIoU: 42.6 vs . 39.7, GFlops: 31.6 vs . 79.3).”
From Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (2021) · cites this paper as [72]
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Cites
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Research works printed but not linked: 2
- Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. Convolu- tional sequence to sequence learning. arXiv, 2017.
- Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, et al. A survey on visual transformer. arXiv, 2020.
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Items 101–112 of 112
Internal-external boundary attention fusion for glass surface segmentation
Han, D., Yoon, H., Kwon, H., Kim, H.-C., Choo, H., Lee, S., & Zhang, C. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108232
A YOLOv5 Model with Hybrid Attention Mechanism for Transparent Product Detection in E-Commerce
张, 伟. (2025) · E-Commerce Letters · doi:10.12677/ecl.2025.14103364
Qian, Y., Chen, H., Lu, D., Yang, L., Jiang, X., & Qin, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229101
Fast Image Segmentation Toward Automation of 3D Ice Printing
Garcia, A., Garg, A., Yang, F., Özdoğanlar, O. B., & LeDuc, P. R. (2026) · Chemical & Biomedical Imaging · doi:10.1021/cbmi.5c00064
Efficient and Integrated Panoramic Glass Precision Sorting Network
黄智鸿 (2026) · Computer Science and Application · doi:10.12677/csa.2026.162061
SeeClear: Reliable Transparent Object Depth Estimation via Generative Opacification
Wang, X., He, Y., Shi, J., Lu, J., Yang, Y., Jiang, Y., & Jiang, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37211-6_5
Jeon, J., 이석한, & Wallraven, C. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461633
DepthShield: Robust Depth Estimation on Transparent or Mirror Surfaces
Zhu, W., Zhang, Y., Lan, L., Che, Y., & Xiao, C. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463855
Li, D., Dong, Z., Yang, Z., Li, K.-K., & Huang, Y. (2026) · Journal of Imaging · doi:10.3390/jimaging12050212
Kong, V., Yoon, S. T., Won, J.-H., & Kim, T.-K. (2026) · IEEE Access · doi:10.1109/access.2026.3734643
Joint Contour and Scene Adaptation for Transparent Reagent Bottle Detection
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Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., & Wang, Y. (2021) · arXiv · doi:10.48550/arxiv.2103.00112 · Cited by 1,394
Metadata
- Authors
- Xie, E., Wang, W., Wang, W., Sun, P., Xu, H., Liang, D., & Luo, P.
- Affiliations
- University of Hong Kong (HK)
- Sensetime (China) (CN)
- Huawei Technologies (China) (CN)
- Venue
- Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
- Published
- 2021-08
- Pages
- 1194-1200
- OpenAlex
- W3189898414
- Cited by
- 111
- References
- 26
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 31 · 2023: 23 · 2022: 23 · 2021: 10
- FWCI
- 7.11
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- semantic segmentation, Transformer encoder, dictionary lookup, global receptive field, fine-grained segmentation
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