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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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
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End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
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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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Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00061 · Cited by 5,259
Self-citationSegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021) · arXiv · doi:10.48550/arxiv.2105.15203 · Cited by 5,090
Self-citationSegment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
Ji, W., Li, J., Bi, Q., Liu, T., Li, W., & Cheng, L. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1385-0 · Cited by 284
Jia, M., Cheng, X., Lu, S., & Zhang, J. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3141267 · Cited by 225
A Survey of Techniques for Optimizing Transformer Inference
Chitty-Venkata, K. T., Mittal, S., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102990 · Cited by 154
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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