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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PVT-Small+Trans2Seg performs better than ResNet50-d16+Trans2Seg (mIoU: 42.6 vs 39.7, GFLOPs: 31.6 vs 79.3).
“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]
Section 5.1 identifies a dilated ResNet-50 as Trans2Seg’s CNN extractor, but the passages found do not compare it with PVT-Small. A search for both model names and the stated mIoU and GFLOPs values found no such comparison in our copy. (LLM, conf=0.75)
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Vision Transformer Computation and Resilience for Dynamic Inference
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- 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 · 2020: 1
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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