Fully Convolutional Instance-Aware Semantic Segmentation
Open accessAbstract
We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. It inherits all the merits of FCNs for semantic segmentation [29] and instance mask proposal [5]. It performs instance mask prediction and classification jointly. The underlying convolutional representation is fully shared between the two sub-tasks, as well as between all regions of interest. The network architecture is highly integrated and efficient. It achieves state-of-the-art performance in both accuracy and efficiency. It wins the COCO 2016 segmentation competition by a large margin. Code would be released at https://github.com/daijifeng001/TA-FCN.
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The Hungarian algorithm enforces permutation-invariance and guarantees that each target element has a unique match.
“The usual solution is to design a loss based on the Hungarian algorithm [20], to find a bipartite matching between ground-truth and prediction. This enforces permutation-invariance, and guarantees that each target element has a unique match.”
From End-to-End Object Detection with Transformers (2020) · cites this paper as [20]
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- Authors
- Li, Y., Qi, H., Dai, J., Ji, X., & Wei, Y.
- Affiliations
- Microsoft Research Asia (China) (CN)
- Tsinghua University (CN)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 4438-4446
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2555182955
- Cited by
- 1,116
- References
- 44
- Citations by year
- 2026: 11 · 2025: 38 · 2024: 75 · 2023: 117 · 2022: 146 · 2021: 219 · 2020: 180 · 2019: 182 · 2018: 116 · 2017: 32
- FWCI
- 40.77
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Microsoft Research
- 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 › Medical Image Segmentation Techniques
- Keywords
- fully convolutional network, instance classification, joint segmentation and classification, region of interest
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