The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Scaling Open-Vocabulary Image Segmentation with Image-Level Labels
Open accessAbstract
We design an open-vocabulary image segmentation model to organize an image into meaningful regions indicated by arbitrary texts. Recent works (CLIP and ALIGN), despite attaining impressive open-vocabulary classification accuracy with image-level caption labels, are unable to segment visual concepts with pixels. We argue that these models miss an important step of visual grouping, which organizes pixels into groups before learning visual-semantic alignments. We propose OpenSeg to address the above issue while still making use of scalable image-level supervision of captions. First, it learns to propose segmentation masks for possible organizations. Then it learns visual-semantic alignments by aligning each word in a caption to one or a few predicted masks. We find the mask representations are the key to support learning image segmentation from captions, making it possible to scale up the dataset and vocabulary sizes. OpenSeg significantly outperforms the recent open-vocabulary method of LSeg by +19.9 mIoU on PASCAL dataset, thanks to its scalability.
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Feature Pyramid Networks for Object Detection
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Jatavallabhula, K., Kuwajerwala, A., Gu, Q., Omama, M., Iyer, G., Saryazdi, S., Chen, T., Maalouf, A., Li, S., Keetha, N., et al. (2023) · Robotics: Science and Systems XIX · doi:10.15607/rss.2023.xix.066 · Cited by 228
YOLOE: Real-time seeing anything
Wang, A., Liu, L., Chen, H., Lin, Z., Han, J., & Ding, G. (2025) · 2025 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02280 · Cited by 65
Metadata
- Authors
- Ghiasi, G., Gu, X., Cui, Y., & Lin, T.-Y.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2022
- Pages
- 540-557
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4312912313
- Cited by
- 339
- References
- 51
- FWCI
- 26.85
- Type
- conference paper
- Language
- en
- Open access
- green
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