The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
CRIS: CLIP-Driven Referring Image Segmentation
Abstract
Referring image segmentation aims to segment a referent via a natural linguistic expression. Due to the distinct data properties between text and image, it is challenging for a network to well align text and pixel-level features. Existing approaches use pretrained models to facilitate learning, yet separately transfer the language/vision knowledge from pretrained models, ignoring the multi-modal corresponding information. Inspired by the recent advance in Contrastive Language-Image Pretraining (CLIP), in this paper, we propose an end-to-end CLIP-Driven Referring Image Segmen-tation framework (CRIS). To transfer the multi-modal knowledge effectively, CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. More specifically, we design a vision-language decoder to propagate fine-grained semantic information from textual representations to each pixel-level activation, which promotes consistency between the two modalities. In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances. The experimental results on three benchmark datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.
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Cites
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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Fully convolutional networks for semantic segmentation
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End-to-End Object Detection with Transformers
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Cited by
LISA: Reasoning Segmentation via Large Language Model
Lai, X., Tian, Z., Chen, Y., Li, Y., Yuan, Y., Liu, S., & Jia, J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00915 · Cited by 466
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Liu, J., Zhang, Y., Chen, J.-N., Xiao, J., Lu, Y., Landman, B. A., Yuan, Y., Yuille, A., Tang, Y., & Zhou, Z. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01934 · Cited by 333
Self-citationFoundation Models Defining a New Era in Vision: A Survey and Outlook
Awais, M., Naseer, M., Khan, S., Anwer, R. M., Cholakkal, H., Shah, M., Yang, M.-H., & Khan, F. S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3506283 · Cited by 304
RRSIS: Referring Remote Sensing Image Segmentation
Yuan, Z., Mou, L., Hua, Y., & Zhu, X. X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3369720 · Cited by 81
Zhu, M., Tian, Y., Chen, H., Zhou, C., Guo, Q., Liu, Y., Yang, M., & Shen, C. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.00349 · Cited by 5
Metadata
- Authors
- Wang, Z., Lu, Y., Li, Q., Tao, X., Guo, Y., Gong, M., & Liu, T.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 11676-11685
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3216551675
- Cited by
- 404
- References
- 52
- FWCI
- 31.29
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, Z., Lu, Y., Li, Q., Tao, X., Guo, Y., Gong, M., & Liu, T. (2022). CRIS: CLIP-Driven Referring Image Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52688.2022.01139
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