The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
Open accessAbstract
Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image. One of the key challenges behind this task is leveraging the referring expression for highlighting relevant positions in the image. A paradigm for tackling this problem is to leverage a powerful vision-language (“cross-madal”) decoder to fuse features independently extracted from a vision encoder and a language encoder. Recent methods have made remarkable advancements in this paradigm by exploiting Transformers as cross-modal decoders, concurrent to the Transformer's overwhelming success in many other vision-language tasks. Adopting a different approach in this work, we show that significantly better cross-modal alignments can be achieved through the early fusion of linguistic and visual features in intermediate layers of a vision Transformer encoder network. By conducting cross-modal feature fusion in the visual feature encoding stage, we can leverage the well-proven correlation modeling power of a Transformer encoder for excavating helpful multi-modal context. This way, accurate segmentation results are readily harvested with a light-weight mask predictor. Without bells and whistles, our method surpasses the previous state-of-the-art methods on Ref CoCo, RefCOCO+, and G-Ref by large margins.
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Cites
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Cited by
DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting
Rao, Y., Zhao, W., Chen, G., Tang, Y., Zhu, Z., Huang, G., Zhou, J., & Lu, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01755 · Cited by 631
Self-citationRRSIS: 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
- Yang, Z., Wang, J., Tang, Y., Chen, K., Zhao, H., & Torr, P. H. S.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 18134-18144
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4200631575
- Cited by
- 406
- References
- 57
- FWCI
- 29.55
- Type
- conference paper
- Language
- en
- Open access
- green
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- Yang, Z., Wang, J., Tang, Y., Chen, K., Zhao, H., & Torr, P. H. S. (2022). LAVT: Language-Aware Vision Transformer for Referring Image Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52688.2022.01762
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