Proceedings

Local Representation is Not Enough: Soft Point-Wise Transformer for Descriptor and Detector of Local Features

Zihao Wang, Xueyi Li, Zhen Li

Download BibTeX Cite

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 1150-1156.

https://doi.org/10.24963/ijcai.2021/159

Keywords

  • Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation
  • Computer Vision: 2D and 3D Computer Vision

Abstract

Significant progress has been witnessed for the descriptor and detector of local features, but there still exist several challenging and intractable limitations, such as insufficient localization accuracy and non-discriminative description, especially in repetitive- or blank-texture regions, which haven't be well addressed. The coarse feature representation and limited receptive field are considered as the main issues for these limitations. To address these issues, we propose a novel Soft Point-Wise Transformer for Descriptor and Detector, simultaneously mining long-range intrinsic and cross-scale dependencies of local features. Furthermore, our model leverages the distinct transformers based on the soft point-wise attention, substantially decreasing the memory and computation complexity, especially for high-resolution feature maps. In addition, multi-level decoder is constructed to guarantee the high detection accuracy and discriminative description. Extensive experiments demonstrate that our model outperforms the existing state-of-the-art methods on the image matching and visual localization benchmarks.

Zihao Wang, Xueyi Li, and Zhen Li. 2021. Local Representation is Not Enough: Soft Point-Wise Transformer for Descriptor and Detector of Local Features. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 1150–1156. https://doi.org/10.24963/ijcai.2021/159
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards