Attention Weighted Local Descriptors
Abstract
Local features detection and description are widely used in many vision applications with high industrial and commercial demands. With large-scale applications, these tasks raise high expectations for both the accuracy and speed of local features. Most existing studies on local features learning focus on the local descriptions of individual keypoints, which neglect their relationships established from global spatial awareness. In this paper, we present AWDesc with a consistent attention mechanism (CoAM) that opens up the possibility for local descriptors to embrace image-level spatial awareness in both the training and matching stages. For local features detection, we adopt local features detection with feature pyramid to obtain more stable and accurate keypoints localization. For local features description, we provide two versions of AWDesc to cope with different accuracy and speed requirements. On the one hand, we introduce Context Augmentation to address the inherent locality of convolutional neural networks by injecting non-local context information, so that local descriptors can "look wider to describe better". Specifically, well-designed Adaptive Global Context Augmented Module (AGCA) and Diverse Surrounding Context Augmented Module (DSCA) are proposed to construct robust local descriptors with context information from global to surrounding. On the other hand, we design an extremely lightweight backbone network coupled with the proposed special knowledge distillation strategy to achieve the best trade-off in accuracy and speed. What is more, we perform thorough experiments on image matching, homography estimation, visual localization, and 3D reconstruction tasks, and the results demonstrate that our method surpasses the current state-of-the-art local descriptors. Code is available at: https://github.com/vignywang/AWDesc.
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NeRF and Semantic-NeRF encode scene geometry and semantics into compact neural fields, enabling robust localization in new environments and showing multimodal fusion potential for perception and mapping.
“Neural implicit representations, like NeRF [135] and Semantic-NeRF [136], encode scene geometry and semantics into compact neural fields, enabling robust localization in new environments and showcasing the potential of multimodal fusion for bridging perception and mapping.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [135]
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Multimodal fusion and vision–language models: A survey for robot vision
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“These methods leverage complementary sensory inputs such as RGB images, depth data, and inertial measurements to address challenges like sensor noise, dynamic scenes, and varying lighting conditions [135, 136, 137].”
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- Authors
- Wang, C., Xu, R., Lu, K., Xu, S., Meng, W., Zhang, Y., Fan, B., & Zhang, X.
- Affiliations
- Chinese Academy of Sciences (CN)
- School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China
- Beijing University of Posts and Telecommunications (CN)
- Thunder Software Technology Company, Ltd., Chengdu, China
- University of Science and Technology Beijing (CN)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2023-09-01
- Volume
- 45
- Issue
- 9
- Pages
- 10632-10649
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W4366351734
- Cited by
- 36
- References
- 80
- Citations by year
- 2026: 6 · 2025: 18 · 2024: 10 · 2023: 2
- FWCI
- 3.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (U21A20515, 62032022, 62271074, 62222302, U2003109, 62171321, 62071157, 62162044)
- Open Research Fund of Key Laboratory of Space Utilization, Chinese Academy of Sciences (LSU-KFJJ-2021-05)
- National Laboratory of Pattern Recognition
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- local descriptors, local feature detection, attention mechanism, spatial awareness, context augmentation, feature pyramid, lightweight backbone, knowledge distillation, image matching, homography estimation, visual localization, 3D reconstruction
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