MTLDesc: Looking Wider to Describe Better
Open accessAbstract
Limited by the locality of convolutional neural networks, most existing local features description methods only learn local descriptors with local information and lack awareness of global and surrounding spatial context. In this work, we focus on making local descriptors ``look wider to describe better'' by learning local Descriptors with More Than Local information (MTLDesc). Specifically, we resort to context augmentation and spatial attention mechanism to make the descriptors obtain non-local awareness. First, Adaptive Global Context Augmented Module and Diverse Local Context Augmented Module are proposed to construct robust local descriptors with context information from global to local. Second, we propose the Consistent Attention Weighted Triplet Loss to leverage spatial attention awareness in both optimization and matching of local descriptors. Third, Local Features Detection with Feature Pyramid is proposed to obtain more stable and accurate keypoints localization. With the above innovations, the performance of the proposed MTLDesc significantly surpasses the current state-of-the-art local descriptors on HPatches, Aachen Day-Night localization and InLoc indoor localization benchmarks. Our code is available at https://github.com/vignywang/MTLDesc.
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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 [136]
The Method (p. 2) describes MTLDesc as a convolutional network for keypoint detection and local descriptors, not a neural field. Searches for NeRF, Semantic-NeRF, implicit representations, and multimodal fusion found no passage in our copy addressing the attributed claim. (LLM, conf=0.76)
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- Authors
- Wang, C., Xu, R., Zhang, Y., Xu, S., Meng, W., Fan, B., & Zhang, X.
- Affiliations
- Chinese Academy of Sciences (CN)
- Beijing University of Posts and Telecommunications (CN)
- University of Science and Technology Beijing (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2022-06-28
- Volume
- 36
- Issue
- 2
- Pages
- 2388-2396
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4283806071
- Cited by
- 33
- References
- 46
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 10 · 2023: 13 · 2022: 3
- FWCI
- 2.90
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (2021-05, 62162044, 61771026, 61971418, 62071157, 61620106003, 62171321, U2003109)
- Chinese Academy of Sciences (LSU-KFJJ-2021-05)
- 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 › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- local descriptors, spatial attention, context augmentation, triplet loss, feature pyramid, keypoint detection, image matching, visual localization, indoor positioning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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