Working Hard To Know Your Neighbor's Margins: Local Descriptor Learning Loss

Abstract

We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT. We show that the proposed loss that maximizes the distance between the closest positive and closest negative patch in the batch is better than complex regularization methods; it works well for both shallow and deep convolution network architectures. Applying the novel loss to the L2Net CNN architecture results in a compact descriptor -- it has the same dimensionality as SIFT (128) that shows state-of-art performance in wide baseline stereo, patch verification and instance retrieval benchmarks. It is fast, computing a descriptor takes about 1 millisecond on a low-end GPU.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keymishchuk2017working

Related papers

• Robust Angular Local Descriptor Learning (2019)3.58
• Hynet: Learning Local Descriptor With Hybrid Similarity Measure And Triplet Loss (2020)0.00
• Pn-net: Conjoined Triple Deep Network For Learning Local Image Descriptors (2016)0.00
• Learning And Aggregating Deep Local Descriptors For Instance-level Recognition (2020)13.88
• Learning Local Descriptors By Optimizing The Keypoint-correspondence Criterion: Applications To Face Matching, Learning From Unlabeled Videos And 3d-shape Retrieval (2016)11.75
• Learning-based Dimensionality Reduction For Computing Compact And Effective Local Feature Descriptors (2022)9.99
• Local Feature Descriptor Learning With Adaptive Siamese Network (2017)0.00
• Digging Into Self-supervised Learning Of Feature Descriptors (2021)7.16