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Found 9 papers, 4 papers with code
Small, Versatile and Mighty: A Range-View Perception Framework
Our proposed Small, Versatile, and Mighty (SVM) network utilizes a pure convolutional architecture to fully unleash the efficiency and multi-tasking potentials of the range view representation.
Curricular Object Manipulation in LiDAR-based Object Detection
For the loss design, we propose the COMLoss to dynamically predict object-level difficulties and emphasize objects of different difficulties based on training stages.
Towards Privacy-Preserving, Real-Time and Lossless Feature Matching
Given limited public projects in this field, codes of our method and implemented baselines are made open-source in https://github. com/IrvingMeng/SecureVector.
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters
The growing public concerns on data privacy in face recognition can be greatly addressed by the federated learning (FL) paradigm.
Basket-based Softmax
Experimentally, we demonstrate the efficiency and superiority of the BBS on the tasks of face recognition and re-identification, with both simulated and real-world datasets.
Learning Compatible Embeddings
Achieving backward compatibility when rolling out new models can highly reduce costs or even bypass feature re-encoding of existing gallery images for in-production visual retrieval systems.
PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition
Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e. g., in cases of surveillance and photo-tagging).
MagFace: A Universal Representation for Face Recognition and Quality Assessment
This paper proposes MagFace, a category of losses that learn a universal feature embedding whose magnitude can measure the quality of the given face.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
Searching for Alignment in Face Recognition
A standard pipeline of current face recognition frameworks consists of four individual steps: locating a face with a rough bounding box and several fiducial landmarks, aligning the face image using a pre-defined template, extracting representations and comparing.
