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Found 6 papers, 1 papers with code
ICD-Face: Intra-class Compactness Distillation for Face Recognition
Knowledge distillation is an effective model compression method to improve the performance of a lightweight student model by transferring the knowledge of a well-performed teacher model, which has been widely adopted in many computer vision tasks, including face recognition (FR).
CoupleFace: Relation Matters for Face Recognition Distillation
In this work, we observe that mutual relation knowledge between samples is also important to improve the discriminative ability of the learned representation of the student model, and propose an effective face recognition distillation method called CoupleFace by additionally introducing the Mutual Relation Distillation (MRD) into existing distillation framework.
GLPanoDepth: Global-to-Local Panoramic Depth Estimation
In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image.
Ranked #7 on
Depth Estimation
on Stanford2D3D Panoramic
Analogical Reasoning for Visually Grounded Compositional Generalization
Children acquire language subconsciously by observing the surrounding world and listening to descriptions.
Analogical Reasoning for Visually Grounded Language Acquisition
Children acquire language subconsciously by observing the surrounding world and listening to descriptions.
Asymmetric Rejection Loss for Fairer Face Recognition
In this paper, we propose an Asymmetric Rejection Loss, which aims at making full use of unlabeled images of those under-represented groups, to reduce the racial bias of face recognition models.
