Kai Jin

TL;DR: A deep regression network termed DepressNet is presented to learn a depression representation with visual explanation, with results showing that the DAM induced by the learned deep model may help reveal the visual depression pattern on faces and understand the insights of automated depression diagnosis.

TL;DR: This work proposes a new kinship metric learning (KML) method with a coupled deep neural network (DNN) model, and introduces the property of hierarchical compactness into the coupled network to facilitate deep metric learning with limited amount of kinship training data.

TL;DR: This paper introduces the constraints of inter-frame temporal smoothness and within-frame identity exclusivity on multiple faces in videos, and model the tasks of multiple face recognition (MFR) and multiple face tracking (MFT) jointly in an alternative optimization framework and shows this joint formulation for two different tasks leads to significantly improved MFR accuracy.

TL;DR: This paper proposes a novel approach namely SwiniPASSR, which adopts Swin Transformer as the backbone, meanwhile incorporating it with the Bi-directional Parallax Attention Module (biPAM) to maximize auxiliary information given by the binocular mechanism.

TL;DR: This paper presents a deep metric learning method with a carefully designed two-stream neural network to jointly learn a pair of deep embeddings for parent-child images, and shows improved performance over state of the art metric learning solutions to general kinship verification on two benchmarks.