Jun Yu

TL;DR: A semi-supervised distance metric learning method by exploring feature correlations using unlabeled samples to calculate the prediction error by means of local linear regression and fuse the knowledge learned from both labeled and unlabeling samples into an overall objective function which can be solved by maximum eigenvectors.

TL;DR: The results clearly demonstrate that the use of Random Forests-based feature selection can improve the classification performance by the selected features.

TL;DR: By learning the tree classifier, the deep network and the visual hierarchy adaptation jointly in an end-to-end manner, the LMM algorithm can achieve higher accuracy rates on hierarchical visual recognition.

TL;DR: This work presents an effective weakly-supervised model, named as Multi-Level Attentional Reconstruction Network (MARN), which only relies on video-sentence pairs during the training stage and develops a novel proposal sampling mechanism to leverage intra-proposal information for learning better proposal representation and adopt 2D convolution to exploit inter-pro proposal clues for learning reliable attention map.

TL;DR: A subspace learning algorithm based on supervised manifold learning techniques to address the problem of inferring 3D human poses from monocular video frames by obtaining a global linear projection from the embedding whereby the Euclidean distances between transformed feature vectors can faithfully reflect the corresponding pose distances.