Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

http://masou-keshtkar.persiangig.com/document/sdarticle.pdf

GSA: A Gravitational Search Algorithm

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

On robust estimation of the location parameter

The PASCAL Visual Object Classes Challenge

While being the de facto standard coordinate representation for human pose estimation, heatmap has not been investigated in-depth. This work fills this gap. For the first time, we find that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is surprisingly significant for the performance. We further probe the design limitations of the standard coordinate decoding method, and propose a more principled distributionaware decoding method. Also, we improve the standard coordinate encoding process (i.e. transforming ground-truth coordinates to heatmaps) by generating unbiased/accurate heatmaps. Taking the two together, we formulate a novel Distribution-Aware coordinate Representation of Keypoints (DARK) method. Serving as a model-agnostic plug-in, DARK brings about significant performance boost to existing human pose estimation models. Extensive experiments show that DARK yields the best results on two common benchmarks, MPII and COCO. Besides, DARK achieves the 2nd place entry in the ICCV 2019 COCO Keypoints Challenge. The code is available online.

/pdf/distribution-aware-coordinate-representation-for-human-pose-il0y82t3zg.pdf

Distribution-Aware Coordinate Representation for Human Pose Estimation

Existing human pose estimation approaches often only consider how to improve the model generalisation performance, but putting aside the significant efficiency problem. This leads to the development of heavy models with poor scalability and cost-effectiveness in practical use. In this work, we investigate the under-studied but practically critical pose model efficiency problem. To this end, we present a new Fast Pose Distillation (FPD) model learning strategy. Specifically, the FPD trains a lightweight pose neural network architecture capable of executing rapidly with low computational cost. It is achieved by effectively transferring the pose structure knowledge of a strong teacher network. Extensive evaluations demonstrate the advantages of our FPD method over a broad range of state-of-the-art pose estimation approaches in terms of model cost-effectiveness on two standard benchmark datasets, MPII Human Pose and Leeds Sports Pose.

/pdf/fast-human-pose-estimation-1lyrvca47n.pdf

Fast Human Pose Estimation

Fast crowd density estimation with convolutional neural networks

A novel algorithm of document clustering based on non-negative sparse analysis is proposed. In contrast to the algorithm based on non-negative matrix factorization, our algorithm can obtain documents topics exactly by controlling the sparseness of the topic matrix and the encoding matrix explicitly. Thus, the clustering accuracy has been improved greatly. In the end, simulation results are employed to further illustrate the accuracy and efficiency of this algorithm.

Mao Ye

Papers

Distribution-Aware Coordinate Representation for Human Pose Estimation

Fast Human Pose Estimation

Fast crowd density estimation with convolutional neural networks

Document clustering based on nonnegative sparse matrix factorization

Age invariant face recognition and retrieval by coupled auto-encoder networks