We find that R-CNN outperforms OverFeat by a large margin on the 200-class ILSVRC2013 detection dataset.
We propose a method for human pose estimation based on Deep Neural Networks (DNNs).
This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images. It has higher learning capability than models based on hand-crafted features.
In the former, our AOGTracker outperforms state-of-the-art tracking algorithms including two trackers based on deep convolutional network.
We revisit a pioneer unsupervised learning technique called archetypal analysis, which is related to successful data analysis methods such as sparse coding and non-negative matrix factorization.
We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulation and occlusion.
Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012).
Based on t-SVD, the notion of multilinear rank and a related tensor nuclear norm was proposed in  to characterize informational and structural complexity of multilinear data.