Recovering 3D human pose from 2D joints is a highly unconstrained problem. We
propose a novel neural network framework, PoseNet3D, that takes 2D joints as
input and outputs 3D skeletons and SMPL body model parameters. By casting our
learning approach in a student-teacher framework, we avoid using any 3D data
such as paired/unpaired 3D data, motion capture sequences, depth images or
multi-view images during training. We first train a teacher network that
outputs 3D skeletons, using only 2D poses for training. The teacher network
distills its knowledge to a student network that predicts 3D pose in SMPL
representation. Finally, both the teacher and the student networks are jointly
fine-tuned in an end-to-end manner using temporal, self-consistency and
adversarial losses, improving the accuracy of each individual network. Results
on Human3.6M dataset for 3D human pose estimation demonstrate that our approach
reduces the 3D joint prediction error by 18% compared to previous unsupervised
methods. Qualitative results on in-the-wild datasets show that the recovered 3D
poses and meshes are natural, realistic, and flow smoothly over consecutive
frames.

提出使用神经网络框架 PoseNet3D 将二维关节作为输入，输出三维骨架和 SMPL 体模型参数，通过在学生 - 教师框架下的学习方法，无需使用任何 3D 数据进行训练，结果表明与之前的无监督方法相比，该方法将 3D 关节预测误差降低了 18％。

PoseNet3D: 通过知识蒸馏学习具有时间一致性的三维人体姿势

PoseNet3D: Learning Temporally Consistent 3D Human Pose via Knowledge  Distillation

Due to the prevalence of social media websites, one challenge facing computer
vision researchers is to devise methods to process and search for persons of
interest among the billions of shared photos on these websites. Facebook
revealed in a 2013 white paper that its users have uploaded more than 250
billion photos, and are uploading 350 million new photos each day. Due to this
humongous amount of data, large-scale face search for mining web images is both
important and challenging. Despite significant progress in face recognition,
searching a large collection of unconstrained face images has not been
adequately addressed. To address this challenge, we propose a face search
system which combines a fast search procedure, coupled with a state-of-the-art
commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe
face, we first filter the large gallery of photos to find the top-k most
similar faces using deep features generated from a convolutional neural
network. The k candidates are re-ranked by combining similarities from deep
features and the COTS matcher. We evaluate the proposed face search system on a
gallery containing 80 million web-downloaded face images. Experimental results
demonstrate that the deep features are competitive with state-of-the-art
methods on unconstrained face recognition benchmarks (LFW and IJB-A). Further,
the proposed face search system offers an excellent trade-off between accuracy
and scalability on datasets consisting of millions of images. Additionally, in
an experiment involving searching for face images of the Tsarnaev brothers,
convicted of the Boston Marathon bombing, the proposed face search system could
find the younger brother's (Dzhokhar Tsarnaev) photo at rank 1 in 1 second on a
5M gallery and at rank 8 in 7 seconds on an 80M gallery.

本文提出了一个脸部搜索系统，它使用快速搜索程序和 COTS 匹配器相结合的级联框架，通过卷积神经网络生成的深度特征来过滤大量的照片，并在 80 百万张网络下载的人脸图像的图库上对该系统进行了评估。

大规模人脸搜索：8000 万图库

Face Search at Scale: 80 Million Gallery

Some of the most compelling applications of online convex optimization,
including online prediction and classification, are unconstrained: the natural
feasible set is R^n. Existing algorithms fail to achieve sub-linear regret in
this setting unless constraints on the comparator point x^* are known in
advance. We present algorithms that, without such prior knowledge, offer
near-optimal regret bounds with respect to any choice of x^*. In particular,
regret with respect to x^* = 0 is constant. We then prove lower bounds showing
that our guarantees are near-optimal in this setting.

本文提出了在线凸优化算法来解决无约束情况下在线预测和分类的问题，并证明了其相对于参数 x^* 几乎达到最优的遗憾界。