Learning to regress 3D human body shape and pose (e.g.~SMPL parameters) from
monocular images typically exploits losses on 2D keypoints, silhouettes, and/or
part-segmentation when 3D training data is not available. Such losses, however,
are limited because 2D keypoints do not supervise body shape and segmentations
of people in clothing do not match projected minimally-clothed SMPL shapes. To
exploit richer image information about clothed people, we introduce
higher-level semantic information about clothing to penalize clothed and
non-clothed regions of the image differently. To do so, we train a body
regressor using a novel Differentiable Semantic Rendering - DSR loss. For
Minimally-Clothed regions, we define the DSR-MC loss, which encourages a tight
match between a rendered SMPL body and the minimally-clothed regions of the
image. For clothed regions, we define the DSR-C loss to encourage the rendered
SMPL body to be inside the clothing mask. To ensure end-to-end differentiable
training, we learn a semantic clothing prior for SMPL vertices from thousands
of clothed human scans. We perform extensive qualitative and quantitative
experiments to evaluate the role of clothing semantics on the accuracy of 3D
human pose and shape estimation. We outperform all previous state-of-the-art
methods on 3DPW and Human3.6M and obtain on par results on MPI-INF-3DHP. Code
and trained models are available for research at this https URL

利用 Differentiable Semantic Rendering（DSR）loss 来训练一个人体回归器，从而从单目图像中回归出 3D 人体形状和姿势。在 DSR loss 中，利用了服装的语义信息来惩罚有衣和没衣的区域，从而更好的匹配服装区和未穿衣区，同时为了确保可微的训练，从成千上万个衣服扫描中学习 SMPL 顶点的语义衣服先验。在 3DPW 和 Human3.6M 上优于先前的最先进的方法，并在 MPI-INF-3DHP 上获得了与之相当的结果。

使用可导语义渲染从图像中学习回归人体

Learning to Regress Bodies from Images using Differentiable Semantic  Rendering

In this paper, we focus on improving online multi-object tracking (MOT). In
particular, we introduce a region-based Siamese Multi-Object Tracking network,
which we name SiamMOT. SiamMOT includes a motion model that estimates the
instance's movement between two frames such that detected instances are
associated. To explore how the motion modelling affects its tracking
capability, we present two variants of Siamese tracker, one that implicitly
models motion and one that models it explicitly. We carry out extensive
quantitative experiments on three different MOT datasets: MOT17, TAO-person and
Caltech Roadside Pedestrians, showing the importance of motion modelling for
MOT and the ability of SiamMOT to substantially outperform the
state-of-the-art. Finally, SiamMOT also outperforms the winners of ACM MM'20
HiEve Grand Challenge on HiEve dataset. Moreover, SiamMOT is efficient, and it
runs at 17 FPS for 720P videos on a single modern GPU. Codes are available in
https://github.com/amazon-research/siam-mot.

本文提出了一种基于区域的孪生多目标跟踪网络（SiamMOT），采用运动模型估计两帧之间的物体运动，探讨两种不同运动建模对多目标跟踪效果的影响，通过在三个数据集上进行实验，SiamMOT 相对于当前技术的跟踪方法具有明显的优越性，并且具有高效性。

SiamMOT: Siamese 多目标跟踪

SiamMOT: Siamese Multi-Object Tracking

A significant amount of search queries originate from some real world
information need or tasks. In order to improve the search experience of the end
users, it is important to have accurate representations of tasks. As a result,
significant amount of research has been devoted to extracting proper
representations of tasks in order to enable search systems to help users
complete their tasks, as well as providing the end user with better query
suggestions, for better recommendations, for satisfaction prediction, and for
improved personalization in terms of tasks. Most existing task extraction
methodologies focus on representing tasks as flat structures. However, tasks
often tend to have multiple subtasks associated with them and a more
naturalistic representation of tasks would be in terms of a hierarchy, where
each task can be composed of multiple (sub)tasks. To this end, we propose an
efficient Bayesian nonparametric model for extracting hierarchies of such tasks
\& subtasks. We evaluate our method based on real world query log data both
through quantitative and crowdsourced experiments and highlight the importance
of considering task/subtask hierarchies.

该研究旨在提出一种基于贝叶斯非参数模型的任务 / 子任务分层提取模型，有着更好的性能，经过大量的实际数据实验验证，考虑任务 / 子任务的层次结构是至关重要的。