We present a new self-supervised approach, SelfPose3d, for estimating 3d
poses of multiple persons from multiple camera views. Unlike current
state-of-the-art fully-supervised methods, our approach does not require any 2d
or 3d ground-truth poses and uses only the multi-view input images from a
calibrated camera setup and 2d pseudo poses generated from an off-the-shelf 2d
human pose estimator. We propose two self-supervised learning objectives:
self-supervised person localization in 3d space and self-supervised 3d pose
estimation. We achieve self-supervised 3d person localization by training the
model on synthetically generated 3d points, serving as 3d person root
positions, and on the projected root-heatmaps in all the views. We then model
the 3d poses of all the localized persons with a bottleneck representation, map
them onto all views obtaining 2d joints, and render them using 2d Gaussian
heatmaps in an end-to-end differentiable manner. Afterwards, we use the
corresponding 2d joints and heatmaps from the pseudo 2d poses for learning. To
alleviate the intrinsic inaccuracy of the pseudo labels, we propose an adaptive
supervision attention mechanism to guide the self-supervision. Our experiments
and analysis on three public benchmark datasets, including Panoptic, Shelf, and
Campus, show the effectiveness of our approach, which is comparable to
fully-supervised methods. Code is available at
https://github.com/CAMMA-public/SelfPose3D

自我监督方法通过多视角输入图像和伪造姿势进行 3D 姿势估计，不需要 2D 或 3D 真实姿势标签，并且其有效性与完全监督方法相当。

SelfPose3d：自监督的多人多视角 3D 姿势估计

SelfPose3d: Self-Supervised Multi-Person Multi-View 3d Pose Estimation

Multi-pitch estimation is a decades-long research problem involving the
detection of pitch activity associated with concurrent musical events within
multi-instrument mixtures. Supervised learning techniques have demonstrated
solid performance on more narrow characterizations of the task, but suffer from
limitations concerning the shortage of large-scale and diverse polyphonic music
datasets with multi-pitch annotations. We present a suite of self-supervised
learning objectives for multi-pitch estimation, which encourage the
concentration of support around harmonics, invariance to timbral
transformations, and equivariance to geometric transformations. These
objectives are sufficient to train an entirely convolutional autoencoder to
produce multi-pitch salience-grams directly, without any fine-tuning. Despite
training exclusively on a collection of synthetic single-note audio samples,
our fully self-supervised framework generalizes to polyphonic music mixtures,
and achieves performance comparable to supervised models trained on
conventional multi-pitch datasets.

多音高估计中的多音高估计，监督式学习技术，自监督式学习目标，多声乐曲数据集，卷积自编码器

实现全自监督多音高估计

Toward Fully Self-Supervised Multi-Pitch Estimation

While pre-trained language models (PTLMs) have achieved noticeable success on
many NLP tasks, they still struggle for tasks that require event temporal
reasoning, which is essential for event-centric applications. We present a
continual pre-training approach that equips PTLMs with targeted knowledge about
event temporal relations. We design self-supervised learning objectives to
recover masked-out event and temporal indicators and to discriminate sentences
from their corrupted counterparts (where event or temporal indicators got
replaced). By further pre-training a PTLM with these objectives jointly, we
reinforce its attention to event and temporal information, yielding enhanced
capability on event temporal reasoning. This effective continual pre-training
framework for event temporal reasoning (ECONET) improves the PTLMs' fine-tuning
performances across five relation extraction and question answering tasks and
achieves new or on-par state-of-the-art performances in most of our downstream
tasks.

本文介绍了一种连续预训练的方法，通过自监督学习目标，强化了预训练模型对事件和时间信息的关注，提高了事件时间推理的能力，在五个任务上提高了模型的微调性能，并在大多数下游任务中获得了新的最先进表现。