The goal of 2D human pose estimation (HPE) is to localize anatomical
landmarks, given an image of a person in a pose. SOTA techniques make use of
thousands of labeled figures (finetuning transformers or training deep CNNs),
acquired using labor-intensive crowdsourcing. On the other hand,
self-supervised methods re-frame the HPE task as a reconstruction problem,
enabling them to leverage the vast amount of unlabeled visual data, though at
the present cost of accuracy. In this work, we explore ways to improve
self-supervised HPE. We (1) analyze the relationship between reconstruction
quality and pose estimation accuracy, (2) develop a model pipeline that
outperforms the baseline which inspired our work, using less than one-third the
amount of training data, and (3) offer a new metric suitable for
self-supervised settings that measures the consistency of predicted body part
length proportions. We show that a combination of well-engineered
reconstruction losses and inductive priors can help coordinate pose learning
alongside reconstruction in a self-supervised paradigm.

简化中文摘要：本文探讨了改善自监督人体姿势估计的方法，包括分析重建质量与姿势估计准确性的关系，提出了一个优于基准模型的模型流水线，使用不到三分之一的训练数据量，并提出了一种适用于自监督环境的度量，用于衡量预测身体部位长度比例的一致性。结果表明，在自监督范式中，精心设计的重建损失和归纳假设可以帮助协调姿势估计和重建。

高效的自监督人体姿势估计与归纳先验调整

Efficient, Self-Supervised Human Pose Estimation with Inductive Prior  Tuning

The \emph{Chow parameters} of a Boolean function $f: \{-1,1\}^n \to \{-1,1\}$
are its $n+1$ degree-0 and degree-1 Fourier coefficients. It has been known
since 1961 (Chow, Tannenbaum) that the (exact values of the) Chow parameters of
any linear threshold function $f$ uniquely specify $f$ within the space of all
Boolean functions, but until recently (O'Donnell and Servedio) nothing was
known about efficient algorithms for \emph{reconstructing} $f$ (exactly or
approximately) from exact or approximate values of its Chow parameters. We
refer to this reconstruction problem as the \emph{Chow Parameters Problem.}
Our main result is a new algorithm for the Chow Parameters Problem which,
given (sufficiently accurate approximations to) the Chow parameters of any
linear threshold function $f$, runs in time $\tilde{O}(n^2)\cdot
(1/\eps)^{O(\log^2(1/\eps))}$ and with high probability outputs a
representation of an LTF $f'$ that is $\eps$-close to $f$. The only previous
algorithm (O'Donnell and Servedio) had running time $\poly(n) \cdot
2^{2^{\tilde{O}(1/\eps^2)}}.$
As a byproduct of our approach, we show that for any linear threshold
function $f$ over $\{-1,1\}^n$, there is a linear threshold function $f'$ which
is $\eps$-close to $f$ and has all weights that are integers at most $\sqrt{n}
\cdot (1/\eps)^{O(\log^2(1/\eps))}$. This significantly improves the best
previous result of Diakonikolas and Servedio which gave a $\poly(n) \cdot
2^{\tilde{O}(1/\eps^{2/3})}$ weight bound, and is close to the known lower
bound of $\max\{\sqrt{n},$ $(1/\eps)^{\Omega(\log \log (1/\eps))}\}$ (Goldberg,
Servedio). Our techniques also yield improved algorithms for related problems
in learning theory.

本文提出了一种新的算法来解决 “Chow Parameters Problem”, 该算法可以高效地从一个线性阈值函数的 Chow 参数中重建该函数，并且给出了这个函数的权重的整数上界优化结果，改进了过去的算法，并在学习理论的相关问题上取得了进展。