PyBADS is a Python implementation of the Bayesian Adaptive Direct Search
(BADS) algorithm for fast and robust black-box optimization (Acerbi and Ma
2017). BADS is an optimization algorithm designed to efficiently solve
difficult optimization problems where the objective function is rough
(non-convex, non-smooth), mildly expensive (e.g., the function evaluation
requires more than 0.1 seconds), possibly noisy, and gradient information is
unavailable. With BADS, these issues are well addressed, making it an excellent
choice for fitting computational models using methods such as
maximum-likelihood estimation. The algorithm scales efficiently to black-box
functions with up to $D \approx 20$ continuous input parameters and supports
bounds or no constraints. PyBADS comes along with an easy-to-use Pythonic
interface for running the algorithm and inspecting its results. PyBADS only
requires the user to provide a Python function for evaluating the target
function, and optionally other constraints.
Extensive benchmarks on both artificial test problems and large real
model-fitting problems models drawn from cognitive, behavioral and
computational neuroscience, show that BADS performs on par with or better than
many other common and state-of-the-art optimizers (Acerbi and Ma 2017), making
it a general model-fitting tool which provides fast and robust solutions.

PyBADS 是一个快速、稳健的黑盒优化算法的 Python 实现，可以用于计算建模和模型拟合问题。

PyBADS：Python 中快速且鲁棒的黑盒优化

PyBADS: Fast and robust black-box optimization in Python

Conventional feature-based and model-based gaze estimation methods have
proven to perform well in settings with controlled illumination and specialized
cameras. In unconstrained real-world settings, however, such methods are
surpassed by recent appearance-based methods due to difficulties in modeling
factors such as illumination changes and other visual artifacts. We present a
novel learning-based method for eye region landmark localization that enables
conventional methods to be competitive to latest appearance-based methods.
Despite having been trained exclusively on synthetic data, our method exceeds
the state of the art for iris localization and eye shape registration on
real-world imagery. We then use the detected landmarks as input to iterative
model-fitting and lightweight learning-based gaze estimation methods. Our
approach outperforms existing model-fitting and appearance-based methods in the
context of person-independent and personalized gaze estimation.

本文提出了一种基于学习的方法来进行眼部标志点的定位，采用该方法能够在未受控制的真实环境下较之传统方法更具优势，在个性化视线估计场景中性能优越。

学习在非受限环境下寻找眼部关键点以进行远程凝视估计

Learning to Find Eye Region Landmarks for Remote Gaze Estimation in  Unconstrained Settings

We present GazeDirector, a new approach for eye gaze redirection that uses
model-fitting. Our method first tracks the eyes by fitting a multi-part eye
region model to video frames using analysis-by-synthesis, thereby recovering
eye region shape, texture, pose, and gaze simultaneously. It then redirects
gaze by 1) warping the eyelids from the original image using a model-derived
flow field, and 2) rendering and compositing synthesized 3D eyeballs onto the
output image in a photorealistic manner. GazeDirector allows us to change where
people are looking without person-specific training data, and with full
articulation, i.e. we can precisely specify new gaze directions in 3D.
Quantitatively, we evaluate both model-fitting and gaze synthesis, with
experiments for gaze estimation and redirection on the Columbia gaze dataset.
Qualitatively, we compare GazeDirector against recent work on gaze redirection,
showing better results especially for large redirection angles. Finally, we
demonstrate gaze redirection on YouTube videos by introducing new 3D gaze
targets and by manipulating visual behavior.

通过模型匹配的方法构建 GazeDirector 用于眼睛注视的重新定向，其通过分析与合成来追踪眼睛，实现了对 3D 准确指定目视的功能，并在 Columbia 注视数据集上的实验中得到了良好的结果。