Scene reconstruction in the presence of high-speed motion and low
illumination is important in many applications such as augmented and virtual
reality, drone navigation, and autonomous robotics. Traditional motion
estimation techniques fail in such conditions, suffering from too much blur in
the presence of high-speed motion and strong noise in low-light conditions.
Single-photon cameras have recently emerged as a promising technology capable
of capturing hundreds of thousands of photon frames per second thanks to their
high speed and extreme sensitivity. Unfortunately, traditional computer vision
techniques are not well suited for dealing with the binary-valued photon data
captured by these cameras because these are corrupted by extreme Poisson noise.
Here we present a method capable of estimating extreme scene motion under
challenging conditions, such as low light or high dynamic range, from a
sequence of high-speed image frames such as those captured by a single-photon
camera. Our method relies on iteratively improving a motion estimate by
grouping and aggregating frames after-the-fact, in a stratified manner. We
demonstrate the creation of high-quality panoramas under fast motion and
extremely low light, and super-resolution results using a custom single-photon
camera prototype. For code and supplemental material see our
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在高速运动和低照明条件下的场景重建是许多应用（如增强和虚拟现实、无人机导航和自主机器人）中的重要问题。本文介绍了一种能够从高速图像序列（如由单光子相机捕获的序列）中估计极端场景运动的方法，在低光或高动态范围等具有挑战性的条件下表现出色。

光子构景

Panoramas from Photons

Nighttime person Re-ID (person re-identification in the nighttime) is a very
important and challenging task for visual surveillance but it has not been
thoroughly investigated. Under the low illumination condition, the performance
of person Re-ID methods usually sharply deteriorates. To address the low
illumination challenge in nighttime person Re-ID, this paper proposes an
Illumination Distillation Framework (IDF), which utilizes illumination
enhancement and illumination distillation schemes to promote the learning of
Re-ID models. Specifically, IDF consists of a master branch, an illumination
enhancement branch, and an illumination distillation module. The master branch
is used to extract the features from a nighttime image. The illumination
enhancement branch first estimates an enhanced image from the nighttime image
using a nonlinear curve mapping method and then extracts the enhanced features.
However, nighttime and enhanced features usually contain data noise due to
unstable lighting conditions and enhancement failures. To fully exploit the
complementary benefits of nighttime and enhanced features while suppressing
data noise, we propose an illumination distillation module. In particular, the
illumination distillation module fuses the features from two branches through a
bottleneck fusion model and then uses the fused features to guide the learning
of both branches in a distillation manner. In addition, we build a real-world
nighttime person Re-ID dataset, named Night600, which contains 600 identities
captured from different viewpoints and nighttime illumination conditions under
complex outdoor environments. Experimental results demonstrate that our IDF can
achieve state-of-the-art performance on two nighttime person Re-ID datasets
(i.e., Night600 and Knight ). We will release our code and dataset at
this https URL

提出了一种夜间人物重识别的光照提取框架（IDF），通过利用光照增强和光照提取方案来促进重识别模型的学习，在夜间低照明条件下，能够提高夜间人物重识别方法的性能并且建立了一个新的实际数据集 Night600 用于评估。

夜间人物再识别的光照提取框架及新基准

Illumination Distillation Framework for Nighttime Person  Re-Identification and A New Benchmark

Just like weights, bias terms are the learnable parameters of many popular
machine learning models, including neural networks. Biases are believed to
effectively increase the representational power of neural networks to solve a
wide range of tasks in computer vision. However, we argue that if we consider
the intrinsic distribution of images in the input space as well as some desired
properties a model should have from the first principles, biases can be
completely ignored in addressing many image-related tasks, such as image
classification. Our observation indicates that zero-bias neural networks could
perform comparably to neural networks with bias at least on practical image
classification tasks. In addition, we prove that zero-bias neural networks
possess a nice property called scalar (multiplication) invariance, which has
great potential in learning and understanding images captured under poor
illumination conditions. We then extend scalar invariance to more general cases
that allow us to verify certain convex regions of the input space. Our
experimental results show that zero-bias models could outperform the
state-of-art models by a very large margin (over 60%) when predicting images
under a low illumination condition (multiplying a scalar of 0.01); while
achieving the same-level performance as normal models.

本文研究讨论在解决图像分类相关任务时，通过考虑输入空间的内在分布和模型的首要属性，可以忽略偏差对神经网络效果的影响，而使用零偏差神经网络可能在保持同等性能下，拥有更好的标量不变性和学习图像的能力，在低光照条件下通过只乘以 0.01 的标量来预测的情况下，其性能显著高于现有的基准模型（超过 60%）。