Miracleyoo

The following document is my resume:

Points

• Input:

  • One counter-intuitive fact is that almost all the mainstream SNN frameworks’ input shape is not a series of 1-D spike signals. Actually, the input shape is pretty much like other ANNs, but with an additional T time dimension. Here is the common input shape: T x N x C x H x W, where T means time, N means batch size, C means channel number, and H, W stands for the input image size. Except for the first two dimensions (T, N), the rest of the dimensions are quite flexible and can be modified as needed, and we can simplify the input shape as T x N x X.
  • For the Loihi SNN hardware platform, the input size is a bit different, but it’s a simple permutation: N x X x T.

Unsupervised Domain Adaption Algorithms

• GRL: Gradient Reversal Layer。 目标是让两个domain的distribution在feature extractor眼中无法区分（即match两个domain使其分布趋同）。
• MMD：Minimize the Maximum Mean Discrepancy between the target and source domain。最小化两个域之间的最大平均差异。作者提出了一个metric来衡量域间差异，通过加loss来抑制这个差异，最终达到让网络最终层输出与域无关的稳定feature。
• AFN：比较玄学。他们说之所以在target domain上表现不好是因为目标向量的norm相比源域的更小。所以它们就逐渐提高深度embedding的L2 norms来解决这个问题。

Taxonomy

• By method: Top-Down; Bottom-Up
• By time dimension: Frame; Time Sequence
• By input type: Monocular; Multi-View
• By human number: Single; Multiple
• By output type: Skeleton; Mesh (SMPL, SCAPE, DensePose)

基本操作

1. 降噪
2. 点云聚类（DBSCAN）
3. 追踪（Tracking）
4. ID（Identification）

ImmFusion: Robust mmWave-RGB Fusion for 3D Human Body Reconstruction in All Weather Conditions

• 2022, Arxiv
• Question: How to merge mmWave radar with RGB frames to do 3D human mesh reconstruction?
• Spec: Single person, 3D mesh, RGB + mmWave Radar.
• Features: Robust in extreme weather/conditions like rain, smoke, low light, and occlusion.

DGCN: Dynamic Graph Convolutional Network for Efficient Multi-Person Pose Estimation

Summary

• Multi-person.
• Image-based. Graph is just used in their DGCM module.
• Bottom-Up.

Paper's full name: A 256x256 6.3pJ/pixel-event Query-driven Dynamic Vision Sensor with Energy-conserving Row-parallel Event Scanning, Link

Idea

This paper proposed a novel query-driven DVS (qDVS) hardware. This new hardware combines the advantages of APS and DVS, following a fixed scanning rate to inquire all pixels whether are good to fire an event. The output of qDVS is event frames. Pixels here are responsible for fewer functions, they only need to tell whether they are good to shoot and the polarity, as their address is fixed on the generated event frames. As each pixel has fewer functions, they are able to be made smaller, which results in an overall higher pixel density. Also, since the output of qDVS is already framed, machine learning researchers don't need to do accumulation themselves and hence optimized the processing pipeline.

Overview

• SMPL主要含有两组参数，一组是人物的体态信息β，一组是人物的姿态信息θ。
• SMPL本身是“相对的”，其只包含人物本身的信息，而不包含任何与环境、相机视角、位置等信息。另外，其mesh点记录的值是相对于模板人类模型标准值的。
• SMPL不包含手、脸和衣服，但后续的其他文章逐渐完善了相应参数。

前言：WSL1和WSL2各有各的特点。WSL2支持GPU和外接USB硬件设备访问，效率也更高；但一旦涉及到Windows内部的文件系统访问，那只能说是慢的不能行，尤其是运行涉及到大量文件操作的脚本时。但是我用WSL很久了，也有了不少个性化设置，安装了很多Library。如果重头再来一遍，耗时耗力且可能某些内容与之前的环境不兼容。于是想法来了：能不能直接把已有的WSL克隆一份，并升级为WSL2，或者相反？