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Proj CMI Paper Reading: Developing Trustworthy Hardware with Security-Driven Design and Verification
Abstract 背景：1. 集成电路可能需要为了特定程序优化 2. 由于集成电路比较复杂，寄存器传输级Register-Transfer Level (RTL)设计的外包更为常见 we must trust our ICs have been designed and fabricated to specification, i.e., they do not contain any hardware Trojans 本文任论文解读：《SG-Net: Syntax Guided T ransformer forLanguage Representation》
期刊名： IEEE Transactions on Pattern Analysis and Machine Intelligence 影像因子/分区：16.225/Q1 一、摘要 抽象理解人类语言是人工智能的关键主题之一。将所提出的SG-Net应用于典型的变压器编码器。在机器阅读理解、自然语言推理和神经机器翻译等常用基准测试任务上导向滤波(Guided Filter)相关公式及伪代码
参考文献：《张文婷_基于导向滤波的图像增强算法研究》 导向滤波器的定义： 式中为输出像素点，为导向图像素点(导向图可以为输入图像，也可以是其他图像) 假设输出图像q是输入图像p减去不必要的纹理和噪声n,则可得到下述公式： 为了让输出图像 q 与输入图像 p 相差最小，引入了下述代价SG-Net
SG-Net: Syntax-Guided Machine Reading Comprehension 这是2020年上交发表在AAAI上的一篇文章，本文在MRC中引入了语法结构信息，这也是我在读《Improving the Robustness of Question Answering Systems to Question Paraphrasing》这篇文章时所想到的一个创新点。 Overview 本Learning Memory-guided Normality for Anomaly Detection论文解析
近日拜读了本篇论文，有以下理解： 1.问题研究背景 检测视频序列中的异常事件（如人行道上的车辆）的问题对监控和故障检测系统尤为重要。由于一些原因，它是极具挑战性的。首先，异常事件是根据不同的情况而确定的。也就是说，同样的活动可能是正常的，也可能是不正常的（例如，在厨房Learning Memory-guided Normality for Anomaly Detection 代码解析
Learning Memory-guided Normality for Anomaly Detection 代码解析 目录 : 整体结构训练(train)部分分析评估(evaluate)部分分析模型(model)部分分析代码环境配置及运行 1.整体结构 1.1 代码文件及作用 根目录下包含 : Evaluate.py 评估代码Train.py 模型训练代码utils.pyGrad-CAM:Visual Explanations from Deep Networks via Gradient-based Localization
目录Grad-CAM:Visual Explanations from Deep Networks via Gradient-based Localization1.Abstract2.Introduction3.Approach4.Evaluating Localization4.1. Weakly-supervised Localization4.2 Weakly-supervised Segmentation5.Evaluating Visualizations5.1 Evaluating ClasD2T2 - trapfuzzer- Coverage-guided Binary Fuzzing with Breakpoints PPT
https://github.com/hac425xxx/slides/blob/main/D2T2 - trapfuzzer- Coverage-guided Binary Fuzzing with Breakpoints - Sili Luo.pdfGuided Anchoring：在线稀疏anchor生成方案，嵌入即提2AP | CVPR 2019
Guided Anchoring通过在线生成anchor的方式解决常规手工预设anchor存在的问题，以及能够根据生成的anchor自适应特征，在嵌入方面提供了两种实施方法，是一个很完整的解决方案 来源：晓飞的算法工程笔记 公众号 论文: Region Proposal by Guided Anchoring 论文地址：https://arxiv.o【论文阅读】Entity and Evidence Guided Relation Extraction for DocRED[2020]
论文地址：https://arxiv.org/abs/2008.12283 论文地址：未找到 我读的第二篇利用Evidence特征的论文。今天是除夕，文章没全弄明白，顺便祝大家身体健康，万事如意。 Abstract 我们提出了一个联合训练框架E2GRE（实体和证据引导的关系提取）(Entity and Evidence Guided Relation Extraction)。OPLD、Learning Point-guided Localization for Detection in Remote Sensing Images
1-OPLD、Learning Point-guided Localization for Detection in Remote Sensing Images 算法介绍 A. 动机 主流的基于回归的应该目标检测算法癌症训练期间对每一个proposal匹配一个真值框，并将它们的偏移量编码作为监督信息。 而不论是RBB还是OBB，za8i这个过程当中都存在极端的Profile Guided Optimization Link Time Optimization
https://github.com/python/cpython Profile Guided Optimization PGO takes advantage of recent versions of the GCC or Clang compilers. If used, either via configure --enable-optimizations or by manually running make profile-opt regardless of configureDRConv：旷视提出区域感知动态卷积，多任务性能提升 | CVPR 2020
论文提出DRConv，很好地结合了局部共享的思想并且保持平移不变性，包含两个关键结构，从实验结果来看，DRConv符合设计的预期，在多个任务上都有不错的性能提升 来源：晓飞的算法工程笔记 公众号 论文: Dynamic Region-Aware Convolution 论文地址：https://arxiv.org/pdf/2003.12243.pdf【论文笔记】Fast Cost-Volume Filtering for Visual Correspondence and Beyond
—— 一篇曾经在middlebury上达到SoT的论文，代价聚合用的guided filter，逻辑清晰，性能不够快，但paper从更高的角度来看计算机视觉中的立体匹配、光流以及图像分割，值得一读 嗯，读完了，有种任督二脉被打通的感觉，把这两年多接触到的词，陌生的，熟悉的，全都连起来了 前言 作者指出以下几个领域Expertly Guided E20-526 Exam Cram with a High Passing Rate
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专栏【PyTorch】 原文链接：https://github.com/zergtant/pytorch-handbook %load_ext autoreload %autoreload 2 import torch import numpy as np import torch.nn as nn import torch.nn.functional as F from PIL import Image from torchvision import transforms fromUnsupervised Attention-guided Image-to-Image Translation
这是NeurIPS 2018一篇图像翻译的文章。目前的无监督图像到图像的翻译技术很难在不改变背景或场景中多个对象交互方式的情况下将注意力集中在改变的对象上去。这篇文章的解决思路是使用注意力导向来进行图像翻译。下面是这篇文章的结果图： 可以看到文章结果很好, 只有前景(对象)改变