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feature-detection-and-matching icon feature-detection-and-matching

本实验是通过Harris、SIFT、NNDR、RANSAC算法、实现一幅图像的特 征检测与匹配，并比较不同算法之间差异性，了解并掌握特征检测与匹配的基本算 法流程和相应算法的原理，并通过代码实践感受算法的优缺点，体会图像的特征检 测与匹配在实际场景中的应用。

genetic-algorithm icon genetic-algorithm

本项目主要设计遗传算法解决简单的度约束的最小生成树问题，同时利用遗传算法优化一个简单的神经网络

photometric-stereo-3d-reconstruction-application icon photometric-stereo-3d-reconstruction-application

基于光度立体技术，使用同一视点不同光照下的图片信息，计算物体表 面法向量并可视化，同时利用法向量计算深度信息并可视化，最后利用深度信息重 建三维图像并使用相关软件可视化。

ppseq2seq icon ppseq2seq

参考PPLM，将基础模型替换为复述模型，以融入语意信息，从而实现classifier-guided的风格迁移任务

psst icon psst

code and data for TSST task

semantic-similarity-calculation icon semantic-similarity-calculation

北京理工大学智能计算工程实践项目：基于语义相似度计算问题，对CNN、LSTM、Transformer、Bert的编码能力(语义理解能力)进行比较，尝试“模型结构和编码能力”的可解释性探讨

transfer-learning icon transfer-learning

模拟实际情况下解决小样本问题：主要基于语义相似度计算和NLI问题，探究迁移学习对目标任务的提升与目标数据集大小的关系（当然还有很多可以进行实验探讨的部分）

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