This document discusses non-structured data analysis, focusing on image data. It defines structured and non-structured data, with images, text, and audio given as examples of non-structured data. Images are described as high-dimensional vectors that are generated from analog to digital conversion via sampling and quantization. Various types of image data and analysis tasks are introduced, including image recognition, computer vision, feature extraction and image compression. Image processing techniques like filtering and binarization are also briefly covered.
1. The document discusses various statistical and neural network-based models for representing words and modeling semantics, including LSI, PLSI, LDA, word2vec, and neural network language models.
2. These models represent words based on their distributional properties and contexts using techniques like matrix factorization, probabilistic modeling, and neural networks to learn vector representations.
3. Recent models like word2vec use neural networks to learn word embeddings that capture linguistic regularities and can be used for tasks like analogy-making and machine translation.
You Only Look One-level Featureの解説と見せかけた物体検出のよもやま話Yusuke Uchida
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第7回全日本コンピュータビジョン勉強会「CVPR2021読み会」(前編)の発表資料です
https://kantocv.connpass.com/event/216701/
You Only Look One-level Featureの解説と、YOLO系の雑談や、物体検出における関連する手法等を広く説明しています
You Only Look One-level Featureの解説と見せかけた物体検出のよもやま話Yusuke Uchida
?
第7回全日本コンピュータビジョン勉強会「CVPR2021読み会」(前編)の発表資料です
https://kantocv.connpass.com/event/216701/
You Only Look One-level Featureの解説と、YOLO系の雑談や、物体検出における関連する手法等を広く説明しています