From 18a9693558fd5a107019829bcc996b6fdeb1ed4b Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Wed, 18 Sep 2024 13:31:29 +0800 Subject: [PATCH] Add more resources Signed-off-by: Jael Gu --- README.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/README.md b/README.md index 86dc0aa..e7a6bd6 100644 --- a/README.md +++ b/README.md @@ -90,3 +90,16 @@ and a corresponding vector in numpy.ndarray given a video input data. - labels: predicted class names. - scores: possibility scores ranking from high to low corresponding to predicted labels. - features: a video embedding in shape of (768,) representing features extracted by model. + + +# More Resources + +- [Understanding Class Activation Mapping (CAM) in Deep Learning - Zilliz blog](https://zilliz.com/learn/class-activation-mapping-CAM): Class Activation Mapping (CAM) is used to visualize and understand the decision-making of convolutional neural networks (CNNs) for computer vision tasks. +- [Vector Database Use Cases: Video Similarity Search - Zilliz](https://zilliz.com/vector-database-use-cases/video-similarity-search): Experience a 10x performance boost and unparalleled precision when your video similarity search system is powered by Zilliz Cloud. +- [How to Get the Right Vector Embeddings - Zilliz blog](https://zilliz.com/blog/how-to-get-the-right-vector-embeddings): A comprehensive introduction to vector embeddings and how to generate them with popular open-source models. +- [Building a Video Analysis System with Milvus Vector Database - Zilliz blog](https://zilliz.com/blog/milvus-helps-analyze-videos-intelligently): Learn how Milvus powers the AI analysis of video content. +- [4 Steps to Building a Video Search System - Zilliz blog](https://zilliz.com/blog/building-video-search-system-with-milvus): Searching for videos by image with Milvus +- [Understanding ImageNet: A Key Resource for Computer Vision and AI Research](https://zilliz.com/glossary/imagenet): The large-scale image database with over 14 million annotated images. Learn how this dataset supports advancements in computer vision. +- [Everything You Need to Know About Zero Shot Learning - Zilliz blog](https://zilliz.com/learn/what-is-zero-shot-learning): A comprehensive guide to Zero-Shot Learning, covering its methodologies, its relations with similarity search, and popular Zero-Shot Classification Models. +- [What is a Generative Adversarial Network? An Easy Guide](https://zilliz.com/glossary/generative-adversarial-networks): Just like we classify animal fossils into domains, kingdoms, and phyla, we classify AI networks, too. At the highest level, we classify AI networks as "discriminative" and "generative." A generative neural network is an AI that creates something new. This differs from a discriminative network, which classifies something that already exists into particular buckets. Kind of like we're doing right now, by bucketing generative adversarial networks (GANs) into appropriate classifications. +So, if you were in a situation where you wanted to use textual tags to create a new visual image, like with Midjourney, you'd use a generative network. However, if you had a giant pile of data that you needed to classify and tag, you'd use a discriminative model. \ No newline at end of file