kjw0612/awesome-deep-vision

★ 11,161Audience · researcherComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((repo))
    Topics covered
      Image classification
      Object detection
      Semantic segmentation
      Image generation
    Resources
      Academic papers
      University courses
      Code and models
    Audience
      Researchers
      Students
    Notes
      No longer maintained
      Some links outdated

mindmap root((repo)) Topics covered Image classification Object detection Semantic segmentation Image generation Resources Academic papers University courses Code and models Audience Researchers Students Notes No longer maintained Some links outdated

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Things people build with this

USE CASE 1

Find landmark research papers on image classification, object detection, or semantic segmentation to read first.

USE CASE 2

Discover university courses and textbooks to build a structured learning path in computer vision.

USE CASE 3

Locate code repositories and pre-trained models linked alongside their original research papers.

USE CASE 4

Get an overview of which topics and research labs have shaped a specific area of visual AI.

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

This repository is a curated reading list for anyone who wants to learn about the research behind teaching computers to see and understand images. It was assembled by a group of researchers and collects papers, courses, books, videos, software tools, and tutorials all in one place. The project is no longer actively maintained, but the archive remains a useful snapshot of the field. The bulk of the list is organized by research topic. There are sections covering how AI systems learn to classify what is in an image, how they detect and locate specific objects, how they track moving objects across video frames, and how they assign a label to every pixel in a scene (called semantic segmentation). Other sections cover reading text in images, estimating how a human body is posed, generating new images, and connecting images to written captions or answering questions about a photo. Each entry in the list links to the original academic paper and sometimes to code, a project page, or a pre-trained model. The papers come from universities and research labs including Microsoft Research, UC Berkeley, Oxford, NYU, and others. There is no software to install here and nothing to run: this is a reference index, not an application. Beyond papers, the list points to university courses on the subject, textbooks, recorded talks, and popular software tools that practitioners use when building computer vision systems. It also links to tutorials and blog posts for readers who want gentler introductions. If you are a researcher, student, or curious reader trying to map out what has been studied in the field of visual AI, this list gives you a structured starting point. Because it is no longer actively maintained, some links may be outdated and newer developments after the last update are not included. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

I'm new to computer vision. Based on the awesome-deep-vision list structure, suggest a 4-week reading plan starting from basics and moving to object detection.

Prompt 2

I want to understand semantic segmentation. Which papers from the awesome-deep-vision list should I read first and in what order?

Prompt 3

Help me find papers from the awesome-deep-vision list that deal with image captioning or visual question answering.

Prompt 4

I'm starting a project on human pose estimation. What papers and tools from the awesome-deep-vision list are most relevant?

Prompt 5

Summarize the key ideas behind one of the foundational image classification papers listed in awesome-deep-vision, such as AlexNet or VGG.

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