Welcome to the Deep Learning for Computer Vision Research Exploration project!
This repository aims to provide a comprehensive collection of research papers, summaries, implementations, and resources related to computer vision, deep learning, and machine learning. The project's goal is to facilitate knowledge expansion, encourage collaboration, and stay updated with the latest advancements in the field.
Computer vision is a rapidly evolving field that involves extracting meaningful information from visual data using machine learning techniques. This project focuses on deep learning approaches applied to computer vision tasks, including object detection, image segmentation, video analysis, and generative models.
Over a span of 60 days, the objective is to read and understand 30 research papers related to computer vision, deep learning, and machine learning, in order to expand knowledge and stay updated with the latest advancements in the field.
Read and understand 30 research papers within a span of 60 days, covering various aspects of computer vision and deep learning. Provide concise and informative summaries of the research papers, highlighting the key contributions and methodologies. Implement selected algorithms and techniques described in the papers to gain hands-on experience and practical insights. Foster collaboration and knowledge sharing among researchers, developers, and enthusiasts in the computer vision community. Getting Started
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Requirements: Ensure that you have the necessary dependencies installed, such as Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and relevant libraries for computer vision tasks. Check the individual paper summaries and implementations for specific requirements.
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Explore Research Papers: Browse through the
papersdirectory to access the research papers covered in this project. Each paper includes a summary, key contributions, and relevant details. Select papers of interest and read them to deepen your understanding of computer vision and deep learning techniques. -
Implementations: Explore the
implementationsdirectory to find code samples, Jupyter notebooks, or projects that demonstrate the implementation of algorithms described in the research papers. Follow the instructions provided in each implementation to run the code and reproduce the results. -
Contribute: We encourage contributions from the community to expand and improve the project. See the Contributing section below for guidelines on how to contribute, add papers, improve summaries, implement algorithms, or help with documentation.
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Stay Updated: As new research papers and advancements emerge in the field, regularly check for updates in the repository. Watch the repository or subscribe to notifications to receive updates on new papers, implementations, and contributions.
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Discussion: Discussions with fellow enthusiasts, researchers, or students who share similar interests. Attend conferences, join online forums or communities, and participate in relevant social media groups.
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Implementation and Experiment: To solidify our understanding, We will be trying to implement some of the state of the art techniques and algorithms described in these papers.
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We will Look to cover well-known conferences and journals in the field of computer vision, such as CVPR (Conference on Computer Vision and Pattern Recognition), ICCV (International Conference on Computer Vision), or PAMI (IEEE Transactions on Pattern Analysis and Machine Intelligence).
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We will be taking detailed notes and summarizing the main contributions, methodologies, and results. This will help us retain the information and refer back to it later.
#60days30CVPapers
| Sr. No. | Day | Paper Title | Description | Code | Blog | Video Explanation | Notes |
|---|---|---|---|---|---|---|---|
| 01 | 1-2 | Fully Convolutional Networks for Semantic Segmentation | To build “fully convolutional” networks that take input of the arbitrary size and produce correspondingly-sized output with efficient inference and learning. | ||||
| 02 | 3-4 | Convolutional Networks for Biomedical Image Segmentation | |||||
| 03 | 5-6 | ||||||
| 04 | 7-8 | ||||||
| 05 | 9-10 |
We welcome contributions from researchers, developers, and enthusiasts who are interested in computer vision, deep learning, and machine learning. Your contributions can include adding research papers, improving summaries, implementing algorithms, validating results, enhancing documentation, or fixing bugs.
For detailed guidelines on contributing, please refer to the Contributing.md file.
This project is licensed under the MIT License. You are free to use, modify, and distribute this project for personal and commercial purposes. Refer to the LICENSE file for more information.
Please note that while this project aims to provide accurate and up-to-date information, it does not replace thorough literature reviews or academic research. Always refer to the original research papers for the most accurate and complete understanding.
Thank you for your interest in the Computer Vision Research Exploration project. We hope this resource helps you expand your knowledge and contribute to the exciting field of computer vision.
If you have any questions or suggestions, please feel free to reach out. Happy exploring!