### ALGO Team Intern Guidelines (CV)

#### Introduction

As part of our algorithm team, you will be working on the Multi-Model Embedding project, focusing on evaluating the performance of different embedding approaches specifically for image data. This guideline outlines the required background knowledge, tools, and tasks you will undertake during your internship.

#### Required Background Knowledge

**AI+Science:**
1. **[Recent advances and applications of deep learning methods in materials science, npj Computational Materials, 2022](https://www.nature.com/articles/s41524-022-00734-6)**
- Understand the integration of deep learning techniques in materials science and their impact.

**Computer Vision (CV):**
1. **[An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ICLR 2021)](https://arxiv.org/abs/2010.11929)**
- Study the use of transformer models in image recognition tasks.
2. **[DINOv2: Learning Robust Visual Features without Labels (CVPR 2023)](https://arxiv.org/abs/2304.07193)**
- Explore how self-supervised learning can be applied to visual feature extraction.
3. **[How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites (CVPR 2024)](https://arxiv.org/abs/2404.16821)**
- Learn about recent advancements and optimizations.

#### Tools and Frameworks

- **Programming Languages:** Python
- **Libraries:** PyTorch, TensorFlow, OpenCV, Hugging Face Transformers
- **Development Environment:** Jupyter Notebook, Visual Studio Code
- **Version Control:** Git, GitHub

#### Potential Tasks

1. **Literature Review:**
- Conduct a thorough literature review on SOTA embeddings and their applications in computer vision, focusing on the methods outlined (beit.py, dinov2.py, googleViT.py, internvit.py, mamba.py, vit.py).

2. **Data Preprocessing:**
- Preprocess various image datasets for embedding evaluations, including tasks like resizing, normalization, and augmentation.

3. **Embedding Implementations:**
- Implement and fine-tune embeddings using the specified methods for image data, focusing on optimizing the models for scientific domain tasks.

4. **Performance Evaluation:**
- Develop and apply evaluation metrics to assess the performance of different embeddings on various image datasets. Conduct experiments to compare the effectiveness of each method in preserving high-value information.

5. **Visualization and Reporting:**
- Visualize results using tools like matplotlib and seaborn, focusing on clarity and scientific rigor.
- Prepare detailed reports and presentations to communicate findings and insights.

6. **Collaboration:**
- Collaborate with team members who are working on the vectorization of structural and textual information to integrate multimodal embeddings into comprehensive models.
- Participate in regular team meetings and brainstorming sessions.

#### Working Place
- **上海市浦东新区民生路118号（昌邑路地铁）滨江万科中心29F**

#### Additional Suggestions

* **Coursera:** Deep Learning Specialization by Andrew Ng
* **edX:** Professional Certificate in Data Science by Harvard University
* **"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville**
* **"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron**