科研实习 | 约翰霍普金斯大学Alan Yuille教授招收CV方向暑期科研实习生
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Johns Hopkins University
Advisor Profile
Professor Alan Yuille is a Bloomberg Distinguished Professor in the Department of Computer Science and the Department of Cognitive Science at Johns Hopkins. He published many influential papers in computer vision, cognitive science, etc. He has won the ICCV Marr Award and is an IEEE Fellow.
Lab Page:
https://ccvl.jhu.edu/
Overall Information
We are seeking several summer research interns for 2024. The internship starts in May, and the duration is flexible (between 6 months to 1 year). Exceptional interns from previous years have been published as the first authors at top conferences in computer vision or medical image processing, such as CVPR, ICLR, and MICCAI. Priority will be given to exceptional interns for Ph.D. applications.
Research Directions
Our lab’s research lies in computer vision and machine learning. The detailed research groups include: 3D generative models, 3D datasets, Medical image analysis, Transformers, Vision and language, Embodied AI (mentored by Prof. Tianmin Shu).
Requirements
The applicants are expected to fulfill one of the following group’s requirements. Besides, we would really appreciate it if you could specify which group you’re interested in when submitting your applications. We strongly enough you to read the related papers of our group and learn some preliminary knowledge by checking our publication list:
https://ccvl.jhu.edu/publication/
The requirements for different groups are as follows:
1. 3D generative models:
Basic: basic usage of PyTorch;
Basic: understanding of the 3D imaging (camera system);
Preferred: Publications (can be under review) on related topics, e.g., NeRF, 3D reconstruction, pose estimation, 3D detection, etc.
Understanding recent 3D vision or reconstruction techniques.
At least one of the following topics:
- 3D from images (pose and shape, 3D detection)
- Differentiable rendering (e.g., PyTorch3D, Gaussian Splatting)
- Other 3D-related topics
2. 3D datasets:
Basic skills in using Python, PyTorch, and other machine-learning libraries;
Basic skills in using 3D tools, e.g., Blender.
3. Medical image analysis:
Proficiency in computer vision and image analysis concepts;
Proficiency in Python programming to use prevalent frameworks (such as nnU-Net and MONAI);
Prior experience with the analysis of radiological image datasets for AI applications is preferred;
Relevent publication/submission in conferences/journals (such as MICCAI, TMI, and MedIA) is preferred.
4. Transformers:
Basic skills in using Python, PyTorch, and other machine-learning libraries;
Basic mathematics foundations in related areas, e.g., statistical learning and optimization;
Knowledge of the basic concepts of the Transformers architectures;
Publications or submissions in related conferences and journals, e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, TPAMI, IJCV, JMLR.
5. Vision and language:
Proficiency in using Python, PyTorch, and other machine-learning libraries;
Basic knowledge in common deep learning methods in image understanding, language modeling, and multimodal learning (E.g. CNN, LSTM, Transformer);
Understanding the concepts of generative learning and the attention mechanism with transformers;
Hands-on experience with the vision-language model or large language model (e.g., CLIP, GPT, LLAMA, BLIP, Flamingo, StableDiffusion…);
Publications or submissions in related conferences or journals, e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, TPAMI, IJCV, TMLR.
6. Embodied AI (mentored by Prof. Tianmin Shu):
Basic skills in using Python, PyTorch, and other machine-learning libraries;
Experience or interests in the following topics:
- Generative AI for developing embodied simulators with diverse and realistic human behaviors, including but not limited to synthesizing human-object interactions in household environments, human-vehicle interactions, and physically grounded social interactions.
- Multimodal theory of mind reasoning for embodied agents.Embodied human-AI cooperation and communication.
How to Apply
If interested, please email Professor Alan Yuille ([email protected]) with your resume attached. Interns will collaborate with Professor Alan Yuille, Professor Tianmin Shu, and their research teams at Johns Hopkins.
实习内推
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高校招生
华盛顿大学计算机系王晟老师|香港科技大学(广州)骆昱宇老师|伊利诺伊大学香槟分校张欢老师|西湖大学机器智能实验室|新加坡国立大学尤洋老师课题组|清华大学信息国家研究中心|香港理工大学Boris NG教授|香港科技大学(广州)张延林老师|香港科技大学(广州)谢思泓教授|北京大学王乐业老师|伊利诺伊大学香槟分校张潼教授|香港科技大学(广州)孙莹老师|伊利诺伊理工大学王韧老师|香港中文大学(深圳)濮实老师|新加坡管理大学何盛烽副教授|香港科技大学(广州)赵航老师|南方科技大学视觉郑锋老师|北京大学长沙计算与数字经济研究院|香港理工大学MIND实验室|香港理工大学李青教授|大湾区大学杨斯崑老师|墨尔本大学Ting Dang老师|南方科技大学荆炳义教授|香港科技大学(广州)白云老师|佐治亚大学卢国玉老师|北京大学孔桂兰老师|北京大学张辉帅老师|香港科技大学(广州)王泽宇老师
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