We are looking for a motivated student to work on a research topic that involves deep learning and scene understanding. The project consists on learning generative models for scene graphs, which is a compact data representation that describes an image or 3D model of a scene. Each node of this graph represents an object, while the edges represent relationships/interactions between these objects, e.g. "boy - holding - racket" or "cat - next to - tree". The application of scene graphs involve image generation, content-based queries for image search, and sometimes serve as additional context to improve object detection accuracy.
Example scene graph of an image:
source: Xu et al. Scene Graph Generation by Iterative Message Passing. CVPR 2017
Prerequisites: The candidate should have interest and a good understanding for graph structures, deep learning knowledge, be comfortable with Python, and preferably have some basic experience with a deep learning framework, such as PyTorch?. Last but not least - a good motivation to work on challenging and new ideas.
Preferably master thesis. Also possible as guided research.
Supervision: Helisa Dhamo, PD Dr. Federico Tombari
Please drop an email to Helisa Dhamo if you want to find out more.
ProjectForm | |
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Title: | Scene graph generation |
Abstract: | We are looking for a motivated student to work on a research topic that involves deep learning and scene understanding. The project consists on generating scene graphs which is a compact data representation that describes an image or 3D model of a scene. Each node of this graph represents an object, while the edges represent relationships/interactions between these objects, e.g. "boy - holding - racket" or "cat - next to - tree". The application of scene graphs involve image generation content-based queries for image search, and sometimes serve as additional context to improve object detection accuracy. Preferably master thesis. Also possible as guided research. |
Student: | Sarthak Garg |
Director: | Federico Tombari |
Supervisor: | Helisa Dhamo Azade Farshad |
Type: | Master Thesis |
Area: | Machine Learning, Computer Vision |
Status: | finished |
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Finish: | |
Thesis (optional): | |
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