Will you help us find an end-to-end approach?
Due to the exponential growth of video content over the last decade, there has been an increasing need for video analytics. Using video analytics has many practical benefits, such as helping in the monitoring of surveillance imagery or generating video previews on YouTube.
At Gyver, we are developing a video analytics system specially designed for TV content. Currently, we generate metadata based on a large number of explicit features that are generated by visual and audio networks. However, we are interested in the capabilities of an end-to-end approach, which would help to make our solutions less domain-specific.
A number of methods have been proposed for solving problems in this field, for example using Adversarial LSTMs or using a Reinforcement Learning approach. While these methods can serve as a good baseline, they often have room for improvement. We are specifically interested in performance on longer videos instead of the short videos in scientific datasets. We are looking for a student that wants to tackle these problems in a master thesis.
We are looking for a student who:
- Is proficient in Python
- Has experience with Pythons deep learning packages
(PyTorch is our “weapon of choice”)
- Has some experience with complex machine learning models.
For example, GANs and LSTMs
At Gyver, we provide:
- A working space at our office in Amsterdam
- Our own server, suited for training deep learning models
- Access to scientific datasets, as well as our own dataset
- A great team with a casual atmosphere, free lunch and friday drinks
Please get in touch using firstname.lastname@example.org. Or use the contact form below.