Although it is possible to generate realistic images, GAN models often lack fine-grained control over the generated images. Recently, several methods have been proposed to provide fine-grained control over latent space. Most of these works allow us to find domain-independent and general directions such as rotation, zoom-in or color change, while others proposed techniques to explore domain-specific directions such as changing age, gender, or expression on facial images.
Named entity recognition aims to detect entities that refer to people, locations, organizations and similar in a given sentence.
This project involves reimplementing a recent NER tagger that is shown to surpass the state-of-the-art performance for morphologically rich languages [1].
We will employ a software framework that is specific to NLP to easily build, train, evaluate and deploy the new tagger, i.e. Stanza, Flair or Huggingface.
We will also add some new features to exploit all types of word embeddings easily.
Edge systems can be thought of as micro-cloud infrastructures that serve devices in proximity. Devices with insufficient computational capacities (AR/VR glasses, mobile gadgets, smartphones depending on the application) can augment their compute power using these edge servers.
We are looking for motivated student(s) to form a dataset of micro-scale beads from microscopic videos and test it with state-of-the-art techniques of multi-object detection, instance segmentation, and, if time permits, multi-object tracking (MOT).
The main challenge here is that a continuous flow making moving micro-scale beads look blurry, very fast to detect, and the existence of false-negative candidates.
This application will provide a platform for posting academic jobs and ease the process of searching for the academic job postings. Typically, during a job search period, people visit many job posting web sites and subscribe to email alerts to look for appropriate job postings. SmartJobs@Academia application will make this process easier for both sides, i.e., job searchers and posters.