Tag: Deep Learning

M1-V Deep Learning in Video Compression

June 29, 2022 in 2019-2020, Project

Advancing the state-of-the-art in image/video compression by adopting deep learning methods in prediction, transform, entropy coding and post processing. Develop fresh new coding tools based on deep learning for post processing, reconstruction enhancement. Investigate new pipelines using deep learning for end-to-end image/video compression. Achieve significant coding improvements with applicable computational complexity as well as deliver insights into deep learning video compression for machine consumption, e.g., tracking, segmentation, recognition.

M3-V Privacy-Preserving Fall Detection with Deep Learning on mmWaveRadar Signal (renamed: Low-Light and Low-Resolution Image Enhancement)

June 29, 2022 in 2019-2020, Project

This project aims to design a deep learning based end-to-end solution for dark image denoising and enhancement using image decomposition method, achieving significant improvement in output image quality. Further it aims to improve the image super-resolution by exploiting temporal redundancy. The project also aims at developing an end-to-end solution for multi-frame image super-resolution which involves image registration using optical flow and deep learning for noise removal, achieving significant gain in performance over the state-of-the-art methods.

B1-S DeepCloud: An Intelligent Platform by the Community and for the Community

June 29, 2022 in 2018-2019, Project

DeepCloud is designed as an open software-defined ecosystem for researchers at different levels with the following salient features and transformative impacts. It is one of the first massively scalable multi-tenant open cloud platform with full-fledged building blocks and comprehensive shared stores (app, model, knowledge, data) for deep learning research and applications.

F1-M Bidirectional Deep Learning Architecture for Scene Understanding

June 29, 2022 in 2018-2019, Project

This project aims at creating deep architectures inspired by cognitive sciences to under visual scenes either in images or videos. The characteristic of the proposed architecture is that it simplifies the inference using biological plausible marginals (object type and spatial location), which can be learned in an unsupervised way directly from data (i.e. without labels).