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Our
Projects

This is our project page. We are studying video processing and analysis using deep learning and various intelligent signal processing.  

Multi 360 Image Super-Resolution

One or more 360° images in adjacent views can be utilized to significantly improve the resolution of a target 360° image. In this work, we propose an efficient reference-based 360° image super-resolution (RefSR) technique to exploit a wide field of view (FoV) among adjacent 360° cameras. Latitude-aware convolution (LatConv) is designed to generate more robust features to circumvent the distortion and keep the image quality. We also develop synthetic 360° image datasets and introduce a synthetic-to-real learning scheme that transfers knowledge learned from synthetic 360° images to a deep neural network.

Video Question and Answering using Compressed Features for Deep Learning

Video Question Answering (Video QA) aims to give an answer to the question through semantic reasoning between visual and linguistic information. Recently, handling large amounts of multi-modal video and language information of a video is considered important in the industry. In this work, we develop a novel deep neural network to provide video QA features obtained from coded video bit-stream to reduce the complexity. The proposed network includes several dedicated deep modules to both the video QA and the video compression system, which is the first attempt at the video QA task. 

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360 Image super-resolution

360 Image super-resolution

00:55
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360 Image super-resolution

360 Image super-resolution

00:20
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360 Image super-resolution

360 Image super-resolution

00:12
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VQAC(long)

VQAC(long)

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