Unlocking the Potential of Disentangled Representation for Robust Media Understanding

Wenjun Zeng
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Abstract

It has been argued that for AI to fundamentally understand the world around us, it must learn to identify and disentangle the underlying explanatory factors hidden in the observed environment of low-level sensory data. In this talk, I will first provide an overview of the recent developments in disentangled representation learning and identify some major trends. I will then present some applications of this powerful concept for robust media processing and understanding in tasks such as image restoration, super-resolution, classification, person re-ID, depth estimation, etc. I will also discuss some future directions.
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为稳健的媒体理解释放解纠缠表示的潜力
有人认为,人工智能要想从根本上理解我们周围的世界,就必须学会识别和解开隐藏在低水平感知数据观察环境中的潜在解释因素。在这次演讲中,我将首先概述解纠缠表示学习的最新发展,并确定一些主要趋势。然后,我将介绍这一强大概念在鲁棒媒体处理和理解方面的一些应用,如图像恢复、超分辨率、分类、人员重新识别、深度估计等任务。我还将讨论一些未来的方向。
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In-training Restoration Models Matter: Data Augmentation for Degraded-reference Image Quality Assessment Unlocking the Potential of Disentangled Representation for Robust Media Understanding Visual Signal Assessment, Analysis and Enhancement for Low-resolution or Varying-illumination Environment Advances of Computational Imaging on Mobile Phones Proceedings of the 2nd International Workshop on Robust Understanding of Low-quality Multimedia Data: Unitive Enhancement, Analysis and Evaluation
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