Fajtl, Jiri (2021) Visual memories. (PhD thesis), Kingston University, .
Abstract
Despite the rapid progress in the field of artificial intelligence, there are still important new areas to be explored and existing methods enhanced to make machines think like humans. This thesis conducts research in four machine learning and computer vision areas in this direction. First, we study what makes some images more memorable than others and propose a new machine learning method to learn and predict image memorability, closely matching human performance. A spatial attention function is learnt to localize image regions responsible for the image retention in memory. To identify meaningful temporal segments in a video stream, we study episodic segmentation in our memory and design a novel algorithm for video summarization to mimic human capabilities. A soft, self-attention method without a recurrent network is used to learn frame importance scores for the video summarization. This simple algorithm demonstrates a performance superior to the current state-of-the-art methods. Inspired by our brain’s ability to project high dimensional visual information to computationally efficient, meaningful representations, we propose a method for latent binary representations learning and methods for operations in this discrete latent space such as interpolation, novel image generation, and attribute modification outperforming more complex published methods. To advance methods targeting catastrophic interference, one of the most fundamental problems of artificial neural networks, we study elementary neural mechanisms mitigating this phenomenon in our brain’s memory. Building on our insights on the function of pattern separation in the hippocampus, we propose a conceptually simple and resource-efficient method to learn high dimensional sparse binary representations for continual learning. By performing elementary binary operations or and and over a continual stream of sparse representations of novel classes, our method exhibits performance significantly exceeding the current state-of-the-art meta-learning methods on identical benchmarks.
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