The pursuit of beauty: Converting image labels to meaningful vectors
This addresses the problem of semantic understanding in computer vision for tasks like image reconstruction and dataset analysis, but appears incremental as it builds on existing label-to-vector conversion methods.
The paper tackles the challenge of understanding image semantics by introducing Occlusion-based Latent Representations (OLR) to convert image labels into meaningful vectors, with results suggesting the model can capture data concepts and discover interrelations.
A challenge of the computer vision community is to understand the semantics of an image, in order to allow image reconstruction based on existing high-level features or to better analyze (semi-)labelled datasets. Towards addressing this challenge, this paper introduces a method, called Occlusion-based Latent Representations (OLR), for converting image labels to meaningful representations that capture a significant amount of data semantics. Besides being informational rich, these representations compose a disentangled low-dimensional latent space where each image label is encoded into a separate vector. We evaluate the quality of these representations in a series of experiments whose results suggest that the proposed model can capture data concepts and discover data interrelations.