Abstract: Textual vision, the fusion of natural language processing and computer vision, has gained significant attention in recent years due to its applications in tasks such as image captioning, text-based image retrieval, and visual question answering. In this paper, we explore the utilization of quantized latent spaces in textual vision tasks. Latent space representations, generated from textual data, capture semantic information essential for understanding and interpreting text. By quantizing these latent spaces, we aim to reduce dimensionality while preserving important semantic features. We present a methodology for generating quantized latent space representations from textual data and discuss the process of quantization using various techniques. Experimental results on benchmark datasets demonstrate the effectiveness of our approach compared to baseline methods. Our findings indicate that leveraging quantized latent spaces enhances the performance of textual vision tasks, paving the way for more efficient and interpretable text-based image processing systems.
Keywords: Quantized Latent Spaces, attention mechanism, VQ-VAE mechanism, Conditional GAN, computer vision (CV).
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DOI:
10.17148/IJARCCE.2024.134118
