Abstract:

Generating realistic images from textual descriptions is a challenging task at the intersection of computer vision and natural language processing. This paper presents a novel approach that combines Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) to bridge the gap between text and image generation. The proposed model utilizes an RNN, such as a Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU), to encode sequential text data into meaningful feature representations.

Description:

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