It Isn't Sh!tposting, It's My CAT Posting
This work addresses the need for everyday users to create entertaining content with minimal effort, though it appears incremental as it builds on existing image captioning and text transformation methods.
The paper tackles the problem of generating humorous captions for images by introducing a two-part architecture that first creates a normal caption and then transforms it into a hilarious version while preserving context, resulting in a system called CATNet that enables users to produce funny captions easily.
In this paper, we describe a novel architecture which can generate hilarious captions for a given input image. The architecture is split into two halves, i.e. image captioning and hilarious text conversion. The architecture starts with a pre-trained CNN model, VGG16 in this implementation, and applies attention LSTM on it to generate normal caption. These normal captions then are fed forward to our hilarious text conversion transformer which converts this text into something hilarious while maintaining the context of the input image. The architecture can also be split into two halves and only the seq2seq transformer can be used to generate hilarious caption by inputting a sentence.This paper aims to help everyday user to be more lazy and hilarious at the same time by generating captions using CATNet.