mlsanityData to 14 Oct 2026

It Isn't Sh!tposting, It's My CAT Posting

arXiv 2205.08710 ↗First posted 18 May 2022cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 17 references that are in the map. First posted on arXiv 18 May 2022.

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12 of 17 references.

References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2016Densely Connected Convolutional Networkscited by 6,720
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  5. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  6. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  7. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,423
  8. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
  9. 2014Show and Tell: A Neural Image Caption Generatorcited by 879
  10. 2014Long-term Recurrent Convolutional Networks for Visual Recognition and Descriptioncited by 484
  11. 2016Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioningcited by 232
  12. 2015Language Models for Image Captioning: The Quirks and What Workscited by 19

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