Hyperparameter Analysis for Image Captioning

06/19/2020
by   Amish Patel, et al.
0

In this paper, we perform a thorough sensitivity analysis on state-of-the-art image captioning approaches using two different architectures: CNN+LSTM and CNN+Transformer. Experiments were carried out using the Flickr8k dataset. The biggest takeaway from the experiments is that fine-tuning the CNN encoder outperforms the baseline and all other experiments carried out for both architectures.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/05/2023

Comparative study of Transformer and LSTM Network with attention mechanism on Image Captioning

In a globalized world at the present epoch of generative intelligence, m...
research
12/17/2020

Efficient CNN-LSTM based Image Captioning using Neural Network Compression

Modern Neural Networks are eminent in achieving state of the art perform...
research
05/03/2016

Improving Image Captioning by Concept-based Sentence Reranking

This paper describes our winning entry in the ImageCLEF 2015 image sente...
research
10/12/2018

Quantifying the amount of visual information used by neural caption generators

This paper addresses the sensitivity of neural image caption generators ...
research
01/06/2023

An Image captioning algorithm based on the Hybrid Deep Learning Technique (CNN+GRU)

Image captioning by the encoder-decoder framework has shown tremendous a...
research
11/24/2017

Convolutional Image Captioning

Image captioning is an important but challenging task, applicable to vir...
research
06/14/2022

Measuring Representational Harms in Image Captioning

Previous work has largely considered the fairness of image captioning sy...

Please sign up or login with your details

Forgot password? Click here to reset