Comparative Evaluation of CNN Architectures for Image Caption Generation

Citations Over TimeTop 10% of 2020 papers

Abstract

Aided by recent advances in Deep Learning, Image Caption Generation has seen tremendous progress over the last few years. Most methods use transfer learning to extract visual information, in the form of image features, with the help of pre-trained Convolutional Neural Network models followed by transformation of the visual information using a Caption Generator module to generate the output sentences. Different methods have used different Convolutional Neural Network Architectures and, to the best of our knowledge, there is no systematic study which compares the relative efficacy of different Convolutional Neural Network architectures for extracting the visual information. In this work, we have evaluated 17 different Convolutional Neural Networks on two popular Image Caption Generation frameworks: the first based on Neural Image Caption (NIC) generation model and the second based on Soft-Attention framework. We observe that model complexity of Convolutional Neural Network, as measured by number of parameters, and the accuracy of the model on Object Recognition task does not necessarily co-relate with its efficacy on feature extraction for Image Caption Generation task. We release the code at https://github.com/iamsulabh/cnn variants.

Related Papers

• → Implementing convolutional neural network model for prediction in medical imaging(2022)6 cited
• → Leaf Features Extraction for Plant Classification using CNN(2021)7 cited
• → Deep Convolution Neural Network for RBC Images(2022)2 cited
• → CNN-based Transfer Learning for Covid-19 Diagnosis(2021)3 cited
• → Deep Convolutional Neural Networks(2021)8 cited