Advanced Multimodal Deep Learning Architecture for Image

In this paper, we propose a novel self-supervised deep learning based approach towards learning the cross-modal embedding spaces; for both image to text and ...

We innovatively design an advanced multimodal deep learning architecture, which combines the high-level abstract representation ability of deep neural networks ...

Use Convolutional Recurrent Neural Network to recognize the text image without pre segmentation into words or characters. Use CTC loss Function to train.

Image to text models output a text from a given image. Image captioning or optical character recognition can be considered as the most common applications of ...

This study tries to determine which of the two algorithms has the best feature extraction method. A dataset of arbitrary real-time photos with text is ...

Abstract. The key point of image-text matching is how to accurately measure the similarity between visual and textual inputs. Despite the.

We're introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision.

In this paper, an image-to-text description approach based on Inception-ResNetV2-LSTM with an attention technique is proposed for effective textual ...

Convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) are used in a unique method for text detection.

We have developed a model using convolutional neural networks (CNN)[4] and bi-directional long short term memory (Bi-LSTM) to extract text from images.