• Vishnu
• V. Veera Nagendra
• V.Vaishnavi
• V.Varshitha Reddy
• DR P Venkateswara Rao

Image-text matching is an important task in computer vision and natural language processing that aims to find the semantic relationship between an image and its corresponding text description. One approach to image-text matching is deep learning, which has shown promising results in various computer vision and natural language processing tasks. The first step is to pre-process the image and text data to make them suitable for deep learning models. For images, this may involve resizing, cropping, and normalization. For text, this may involve tokenization, stopword removal, and stemming. In image processing, this may involve using convolutional neural networks (CNNs) to extract features from images. In natural language processing, this may involve using pre-trained language models such as BERT or GPT to extract features from text. Once the features are extracted, the next step is to create a common embedding space for both the image and text features. This can be done by training a deep learning model that learns to map both the image and text features into a shared embedding space. The final step is to measure the similarity between the embedded image and text features. This can be done using various similarity measures such as cosine similarity or Euclidean distance.

Resnet-101, MS COCO,Word2Vec,Corpus,Cross-gram model, Dual Path

"A DEEP LEARNING APPROACH TO IMAGE-TEXT EMBEDDING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.c381-c386, July-2023, Available :http://www.jetir.org/papers/JETIR2307244.pdf

"A DEEP LEARNING APPROACH TO IMAGE-TEXT EMBEDDING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppc381-c386, July-2023, Available at : http://www.jetir.org/papers/JETIR2307244.pdf