activity
20182022
most citedGuiding Attention using Partial-Order Relationships for Image Captioning

1 citations · 1 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CV20221 cited

Guiding Attention using Partial-Order Relationships for Image Captioning

Murad Popattia, Muhammad Rafi, Rizwan Qureshi +1

The use of attention models for automated image captioning has enabled many systems to produce accurate and meaningful descriptions for images. Over the years, many novel approache…

cs.CV2019

Picture What you Read

Ignazio Gallo, Shah Nawaz, Alessandro Calefati +2

Visualization refers to our ability to create an image in our head based on the text we read or the words we hear. It is one of the many skills that makes reading comprehension pos…

cs.CV2019

Do Cross Modal Systems Leverage Semantic Relationships?

Shah Nawaz, Muhammad Kamran Janjua, Ignazio Gallo +3

Current cross-modal retrieval systems are evaluated using R@K measure which does not leverage semantic relationships rather strictly follows the manually marked image text query pa…

cs.CV2018

Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions

Muhammad Kamran Janjua, Shah Nawaz, Alessandro Calefati +1

Majority of the current dimensionality reduction or retrieval techniques rely on embedding the learned feature representations onto a computable metric space. Once the learned feat…

cs.CV2018

Seeing Colors: Learning Semantic Text Encoding for Classification

Shah Nawaz, Alessandro Calefati, Muhammad Kamran Janjua +1

The question we answer with this work is: can we convert a text document into an image to exploit best image classification models to classify documents? To answer this question we…

cs.CV2018

Revisiting Cross Modal Retrieval

Shah Nawaz, Muhammad Kamran Janjua, Alessandro Calefati +1

This paper proposes a cross-modal retrieval system that leverages on image and text encoding. Most multimodal architectures employ separate networks for each modality to capture th…