activity
20202022
most citedLook Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding

13 citations · 16 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV202213 cited

Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding

Eslam Mohamed Bakr, Yasmeen Alsaedy, Mohamed Elhoseiny

The 3D visual grounding task has been explored with visual and language streams comprehending referential language to identify target objects in 3D scenes. However, most existing m…

cs.CL20221 cited

ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture

Youssef Mohamed, Mohamed Abdelfattah, Shyma Alhuwaider +4

This paper introduces ArtELingo, a new benchmark and dataset, designed to encourage work on diversity across languages and cultures. Following ArtEmis, a collection of 80k artworks…

cs.CV20222 cited

It is Okay to Not Be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection

Youssef Mohamed, Faizan Farooq Khan, Kilichbek Haydarov +1

Datasets that capture the connection between vision, language, and affection are limited, causing a lack of understanding of the emotional aspect of human intelligence. As a step i…

cs.CV2021

ArtEmis: Affective Language for Visual Art

Panos Achlioptas, Maks Ovsjanikov, Kilichbek Haydarov +2

We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional e…

cs.LG2020

Inner Ensemble Networks: Average Ensemble as an Effective Regularizer

Abduallah Mohamed, Muhammed Mohaimin Sadiq, Ehab AlBadawy +2

We introduce Inner Ensemble Networks (IENs) which reduce the variance within the neural network itself without an increase in the model complexity. IENs utilize ensemble parameters…

cs.CL2020

Efficient long-distance relation extraction with DG-SpanBERT

Jun Chen, Robert Hoehndorf, Mohamed Elhoseiny +1

In natural language processing, relation extraction seeks to rationally understand unstructured text. Here, we propose a novel SpanBERT-based graph convolutional network (DG-SpanBE…