Adnen Abdessaied

Institute for Visualisation and Interactive Systems, University of Stuttgart

+49 711 685 60052 Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.035 Google Scholar

Biography

Adnen Abdessaied is a PhD student in the Perceptual User Interface group since March 2021. He holds a Bachelor's and Master's degree in Simulation Technology (SimTech) from the University of Stuttgart. The latter was obtained with an overall verdict of very good with distinction and won the Outstanding Work Award from the Industrial Consortium SimTech (IC SimTech). In his Bachelor's studies, he focused on numerical mathematics and classical machine learning techniques. In his master's studies, he got involved in deep multi-modal memory-enhanced neural networks that are inspired by the human brain. His research interests lie in the design and interpretability of multi-modal hybrid Neuro-Symbolic AI models.

Teaching

2021

Machine Learning and Computer Vision for HCI (Fachpraktikum)Master

Open Thesis Projects

 

Multi-Step Visual Reasoning in Video Grounded DialogMaster

 

Diagnostic Dataset for Video Dialog based on CLEVRERMaster

Publications

1. 

   Video Language Co-Attention with Multimodal Fast-Learning Feature Fusion for VideoQA

   Adnen Abdessaied*, Ekta Sood*, Andreas Bulling

   Proc. of the 7th Workshop on Representation Learning for NLP (Repl4NLP), pp. 1–12, 2022.

   Abstract Links BibTeX Project

   [Equal contribution by the first two authors.] We propose the Video Language Co-Attention Network (VLCN) – a novel memory-enhanced model for Video Question Answering (VideoQA). Our model combines two original contributions: A multimodal fast-learning feature fusion (FLF) block and a mechanism that uses self-attended language features to separately guide neural attention on both static and dynamic visual features extracted from individual video frames and short video clips. When trained from scratch, VLCN achieves competitive results with the state of the art on both MSVD-QA and MSRVTT-QA with 38.06% and 36.01% test accuracies, respectively. Through an ablation study, we further show that FLF improves generalization across different VideoQA datasets and performance for question types that are notoriously challenging in current datasets, such as long questions that require deeper reasoning as well as questions with rare answers.

   Paper: abdessaied22_repl4NLP.pdf

   Code: https://git.hcics.simtech.uni-stuttgart.de/public-projects/vlcn

   @inproceedings{abdessaied22_repl4NLP, author = {Abdessaied*, Adnen and Sood*, Ekta and Bulling, Andreas}, title = {Video Language Co-Attention with Multimodal Fast-Learning Feature Fusion for VideoQA}, booktitle = {Proc. of the 7th Workshop on Representation Learning for NLP (Repl4NLP)}, year = {2022}, pages = {1--12} }