Papers by Mahmoud Azab
Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines (D19-1)
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| Challenge: | Using dialog agents, we can collect family history data from in-person consultations and crowdsource it to a genetic counselor. |
| Approach: | They propose to use natural language interactions annotated with medical family histories to collect information from a genetic counselor and crowdsourcing. |
| Outcome: | The proposed system averages 0.87 on complex sentences on the targeted relations. |
Fighting FIRe with FIRE: Assessing the Validity of Text-to-Video Retrieval Benchmarks (2023.findings-eacl)
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Pedro Rodriguez, Mahmoud Azab, Becka Silvert, Renato Sanchez, Linzy Labson, Hardik Shah, Seungwhan Moon
| Challenge: | Existing benchmarks for text-to-video retrieval are incomplete, resulting in false negatives . a recent state-of-the-art model gains 25% recall points, but this is not the case for TVR. |
| Approach: | They propose to retire video captioning datasets as TVR benchmarks . they propose to annotate and release additional caption-video pairs to mitigate this flaw . |
| Outcome: | The proposed method fails to accurately reflect reality, despite lack of purpose-built benchmarks. |
SIMMC-VR: A Task-oriented Multimodal Dialog Dataset with Situated and Immersive VR Streams (2023.acl-long)
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Te-Lin Wu, Satwik Kottur, Andrea Madotto, Mahmoud Azab, Pedro Rodriguez, Babak Damavandi, Nanyun Peng, Seungwhan Moon
| Challenge: | Existing models lack a large-scale benchmark to capture user–assistant interactions . et al., 2022: 145-160. |
| Approach: | They propose a video-grounded task-oriented dialog dataset that captures real-world AI-assisted user scenarios in VR. |
| Outcome: | The proposed dataset captures real-world AI-assisted user scenarios in VR. |
Speaker Naming in Movies (N18-1)
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| Challenge: | Identifying speakers and their names in movies is a primary task for many video analysis problems, such as automatic subtitle labeling. |
| Approach: | They propose a model that leverages visual, textual, and acoustic modalities in an unified optimization framework for speaker naming in movies. |
| Outcome: | The proposed model outperforms baseline models on the MovieQA 2017 challenge for speaker naming in movies and TV shows on visual, textual, and acoustic modalities. |
Normalized Contrastive Learning for Text-Video Retrieval (2022.emnlp-main)
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| Challenge: | Cross-modal contrastive learning suffers from incorrect normalization of the sum retrieval probabilities of each text or video instance. |
| Approach: | They propose a normalized contrastive learning algorithm that normalizes the sum retrieval probabilities of each instance so that every text and video instance is fairly represented. |
| Outcome: | Empirical results show that NCL brings significant gains in text-video retrieval on different model architectures without any architecture engineering. |
LifeQA: A Real-life Dataset for Video Question Answering (2020.lrec-1)
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Santiago Castro, Mahmoud Azab, Jonathan Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, Rada Mihalcea
| Challenge: | Existing video question answering datasets consist of movies and TV shows, but they are not representative of our day-to-day lives. |
| Approach: | They propose a benchmark dataset for video question answering that focuses on day-to-day situations. |
| Outcome: | The proposed dataset analyzes the challenging but realistic aspects of LifeQA . it consists of video clips and over 2.3k multiple-choice questions . |