Papers by Georgios Tzimiropoulos
More Images, More Problems? A Controlled Analysis of VLM Failure Modes. (2026.findings-acl)
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Anurag Das, Adrian Bulat, Alberto Baldrati, Ioannis Maniadis Metaxas, Bernt Schiele, Georgios Tzimiropoulos, Brais Martinez
| Challenge: | Existing evaluations of large vision language models lack a comprehensive analysis of their weaknesses and causes. |
| Approach: | They propose a new benchmark to evaluate multi-image capabilities of Large Vision Language Models. |
| Outcome: | The proposed model outperforms existing benchmarks on multi-image models. |
Graph Guided Question Answer Generation for Procedural Question-Answering (2024.eacl-long)
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Hai Pham, Isma Hadji, Xinnuo Xu, Ziedune Degutyte, Jay Rainey, Evangelos Kazakos, Afsaneh Fazly, Georgios Tzimiropoulos, Brais Martinez
| Challenge: | a new method for question-answer generation from procedural text is sub-optimal for training QA models. |
| Approach: | They propose a method for generating exhaustive and high-quality training data from procedural text . they use procedural data to represent each step and the overall flow of the procedure as graphs . |
| Outcome: | The proposed method outperforms existing methods on task-specific question answering tasks. |
Efficient Vision-Language pre-training via domain-specific learning for human activities (2024.emnlp-main)
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| Challenge: | Current vision-language models owe their success to large-scale pretraining on web-collected data. |
| Approach: | They propose a domain-aligned pretraining strategy that aligns the downstream tasks to the downstream domain without additional data collection. |
| Outcome: | The proposed method outperforms existing models on large-scale vision-language training datasets while preserving generalist knowledge. |
MobileQuant: Mobile-friendly Quantization for On-device Language Models (2024.findings-emnlp)
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Fuwen Tan, Royson Lee, Łukasz Dudziak, Shell Xu Hu, Sourav Bhattacharya, Timothy Hospedales, Georgios Tzimiropoulos, Brais Martinez
| Challenge: | Large language models (LLMs) have revolutionized language processing, but deployment on edge devices is costly in terms of memory, computation and energy. |
| Approach: | They propose to reduce the number of bits used to represent weights and activations . they propose to use 8-bit activations to enable LLMs to fully exploit mobile-friendly hardware . |
| Outcome: | The proposed method reduces the number of bits used to represent weights and activations . 8-bit activations are attractive for on-device deployment as they would exploit mobile-friendly hardware . |
Vision-Free Retrieval: Rethinking Multimodal Search with Textual Scene Descriptions (2025.emnlp-main)
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| Challenge: | Contrastively trained Vision-Language Models exhibit shallow language understanding, manifesting bag-of-words behaviour. |
| Approach: | They propose a vision-free, single-encoder retrieval pipeline to replace traditional text-to-image retrieval paradigm with structured image descriptions. |
| Outcome: | The proposed approach reduces the modality gap and improves compositionality and performance on short and long caption queries. |
A Simple Baseline for Knowledge-Based Visual Question Answering (2023.emnlp-main)
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| Challenge: | Recent studies emphasize the importance of incorporating both explicit and implicit knowledge to answer questions requiring external knowledge. |
| Approach: | They propose a pipeline that incorporates both explicit and implicit knowledge . their method is training-free and does not require access to external databases or APIs . |
| Outcome: | The proposed method achieves state-of-the-art accuracy on OK-VQA and A-OK-VQ datasets. |