Papers by Gregor Geigle
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations (2024.acl-long)
Copied to clipboard
| Challenge: | Vision-and-language models with separate encoders for each modality are limited in availability. |
| Approach: | They propose a multilingual benchmark that offers (partial) translations of ImageNet labels to 100 languages, built without machine translation or manual annotation. |
| Outcome: | The proposed model outperforms models on English and low-resource languages. |
FigMemes: A Dataset for Figurative Language Identification in Politically-Opinionated Memes (2022.emnlp-main)
Copied to clipboard
| Challenge: | FigMemes is a dataset for figurative language classification in politically-opinionated memes. |
| Approach: | They propose to use figurative language classification to identify politically-opinionated memes by analyzing their datasets and comparing them to other machine learning models. |
| Outcome: | The proposed dataset includes annotations of six commonly used types of figurative language in politically-opinionated memes and a wide range of topics and visual styles. |
AdapterDrop: On the Efficiency of Adapters in Transformers (2021.emnlp-main)
Copied to clipboard
Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, Iryna Gurevych
| Challenge: | Recent approaches to transformer models are expensive to fine-tune, slow for inference, and have large storage requirements. |
| Approach: | They propose a method to remove adapters from transformer layers during training and inference . they show that AdapterDrop can dynamically reduce computational overhead . |
| Outcome: | The proposed approach reduces computational overhead while maintaining performance over multiple tasks with minimal loss of performance. |
xGQA: Cross-Lingual Visual Question Answering (2022.findings-acl)
Copied to clipboard
Jonas Pfeiffer, Gregor Geigle, Aishwarya Kamath, Jan-Martin Steitz, Stefan Roth, Ivan Vulić, Iryna Gurevych
| Challenge: | a lack of multilingual multimodal datasets has hindered multimodal vision and language modeling efforts. |
| Approach: | They propose a multilingual evaluation benchmark for the visual question answering task . they extend the established English GQA dataset to 7 typologically diverse languages . |
| Outcome: | The proposed methods outperform current state-of-the-art models in zero-shot cross-lingual settings, but the accuracy remains low across languages. |
African or European Swallow? Benchmarking Large Vision-Language Models for Fine-Grained Object Classification (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent Large Vision Language Models demonstrate impressive abilities on image understanding and reasoning tasks. |
| Approach: | They propose a benchmark for fine-grained object classification that is difficult to evaluate . they benchmark 12 public LVLMs on and show CLIP models exhibit better performance . |
| Outcome: | The proposed model improves on 12 public LVLMs on image understanding and reasoning tasks. |
Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval (2022.tacl-1)
Copied to clipboard
| Challenge: | Current approaches to cross-modal retrieval process text and visual input jointly . current approaches are pretrained from scratch and suffer from huge retrieval latency and inefficiency issues . |
| Approach: | They propose a cooperative retrieve-and-rerank framework that turns pretrained text-image multi-modal models into efficient retrieval models. |
| Outcome: | The proposed framework improves retrieval performance over current approaches . it uses twin networks to encode all items of a corpus and a cross-encoder component for a more nuanced ranking . |
UKP-SQUARE: An Online Platform for Question Answering Research (2022.acl-demo)
Copied to clipboard
Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva, Gregor Geigle, Max Eichler, Clifton Poth, Hannah Sterz, Haritz Puerto, Leonardo F. R. Ribeiro, Jonas Pfeiffer, Nils Reimers, Gözde Şahin, Iryna Gurevych
| Challenge: | Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats and require different model architectures and setups. |
| Approach: | They propose an extensible online QA platform that allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. |
| Outcome: | The proposed tool allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. |
Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large vision-language models (LVLMs) often hallucinate and produce captions that mention concepts that cannot be found in the image. |
| Approach: | They propose to add grounding objectives to captions that explicitly align image regions or objects to text spans to reduce hallucination. |
| Outcome: | The proposed evaluation protocol reduces the amount of hallucination in LVLMs by adding grounding objectives. |
Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model (2025.acl-long)
Copied to clipboard
Gregor Geigle, Florian Schneider, Carolin Holtermann, Chris Biemann, Radu Timofte, Anne Lauscher, Goran Glavaš
| Challenge: | Existing models for large vision-language tasks are trained on English data, which makes them struggle to understand non-English input and fail to generate output in the desired target language. |
| Approach: | They conduct multi-stage experiments on 13 vision-language tasks and 43 languages . they find that one can include as many as 100 training languages simultaneously with as little as 25-50% of non-English data . |
| Outcome: | The proposed model outperforms existing models in 14 tasks and 56 languages. |