6 citations · 11 across the 11 of their papers we have counts for
12 papers
Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model
Gregor Geigle, Florian Schneider, Carolin Holtermann +4
Most Large Vision-Language Models (LVLMs) to date are trained predominantly on English data, which makes them struggle to understand non-English input and fail to generate output i…
African or European Swallow? Benchmarking Large Vision-Language Models for Fine-Grained Object Classification
Gregor Geigle, Radu Timofte, Goran Glavaš
Recent Large Vision-Language Models (LVLMs) demonstrate impressive abilities on numerous image understanding and reasoning tasks. The task of fine-grained object classification (e.…
Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models?
Gregor Geigle, Radu Timofte, Goran Glavaš
Large vision-language models (LVLMs) have recently dramatically pushed the state of the art in image captioning and many image understanding tasks (e.g., visual question answering)…
InstructIR: High-Quality Image Restoration Following Human Instructions
Marcos V. Conde, Gregor Geigle, Radu Timofte
Image restoration is a fundamental problem that involves recovering a high-quality clean image from its degraded observation. All-In-One image restoration models can effectively re…
mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs
Gregor Geigle, Abhay Jain, Radu Timofte +1
Modular vision-language models (Vision-LLMs) align pretrained image encoders with (frozen) large language models (LLMs) and post-hoc condition LLMs to `understand' the image input.…
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations
Gregor Geigle, Radu Timofte, Goran Glavaš
Vision-and-language (VL) models with separate encoders for each modality (e.g., CLIP) have become the go-to models for zero-shot image classification and image-text retrieval. They…
One does not fit all! On the Complementarity of Vision Encoders for Vision and Language Tasks
Gregor Geigle, Chen Cecilia Liu, Jonas Pfeiffer +1
Current multimodal models, aimed at solving Vision and Language (V+L) tasks, predominantly repurpose Vision Encoders (VE) as feature extractors. While many VEs -- of different arch…
UKP-SQUARE: An Online Platform for Question Answering Research
Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva +10
Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e.g., extractive, abstractive), require…
xGQA: Cross-Lingual Visual Question Answering
Jonas Pfeiffer, Gregor Geigle, Aishwarya Kamath +4
Recent advances in multimodal vision and language modeling have predominantly focused on the English language, mostly due to the lack of multilingual multimodal datasets to steer m…
TWEAC: Transformer with Extendable QA Agent Classifiers
Gregor Geigle, Nils Reimers, Andreas Rücklé +1
Question answering systems should help users to access knowledge on a broad range of topics and to answer a wide array of different questions. Most systems fall short of this expec…
Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval
Gregor Geigle, Jonas Pfeiffer, Nils Reimers +2
Current state-of-the-art approaches to cross-modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that…
AdapterDrop: On the Efficiency of Adapters in Transformers
Andreas Rücklé, Gregor Geigle, Max Glockner +4
Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements. Recent approaches tackle these shortco…