6 citations · 11 across the 11 of their papers we have counts for
6 papers · 1 filter
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.…
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…
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…