9 citations · 14 across the 19 of their papers we have counts for
5 papers · 1 filter
Positive-Augmented Contrastive Learning for Vision-and-Language Evaluation and Training
Sara Sarto, Nicholas Moratelli, Marcella Cornia +2
Despite significant advancements in caption generation, existing evaluation metrics often fail to capture the full quality or fine-grained details of captions. This is mainly due t…
BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual Cues
Sara Sarto, Marcella Cornia, Lorenzo Baraldi +1
Effectively aligning with human judgment when evaluating machine-generated image captions represents a complex yet intriguing challenge. Existing evaluation metrics like CIDEr or C…
Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs
Davide Caffagni, Federico Cocchi, Nicholas Moratelli +4
Multimodal LLMs are the natural evolution of LLMs, and enlarge their capabilities so as to work beyond the pure textual modality. As research is being carried out to design novel a…
Towards Retrieval-Augmented Architectures for Image Captioning
Sara Sarto, Marcella Cornia, Lorenzo Baraldi +2
The objective of image captioning models is to bridge the gap between the visual and linguistic modalities by generating natural language descriptions that accurately reflect the c…
The Revolution of Multimodal Large Language Models: A Survey
Davide Caffagni, Federico Cocchi, Luca Barsellotti +5
Connecting text and visual modalities plays an essential role in generative intelligence. For this reason, inspired by the success of large language models, significant research ef…