3 citations · 6 across the 12 of their papers we have counts for
8 papers · 1 filter
Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis
Davide Bucciarelli, Nicholas Moratelli, Marcella Cornia +2
The task of image captioning demands an algorithm to generate natural language descriptions of visual inputs. Recent advancements have seen a convergence between image captioning r…
Causal Graphical Models for Vision-Language Compositional Understanding
Fiorenzo Parascandolo, Nicholas Moratelli, Enver Sangineto +2
Recent work has empirically shown that Vision-Language Models (VLMs) struggle to fully understand the compositional properties of the human language, usually modeling an image capt…
Augmenting Multimodal LLMs with Self-Reflective Tokens for Knowledge-based Visual Question Answering
Federico Cocchi, Nicholas Moratelli, Marcella Cornia +2
Multimodal LLMs (MLLMs) are the natural extension of large language models to handle multimodal inputs, combining text and image data. They have recently garnered attention due to…
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…
Fluent and Accurate Image Captioning with a Self-Trained Reward Model
Nicholas Moratelli, Marcella Cornia, Lorenzo Baraldi +1
Fine-tuning image captioning models with hand-crafted rewards like the CIDEr metric has been a classical strategy for promoting caption quality at the sequence level. This approach…
Revisiting Image Captioning Training Paradigm via Direct CLIP-based Optimization
Nicholas Moratelli, Davide Caffagni, Marcella Cornia +2
The conventional training approach for image captioning involves pre-training a network using teacher forcing and subsequent fine-tuning with Self-Critical Sequence Training to max…
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…
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…