3 citations · 4 across the 9 of their papers we have counts for
11 papers
Mind the Heads: Topological Representation Alignment for Multimodal LLMs
Davide Caffagni, Alberto Compagnoni, Federico Melis +5
Representation alignment has emerged as an effective approach to improve Multimodal Large Language Models (MLLMs) by regularizing their internal representations toward those of an…
Seeing Beyond Words: Self-Supervised Visual Learning for Multimodal Large Language Models
Davide Caffagni, Sara Sarto, Marcella Cornia +5
Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in connecting vision and language, yet their proficiency in fundamental visual reasoning…
ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering
Alberto Compagnoni, Marco Morini, Sara Sarto +5
Multimodal Large Language Models (MLLMs) have shown impressive capabilities in jointly understanding text, images, and videos, often evaluated via Visual Question Answering (VQA).…
Recurrence Meets Transformers for Universal Multimodal Retrieval
Davide Caffagni, Sara Sarto, Marcella Cornia +2
With the rapid advancement of multimodal retrieval and its application in LLMs and multimodal LLMs, increasingly complex retrieval tasks have emerged. Existing methods predominantl…
Mitigating Hallucinations in Multimodal LLMs via Object-aware Preference Optimization
Alberto Compagnoni, Davide Caffagni, Nicholas Moratelli +3
Multimodal Large Language Models (MLLMs) emerge as a unified interface to address a multitude of tasks, ranging from NLP to computer vision. Despite showcasing state-of-the-art res…
TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos
Fanheng Kong, Jingyuan Zhang, Hongzhi Zhang +7
Videos are unique in their integration of temporal elements, including camera, scene, action, and attribute, along with their dynamic relationships over time. However, existing ben…
Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval
Davide Caffagni, Sara Sarto, Marcella Cornia +2
Cross-modal retrieval is gaining increasing efficacy and interest from the research community, thanks to large-scale training, novel architectural and learning designs, and its app…
LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction Tuning
Federico Cocchi, Nicholas Moratelli, Davide Caffagni +4
Recent progress in Multimodal Large Language Models (MLLMs) has highlighted the critical roles of both the visual backbone and the underlying language model. While prior work has p…
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…