Publications by Davide Caffagni

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 architectures and vision-and-language adapters in this paper we concentrate on endowing such models with the capability of answering questions that require external knowledge. Our approach termed Wiki-LLaVA aims at integrating an external knowledge source of multimodal documents which is accessed through a hierarchical retrieval pipeline. Relevant passages using this approach are retrieved from the external knowledge source and employed as additional context for the LLM augmenting the effectiveness and precision of generated dialogues. We conduct extensive experiments on datasets tailored for visual question answering with external data and demonstrate the appropriateness of our approach.

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 efforts are being devoted to the development of Multimodal Large Language Models (MLLMs). These models can seamlessly integrate visual and textual modalities, while providing a dialogue-based interface and instruction-following capabilities. In this paper, we provide a comprehensive review of recent visual-based MLLMs, analyzing their architectural choices, multimodal alignment strategies, and training techniques. We also conduct a detailed analysis of these models across a wide range of tasks, including visual grounding, image generation and editing, visual understanding, and domain-specific applications. Additionally, we compile and describe training datasets and evaluation benchmarks, conducting comparisons among existing models in terms of performance and computational requirements. Overall, this survey offers a comprehensive overview of the current state of the art, laying the groundwork for future MLLMs.

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 maximize hand-crafted captioning metrics. However, when attempting to optimize modern and higher-quality metrics like CLIP-Score and PAC-Score, this training method often encounters instability and fails to acquire the genuine descriptive capabilities needed to produce fluent and informative captions. In this paper, we propose a new training paradigm termed Direct CLIP-Based Optimization (DiCO). Our approach jointly learns and optimizes a reward model that is distilled from a learnable captioning evaluator with high human correlation. This is done by solving a weighted classification problem directly inside the captioner. At the same time, DiCO prevents divergence from the original model, ensuring that fluency is maintained. DiCO not only exhibits improved stability and enhanced quality in the generated captions but also aligns more closely with human preferences compared to existing methods, especially in modern metrics. Additionally, it maintains competitive performance in traditional metrics.

Image captioning is a challenging task that combines Computer Vision and Natural Language Processing to generate descriptive and accurate textual descriptions for input images. Research efforts in this field mainly focus on developing novel architectural components to extend image captioning models and using large-scale image-text datasets crawled from the web to boost final performance. In this work, we explore an alternative to web-crawled data and augment the training dataset with synthetic images generated by a latent diffusion model. In particular, we propose a simple yet effective synthetic data augmentation framework that is capable of significantly improving the quality of captions generated by a standard Transformer-based model, leading to competitive results on the COCO dataset.