arXiv subjects

Marco Morini

Publications and source records attributed to Marco Morini.

3 recordsLinked to original sources

arXiv2026

Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering

Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visual cues with retrieved textual evidence. However, retrieval often introduces noisy and partially relevant content, while images contain distracting visual regions, causing pretrained MLLMs to overlook the evidence that actually supports the answer. To address this, we introduce Look Twice (LoT), a training-free inference-time framework that turns the model's own internal attention into an explicit multimodal evidence-selection mechanism. LoT first leverages the model's internal attention patterns to identify query-relevant image regions and textual sentences, filters attention sinks and distracting content, and reformulates the input to explicitly highlight the selected evidence before answer generation. The method requires no parameter updates, auxiliary models, or architectural modifications. Across four KB-VQA benchmarks and ten off-the-shelf MLLMs ranging from 2B to 38B parameters, LoT improves every evaluated backbone, with average gains of up to +12.5 accuracy points. It also provides further gains when combined with established context-refinement strategies, yielding additional improvements over already refined inputs. These results establish LoT as a general and effective mechanism for enabling pretrained MLLMs to exploit available multimodal evidence more accurately. Source code is publicly available at https://aimagelab.github.io/LoT/.

cs.CV↗

arXiv2025

ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering

Multimodal Large Language Models (MLLMs) have shown impressive capabilities in jointly understanding text, images, and videos, often evaluated via Visual Question Answering (VQA). However, even state-of-the-art MLLMs struggle with domain-specific or knowledge-intensive queries, where relevant information is underrepresented in pre-training data. Knowledge-based VQA (KB-VQA) addresses this by retrieving external documents to condition answer generation, but current retrieval-augmented approaches suffer from low precision, noisy passages, and limited reasoning. To address this, we propose ReAG, a novel Reasoning-Augmented Multimodal RAG approach that combines coarse- and fine-grained retrieval with a critic model that filters irrelevant passages, ensuring high-quality additional context. The model follows a multi-stage training strategy leveraging reinforcement learning to enhance reasoning over retrieved content, while supervised fine-tuning serves only as a cold start. Extensive experiments on Encyclopedic-VQA and InfoSeek demonstrate that ReAG significantly outperforms prior methods, improving answer accuracy and providing interpretable reasoning grounded in retrieved evidence.

cs.CV↗

arXiv2021

Decoding of Variable Length PLH Codes

In this paper, we address the problem of the decoding of variable length physical layer header (PLH) codes. We take as a case study the DVB-S2X standard, and, after selecting a suitable PLH code with variable codeword length, we propose two alternative noncoherent decoding strategies. The proposed decoding strategies allow to estimate the length of the transmitted PLH field, which is unknown at the receiver, jointly with the decoding of the PLH code. We demonstrate that it is possible to achieve an excellent decoding performance while significantly reducing the overhead due to the transmission of the PLH field with respect to a standard fixed-length PLH.

cs.IT↗