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20162026
most citedWorking Memory Connections for LSTM

282 citations · 328 across the 54 of their papers we have counts for

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12 papers · 1 filter

cs.CV2025

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…

cs.CV2025

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).…

cs.GR2025

SVGauge: Towards Human-Aligned Evaluation for SVG Generation

Leonardo Zini, Elia Frigieri, Sebastiano Aloscari +4

Generated Scalable Vector Graphics (SVG) images demand evaluation criteria tuned to their symbolic and vectorial nature: criteria that existing metrics such as FID, LPIPS, or CLIPS…

cs.CV2025

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…

cs.CL2025

Temperature Matters: Enhancing Watermark Robustness Against Paraphrasing Attacks

Badr Youbi Idrissi, Monica Millunzi, Amelia Sorrenti +2

In the present-day scenario, Large Language Models (LLMs) are establishing their presence as powerful instruments permeating various sectors of society. While their utility offers…

cs.CV2025

RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors

Hicham Eddoubi, Jonas Ricker, Federico Cocchi +7

AI-generated images have reached a quality level at which humans are incapable of reliably distinguishing them from real images. To counteract the inherent risk of fraud and disinf…