478 citations · 1.5k across the 97 of their papers we have counts for
112 papers
BRUM: Robust 3D Vehicle Reconstruction from 360 Sparse Images
Davide Di Nucci, Matteo Tomei, Guido Borghi +3
Accurate 3D reconstruction of vehicles is vital for applications such as vehicle inspection, predictive maintenance, and urban planning. Existing methods like Neural Radiance Field…
DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection
Chiara Cappellino, Gianluca Mancusi, Matteo Mosconi +3
Open-Vocabulary object detectors can generalize to an unrestricted set of categories through simple textual prompting. However, adapting these models to rare classes or reinforcing…
Is Multiple Object Tracking a Matter of Specialization?
Gianluca Mancusi, Mattia Bernardi, Aniello Panariello +3
End-to-end transformer-based trackers have achieved remarkable performance on most human-related datasets. However, training these trackers in heterogeneous scenarios poses signifi…
KRONC: Keypoint-based Robust Camera Optimization for 3D Car Reconstruction
Davide Di Nucci, Alessandro Simoni, Matteo Tomei +3
The three-dimensional representation of objects or scenes starting from a set of images has been a widely discussed topic for years and has gained additional attention after the di…
UNMuTe: Unifying Navigation and Multimodal Dialogue-like Text Generation
Niyati Rawal, Roberto Bigazzi, Lorenzo Baraldi +1
Smart autonomous agents are becoming increasingly important in various real-life applications, including robotics and autonomous vehicles. One crucial skill that these agents must…
BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual Cues
Sara Sarto, Marcella Cornia, Lorenzo Baraldi +1
Effectively aligning with human judgment when evaluating machine-generated image captions represents a complex yet intriguing challenge. Existing evaluation metrics like CIDEr or C…
Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities
Lorenzo Baraldi, Federico Cocchi, Marcella Cornia +2
Discerning between authentic content and that generated by advanced AI methods has become increasingly challenging. While previous research primarily addresses the detection of fak…
Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning
Matteo Mosconi, Andriy Sorokin, Aniello Panariello +6
The use of skeletal data allows deep learning models to perform action recognition efficiently and effectively. Herein, we believe that exploring this problem within the context of…
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…
Towards Retrieval-Augmented Architectures for Image Captioning
Sara Sarto, Marcella Cornia, Lorenzo Baraldi +2
The objective of image captioning models is to bridge the gap between the visual and linguistic modalities by generating natural language descriptions that accurately reflect the c…
Trajectory Forecasting through Low-Rank Adaptation of Discrete Latent Codes
Riccardo Benaglia, Angelo Porrello, Pietro Buzzega +2
Trajectory forecasting is crucial for video surveillance analytics, as it enables the anticipation of future movements for a set of agents, e.g. basketball players engaged in intri…
A Second-Order Perspective on Model Compositionality and Incremental Learning
Angelo Porrello, Lorenzo Bonicelli, Pietro Buzzega +3
The fine-tuning of deep pre-trained models has revealed compositional properties, with multiple specialized modules that can be arbitrarily composed into a single, multi-task model…
AIGeN: An Adversarial Approach for Instruction Generation in VLN
Niyati Rawal, Roberto Bigazzi, Lorenzo Baraldi +1
In the last few years, the research interest in Vision-and-Language Navigation (VLN) has grown significantly. VLN is a challenging task that involves an agent following human instr…
Training-Free Open-Vocabulary Segmentation with Offline Diffusion-Augmented Prototype Generation
Luca Barsellotti, Roberto Amoroso, Marcella Cornia +2
Open-vocabulary semantic segmentation aims at segmenting arbitrary categories expressed in textual form. Previous works have trained over large amounts of image-caption pairs to en…
Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing
Alberto Baldrati, Davide Morelli, Marcella Cornia +2
Fashion illustration is a crucial medium for designers to convey their creative vision and transform design concepts into tangible representations that showcase the interplay betwe…
Unveiling the Truth: Exploring Human Gaze Patterns in Fake Images
Giuseppe Cartella, Vittorio Cuculo, Marcella Cornia +1
Creating high-quality and realistic images is now possible thanks to the impressive advancements in image generation. A description in natural language of your desired output is al…
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition
Roberto Bigazzi, Lorenzo Baraldi, Shreyas Kousik +2
Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments. In the literature, there has been an extensive f…
VATr++: Choose Your Words Wisely for Handwritten Text Generation
Bram Vanherle, Vittorio Pippi, Silvia Cascianelli +3
Styled Handwritten Text Generation (HTG) has received significant attention in recent years, propelled by the success of learning-based solutions employing GANs, Transformers, and,…
Key-Graph Transformer for Image Restoration
Bin Ren, Yawei Li, Jingyun Liang +5
While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, espe…
Trends, Applications, and Challenges in Human Attention Modelling
Giuseppe Cartella, Marcella Cornia, Vittorio Cuculo +4
Human attention modelling has proven, in recent years, to be particularly useful not only for understanding the cognitive processes underlying visual exploration, but also for prov…
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…
Monocular Per-Object Distance Estimation with Masked Object Modeling
Aniello Panariello, Gianluca Mancusi, Fedy Haj Ali +3
Per-object distance estimation is critical in surveillance and autonomous driving, where safety is crucial. While existing methods rely on geometric or deep supervised features, on…
Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
Samuele Poppi, Tobia Poppi, Federico Cocchi +3
Large-scale vision-and-language models, such as CLIP, are typically trained on web-scale data, which can introduce inappropriate content and lead to the development of unsafe and b…
HWD: A Novel Evaluation Score for Styled Handwritten Text Generation
Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli +1
Styled Handwritten Text Generation (Styled HTG) is an important task in document analysis, aiming to generate text images with the handwriting of given reference images. In recent…