88 citations · 224 across the 20 of their papers we have counts for
17 papers · 1 filter
Self-Balanced R-CNN for Instance Segmentation
Leonardo Rossi, Akbar Karimi, Andrea Prati
Current state-of-the-art two-stage models on instance segmentation task suffer from several types of imbalances. In this paper, we address the Intersection over the Union (IoU) dis…
Semantic Image Synthesis via Class-Adaptive Cross-Attention
Tomaso Fontanini, Claudio Ferrari, Giuseppe Lisanti +2
In semantic image synthesis the state of the art is dominated by methods that use customized variants of the SPatially-Adaptive DE-normalization (SPADE) layers, which allow for goo…
Automatic Generation of Semantic Parts for Face Image Synthesis
Tomaso Fontanini, Claudio Ferrari, Massimo Bertozzi +1
Semantic image synthesis (SIS) refers to the problem of generating realistic imagery given a semantic segmentation mask that defines the spatial layout of object classes. Most of t…
Memory-augmented Online Video Anomaly Detection
Leonardo Rossi, Vittorio Bernuzzi, Tomaso Fontanini +2
The ability to understand the surrounding scene is of paramount importance for Autonomous Vehicles (AVs). This paper presents a system capable to work in an online fashion, giving…
LDD: A Dataset for Grape Diseases Object Detection and Instance Segmentation
Leonardo Rossi, Marco Valenti, Sara Elisabetta Legler +1
The Instance Segmentation task, an extension of the well-known Object Detection task, is of great help in many areas, such as precision agriculture: being able to automatically ide…
Improving Localization for Semi-Supervised Object Detection
Leonardo Rossi, Akbar Karimi, Andrea Prati
Nowadays, Semi-Supervised Object Detection (SSOD) is a hot topic, since, while it is rather easy to collect images for creating a new dataset, labeling them is still an expensive a…
Transferring Knowledge with Attention Distillation for Multi-Domain Image-to-Image Translation
Runze Li, Tomaso Fontanini, Luca Donati +2
Gradient-based attention modeling has been used widely as a way to visualize and understand convolutional neural networks. However, exploiting these visual explanations during the…
Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing
Leonardo Rossi, Akbar Karimi, Andrea Prati
Within the field of instance segmentation, most of the state-of-the-art deep learning networks rely nowadays on cascade architectures, where multiple object detectors are trained s…
A novel Region of Interest Extraction Layer for Instance Segmentation
Leonardo Rossi, Akbar Karimi, Andrea Prati
Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular atte…
Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval
Federico Magliani, Laura Sani, Stefano Cagnoni +1
Several computer vision and artificial intelligence projects are nowadays exploiting the manifold data distribution using, e.g., the diffusion process. This approach has produced d…
An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval
Federico Magliani, Kevin McGuinness, Eva Mohedano +1
The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance. One of the main bottlene…
A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval
Federico Magliani, Tomaso Fontanini, Andrea Prati
The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks. Through the many convolutional layers, available in a Convolutional Neural…
An accurate retrieval through R-MAC+ descriptors for landmark recognition
Federico Magliani, Andrea Prati
The landmark recognition problem is far from being solved, but with the use of features extracted from intermediate layers of Convolutional Neural Networks (CNNs), excellent result…
Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition
Federico Magliani, Tomaso Fontanini, Andrea Prati
The problem of landmark recognition has achieved excellent results in small-scale datasets. When dealing with large-scale retrieval, issues that were irrelevant with small amount o…
A complete hand-drawn sketch vectorization framework
Luca Donati, Simone Cesano, Andrea Prati
Vectorizing hand-drawn sketches is a challenging task, which is of paramount importance for creating CAD vectorized versions for the fashion and creative workflows. This paper prop…
Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets
Yonatan Tariku Tesfaye, Eyasu Zemene, Andrea Prati +2
In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed. Given a video and a set of detections (obt…
A location-aware embedding technique for accurate landmark recognition
Federico Magliani, Navid Mahmoudian Bidgoli, Andrea Prati
The current state of the research in landmark recognition highlights the good accuracy which can be achieved by embedding techniques, such as Fisher vector and VLAD. All these tech…