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Works200 from public data
- Benchmark Analysis of Representative Deep Neural Network Architectures831
An in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition, with a complete view of what solutions have been explored so far and in which research directions are worth exploring in the future.
- On the use of deep learning for blind image quality assessment369
The best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple subregions of the original image, having a linear correlation coefficient with human subjective scores of almost 0.91.
- Color constancy using CNNs232
Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods to accurately predict the scene illumination.
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- Evaluating the Performance of Structure from Motion Pipelines186
A comparison of different state-of-the-art SfM pipelines in terms of their ability to reconstruct different scenes is reported and an evaluation procedure is proposed that considers both the reconstruction errors as well as the estimation errors of the camera poses used in the reconstruction.
- Single and Multiple Illuminant Estimation Using Convolutional Neural Networks168
An extensive comparison with both local and global illuminant estimation methods in the state of the art, on standard data sets with single and multiple illuminants, proves the effectiveness of this three-stage method.
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- Deep learning for logo recognition126
The proposed recognition pipeline is composed of a logo region proposal followed by a Convolutional Neural Network specifically trained for logo classification, even if they are not precisely localized.
- How Far Can You Get by Combining Change Detection Algorithms?121
Results demonstrate that starting from simple algorithms the proposed IUTIS combination strategy can achieve comparable results with respect to more complex state-of-the-art change detection algorithms, while keeping the computational complexity affordable for real-time applications.
- Automatic color constancy algorithm selection and combination119
This work investigates how illuminant estimation techniques can be improved taking into account intrinsic, low level properties of the images, and shows how these properties can be used to drive the selection of the best algorithm for a given image.
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- An interactive tool for manual, semi-automatic and automatic video annotation108
Results demonstrate that the use of the semi-automatic, as well as the automatic, modality drastically reduces the human effort while preserving the quality of the annotations.
- An introduction to quantum machine learning: from quantum logic to quantum deep learning98
The most notable scientific literature about quantum machine learning is presented, starting from the basics of quantum logic to some specific elements and algorithms of quantum computing (such as QRAM, Grover and HHL), in order to allow a better understanding of latest quantumMachine learning techniques.
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- Large Age-Gap face verification by feature injection in deep networks90
A new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old is introduced.
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- Image Analysis and Processing – ICIAP 200967
This book constitutes the refereed proceedings of the 15th International Conference on Image Analysis and Processing, ICIAP 2009, held in Vietri sul Mare, Italy, in September 2009.
- Automated Pruning for Deep Neural Network Compression60
This is the first study where the generalization capabilities in transfer learning tasks of the features extracted by a pruned network are analyzed, and it is shown that the representations learned using the proposed pruning methodology maintain the same effectiveness and generality of those learned by the corresponding non-compressed network on a set of different recognition tasks.
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- Multitask painting categorization by deep multibranch neural network58
Among the different variants of the proposed network, the best method achieves accuracy levels of 56.5%, 57.2%, and 63.6% on the MultitaskPainting100k dataset for the tasks of artist, style and genre prediction respectively.
- NTIRE 2024 Quality Assessment of AI-Generated Content Challenge54
This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC).
- Rehabilitating the ColorChecker Dataset for Illuminant Estimation50
A new 'recommended' set of ground-truth is re-generated based on the calculation methodology described by Shi and Funt and it is found that the difference in how algorithms perform can be large, with many local rankings of algorithms being reversed.
- Consensus-based framework for illuminant chromaticity estimation50
It is confirmed that the best and the worst algorithms do not exist at all among the state-of-the-art ones and show that simple combining strategies improve the illuminant estimation.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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