30 citations · 140 across the 23 of their papers we have counts for
19 papers · 1 filter
Inhibited Self-Attention: Sharpening Focus in Vision Transformers
Peter R. D. van der Wal, Nicola Strisciuglio, George Azzopardi
Vision Transformers (ViTs) have demonstrated remarkable performance in computer vision tasks. However, their self-attention mechanism often diffuses focus across background regions…
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness
Boqian Wu, Qiao Xiao, Shunxin Wang +5
It is generally perceived that Dynamic Sparse Training opens the door to a new era of scalability and efficiency for artificial neural networks at, perhaps, some costs in accuracy…
PushPull-Net: Inhibition-driven ResNet robust to image corruptions
Guru Swaroop Bennabhaktula, Enrique Alegre, Nicola Strisciuglio +1
We introduce a novel computational unit, termed PushPull-Conv, in the first layer of a ResNet architecture, inspired by the anti-phase inhibition phenomenon observed in the primary…
Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification
Puru Vaish, Shunxin Wang, Nicola Strisciuglio
Computer vision models normally witness degraded performance when deployed in real-world scenarios, due to unexpected changes in inputs that were not accounted for during training.…
Regressing Transformers for Data-efficient Visual Place Recognition
María Leyva-Vallina, Nicola Strisciuglio, Nicolai Petkov
Visual place recognition is a critical task in computer vision, especially for localization and navigation systems. Existing methods often rely on contrastive learning: image descr…
DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning
Shunxin Wang, Christoph Brune, Raymond Veldhuis +1
Neural networks are prone to learn easy solutions from superficial statistics in the data, namely shortcut learning, which impairs generalization and robustness of models. We propo…
A Survey on the Robustness of Computer Vision Models against Common Corruptions
Shunxin Wang, Raymond Veldhuis, Christoph Brune +1
The performance of computer vision models are susceptible to unexpected changes in input images caused by sensor errors or extreme imaging environments, known as common corruptions…
Data-efficient Large Scale Place Recognition with Graded Similarity Supervision
Maria Leyva-Vallina, Nicola Strisciuglio, Nicolai Petkov
Visual place recognition (VPR) is a fundamental task of computer vision for visual localization. Existing methods are trained using image pairs that either depict the same place or…
Brain-inspired algorithms for processing of visual data
Nicola Strisciuglio
The study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compos…
Generalized Contrastive Optimization of Siamese Networks for Place Recognition
María Leyva-Vallina, Nicola Strisciuglio, Nicolai Petkov
Visual place recognition is a challenging task in computer vision and a key component of camera-based localization and navigation systems. Recently, Convolutional Neural Networks (…
MTStereo 2.0: improved accuracy of stereo depth estimation withMax-trees
Rafael Brandt, Nicola Strisciuglio, Nicolai Petkov
Efficient yet accurate extraction of depth from stereo image pairs is required by systems with low power resources, such as robotics and embedded systems. State-of-the-art stereo m…
Place recognition in gardens by learning visual representations: data set and benchmark analysis
Maria Leyva-Vallina, Nicola Strisciuglio, Nicolai Petkov
Visual place recognition is an important component of systems for camera localization and loop closure detection. It concerns the recognition of a previously visited place based on…
A Push-Pull Layer Improves Robustness of Convolutional Neural Networks
Nicola Strisciuglio, Manuel Lopez-Antequera, Nicolai Petkov
We propose a new layer in Convolutional Neural Networks (CNNs) to increase their robustness to several types of noise perturbations of the input images. We call this a push-pull la…
Brain-inspired robust delineation operator
Nicola Strisciuglio, George Azzopardi, Nicolai Petkov
In this paper we present a novel filter, based on the existing COSFIRE filter, for the delineation of patterns of interest. It includes a mechanism of push-pull inhibition that imp…
Learning audio and image representations with bio-inspired trainable feature extractors
Nicola Strisciuglio
Recent advancements in pattern recognition and signal processing concern the automatic learning of data representations from labeled training samples. Typical approaches are based…
Action recognition by learning pose representations
Alessia Saggese, Nicola Strisciuglio, Mario Vento +1
Pose detection is one of the fundamental steps for the recognition of human actions. In this paper we propose a novel trainable detector for recognizing human poses based on the an…
Detection of curved lines with B-COSFIRE filters: A case study on crack delineation
Nicola Strisciuglio, George Azzopardi, Nicolai Petkov
The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to rem…
Delineation of line patterns in images using B-COSFIRE filters
Nicola Strisciuglio, Nicolai Petkov
Delineation of line patterns in images is a basic step required in various applications such as blood vessel detection in medical images, segmentation of rivers or roads in aerial…
Sentiment Recognition in Egocentric Photostreams
Estefania Talavera, Nicola Strisciuglio, Nicolai Petkov +1
Lifelogging is a process of collecting rich source of information about daily life of people. In this paper, we introduce the problem of sentiment analysis in egocentric events foc…