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Found 25 papers, 10 papers with code
Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification
However, common visual augmentations might not guarantee extensive robustness of computer vision models.
CAST: Clustering Self-Attention using Surrogate Tokens for Efficient Transformers
The self-attention from within each cluster is then combined with the cluster summaries of other clusters, enabling information flow across the entire input sequence.
Regressing Transformers for Data-efficient Visual Place Recognition
Visual place recognition is a critical task in computer vision, especially for localization and navigation systems.
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis.
DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning
We propose a data augmentation strategy, named DFM-X, that leverages knowledge about frequency shortcuts, encoded in Dominant Frequencies Maps computed for image classification models.
Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space
We train autoencoders with implicit and explicit regularization techniques to enforce linearity relations among the representations of different blur levels in the latent space.
What do neural networks learn in image classification? A frequency shortcut perspective
Our results demonstrate that NNs tend to find simple solutions for classification, and what they learn first during training depends on the most distinctive frequency characteristics, which can be either low- or high-frequencies.
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations
In this work, we focus on a limitation of neural network-based atlas building and statistical latent modeling methods, namely that they either are (i) resolution dependent or (ii) disregard any data/problem-specific geometry needed for proper mean-variance analysis.
A Survey on the Robustness of Computer Vision Models against Common Corruptions
The performance of computer vision models are susceptible to unexpected changes in input images, known as common corruptions (e. g. noise, blur, illumination changes, etc.
Data-efficient Large Scale Place Recognition with Graded Similarity Supervision
Motivated by the fact that two images of the same place only partially share visual cues due to camera pose differences, we deploy an automatic re-annotation strategy to re-label VPR datasets.
Ranked #1 on
Visual Place Recognition
on MSLS
Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers
We complement previous surveys by providing a comprehensive analysis of recently published methods that deploy deep learning approaches.
Burr detection and classification using RUSTICO and image processing
Machined workpieces must satisfy quality standards such as avoid the presence of burrs in edge finishing to reduce production costs and time.
Ranked #1 on
Classification
on Burr classification images
Generalized Contrastive Optimization of Siamese Networks for Place Recognition
We propose a Generalized Contrastive loss (GCL) function that relies on image similarity as a continuous measure, and use it to train a siamese CNN.
Ranked #3 on
Visual Place Recognition
on Mapillary test
Brain-inspired algorithms for processing of visual data
These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing.
Inhibition-augmented ConvNets
The rectified responses of the push and pull filter pairs are then combined by a linear function.
MTStereo 2.0: improved accuracy of stereo depth estimation withMax-trees
Efficient yet accurate extraction of depth from stereo image pairs is required by systems with low power resources, such as robotics and embedded systems.
Place recognition in gardens by learning visual representations: data set and benchmark analysis
In this paper we propose an extended version of the TB-Places data set, which is designed for testing algorithms for visual place recognition.
A Push-Pull Layer Improves Robustness of Convolutional Neural Networks
We propose a new layer in Convolutional Neural Networks (CNNs) to increase their robustness to several types of noise perturbations of the input images.
Learning sound representations using trainable COPE feature extractors
We construct a set of COPE feature extractors, configured on a number of training patterns.
Brain-inspired robust delineation operator
This type of inhibition allows for sharper detection of the patterns of interest and improves the quality of delineation especially in images with spurious texture.
Learning audio and image representations with bio-inspired trainable feature extractors
Recent advancements in pattern recognition and signal processing concern the automatic learning of data representations from labeled training samples.
Action recognition by learning pose representations
Starting from this consideration, we propose a trainable pose detector, that can be configured on a prototype skeleton in an automatic configuration process.
Detection of curved lines with B-COSFIRE filters: A case study on crack delineation
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 remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others.
Delineation of line patterns in images using B-COSFIRE filters
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 images, detection of cracks in walls or pavements, etc.
Sentiment Recognition in Egocentric Photostreams
Lifelogging is a process of collecting rich source of information about daily life of people.
