This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes representing some African fashion styles. Each sample is coloured and...
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ISBN:
(纸本)9781665448994
This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes representing some African fashion styles. Each sample is coloured and has an image size of 128 x 128. This is a niche dataset that aims to improve visibility, inclusion, and familiarity of African fashion in computervision ***1600 dataset is available here.
The nine papers in this special section focus on the development of new computervision techniques for the interpretation of remote sensing images. These papers represent a follow-up of two workshops held in conjuncti...
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The nine papers in this special section focus on the development of new computervision techniques for the interpretation of remote sensing images. These papers represent a follow-up of two workshops held in conjunction with the ieeeconference on computervision and patternrecognition (CVPR) 2015, that was held in Boston, MA, EARTHvision 2015 and MSF 2015. The purpose of both workshops and of this special issue is to foster fruitful collaboration of computervision, Earth observation, and geospatial analysis communities.
We demonstrate a concept of computervision as a secure, live service on the Internet. We show a platform to distribute a real lime vision algorithm using simple widely available web technologies, such as Adobe Flash....
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ISBN:
(纸本)9781424439942
We demonstrate a concept of computervision as a secure, live service on the Internet. We show a platform to distribute a real lime vision algorithm using simple widely available web technologies, such as Adobe Flash. We allow a user to access this service without downloading an executable or sharing the image stream with anyone. We support developers to publish without distribution complexity Finally the platform supports user-permitted aggregation of data for computervision research or analysis. We describe results a simple distributed motion detection algorithm. We discuss future scenarios for organically extending the horizon of computervision research.
Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on ima...
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ISBN:
(纸本)9781665448994
Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on image (ImageNet-1K) and video (Kinetics-400) understanding show we can achieve 95% sparsity on the self-attention maps while maintaining the performance drop to be less than 2 points. This motivates us to rethink the role of self-attention in vision transformer models.
In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of vari...
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ISBN:
(纸本)9781665448994
In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.
The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translatio...
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ISBN:
(纸本)9781665487399
The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.
Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computervision applications aimed at making high stakes de...
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ISBN:
(纸本)9781665448994
Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computervision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computervision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computervision applications and recommendations.
Given a machine learning model, adversarial perturbations transform images such that the model's output is classified as an attacker chosen class. Most research in this area has focused on adversarial perturbation...
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ISBN:
(数字)9781538661000
ISBN:
(纸本)9781538661000
Given a machine learning model, adversarial perturbations transform images such that the model's output is classified as an attacker chosen class. Most research in this area has focused on adversarial perturbations that are imperceptible to the human eye. However, recent work has considered attacks that are perceptible but localized to a small region of the image. Under this threat model, we discuss both defenses that remove such adversarial perturbations, and attacks that can bypass these defenses.
This work analyzes the problem of homography estimation for robust target matching in the context of real-time mobile vision. We present a device-friendly implementation of the Gaussian Elimination algorithm and show ...
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ISBN:
(纸本)9781479943098
This work analyzes the problem of homography estimation for robust target matching in the context of real-time mobile vision. We present a device-friendly implementation of the Gaussian Elimination algorithm and show that our optimized approach can significantly improve the homography estimation step in a hypothesize-and-verify scheme. Experiments are performed on image sequences in which both speed and accuracy are evaluated and compared with conventional homography estimation schemes.
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