1,720,964 research outputs found
Deep Spiking Convolutional Neural Network for Single Object Localization Based On Deep Continuous Local Learning
No full textInternational audienceWith the advent of neuromorphic hardware, spiking neural networks can be a good energy-efficient alternative to artificial neural networks. However, the use of spiking neural networks to perform computer vision tasks remains limited, mainly focusing on simple tasks such as digit recognition. It remains hard to deal with more complex tasks (e.g. segmentation, object detection) due to the small number of works on deep spiking neural networks for these tasks. The objective of this paper is to make the first step towards modern computer vision with supervised spiking neural networks. We propose a deep convolutional spiking neural network for the localization of a single object in a grayscale image. We propose a network based on DECOLLE, a spiking model that enables local surrogate gradient-based learning. The encouraging results reported on Oxford-IIIT-Pet validates the exploitation of spiking neural networks with a supervised learning approach for more elaborate vision tasks in the future
Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks
No full textInternational audienceMany research works focus on leveraging the complementary geometric information of indoor depth sensors in vision tasks performed by deep convolutional neural networks, notably semantic segmentation. These works deal with a specific vision task known as "RGB-D Indoor Semantic Segmentation". The challenges and resulting solutions of this task differ from its standard RGB counterpart. This results in a new active research topic. The objective of this paper is to introduce the field of Deep Convolutional Neural Networks for RGB-D Indoor Semantic Segmentation. This review presents the most popular public datasets, proposes a categorization of the strategies employed by recent contributions, evaluates the performance of the current state-of-the-art, and discusses the remaining challenges and promising directions for future works
Exploring Joint Embedding Architectures and Data Augmentations for Self-Supervised Representation Learning in Event-Based Vision
No full textInternational audienceThis paper proposes a self-supervised representation learning (SSRL) framework for event-based vision, which leverages various lightweight convolutional neural networks (CNNs) including 2D-, 3D-, and Spiking CNNs. The method uses a joint embedding architecture to maximize the agreement between features extracted from different views of the same event sequence. Popular event data augmentation techniques are employed to design an efficient augmentation policy for event-based SSRL, and we provide novel data augmentation methods to enhance the pretraining pipeline. Given the novelty of SSRL for event-based vision, we elaborate standard evaluation protocols and use them to evaluate our approach. Our study demonstrates that pretrained CNNs acquire effective and transferable features, enabling them to achieve competitive performance in object or action recognition across various commonly used event-based datasets, even in a low-data regime. This paper also conducts an experimental analysis of the extracted features regarding the Uniformity-Tolerance tradeoff to assess their quality, and measure the similarity of representations using linear Center Kernel Alignement. These quantitative measurements reinforce our observations from the performance benchmarks and show substantial differences between the learned representations of all types of CNNs despite being optimized with the same approach
Visual Content Based Retrieval in an Image Database with Relevant Feedback
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Semi-supervised GAN with sparse ground truth as Boundary Conditions
No full textInternational audienceOften, physical phenomena are difficult to model by a simple equation and require a lot of processing resources. Studies on Physics-Informed Neural Networks (PINNs) have repeatedly shown the interest of leveraging the information contained in context-relevant physics equations in order to guide the training, as well the ability of this type of networks to reduce the need for labeled data. Some of these analysis have also demonstrated the interest of additional knowledge through Initial and Boundary Conditions (I/BCs) in this type of context. This knowledge can take a variety of forms and shapes, among which is the one of sparse ground truths, and more precisely sparse matrices, as matrices are often well fitted to represent the spatial aspect of this type of problem. The popularity of Computer Vision techniques is partly due to their ability to take into account the spatial aspect of a given problem. The combined use of methods from these two fields therefore seems natural. This paper introduces a method for the use of Boundary Conditions for Generative Adversarial Networks (GANs), and outside the context of PINNs. The interest of leveraging the BCs with a GAN is evaluated in terms of performance, and various BC configuration and quantities are tested to discuss their impact on obtained performance
Semi-supervised GAN with sparse ground truth as Boundary Conditions
No full textInternational audienceOften, physical phenomena are difficult to model by a simple equation and require a lot of processing resources. Studies on Physics-Informed Neural Networks (PINNs) have repeatedly shown the interest of leveraging the information contained in context-relevant physics equations in order to guide the training, as well the ability of this type of networks to reduce the need for labeled data. Some of these analysis have also demonstrated the interest of additional knowledge through Initial and Boundary Conditions (I/BCs) in this type of context. This knowledge can take a variety of forms and shapes, among which is the one of sparse ground truths, and more precisely sparse matrices, as matrices are often well fitted to represent the spatial aspect of this type of problem. The popularity of Computer Vision techniques is partly due to their ability to take into account the spatial aspect of a given problem. The combined use of methods from these two fields therefore seems natural. This paper introduces a method for the use of Boundary Conditions for Generative Adversarial Networks (GANs), and outside the context of PINNs. The interest of leveraging the BCs with a GAN is evaluated in terms of performance, and various BC configuration and quantities are tested to discuss their impact on obtained performance
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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