In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support features of a given class predicts membershi...
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ISBN:
(纸本)9781665445092
In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support features of a given class predicts membership of the query in that class. We introduce a novel mechanism for few-shot classification by regressing directly from support features to query features in closed form, without introducing any new modules or large-scale learnable parameters. The resulting Feature Map Reconstruction Networks are both more performant and computationally efficient than previous approaches. We demonstrate consistent and substantial accuracy gains on four fine-grained benchmarks with varying neural architectures. Our model is also competitive on the non-fine-grained mini-ImageNet and tiered-ImageNet benchmarks with minimal bells and whistles.
Blind people are incapable of seeing, which is crucial for daily life. Blind people have limited autonomy due to their lack of eyesight. There are many methods for helping blind people navigate that are based on RFID,...
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computervision plays a crucial role in detecting objects. Has various applications, including traffic management and autonomous vehicles. This study aims to evaluate the performance of different object identification...
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Image deblurring and super-resolution (SR) are computervision tasks aiming to restore image detail and spatial scale, respectively. Besides, only a few recent works of literature contribute to this task, as conventio...
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ISBN:
(纸本)9781665448994
Image deblurring and super-resolution (SR) are computervision tasks aiming to restore image detail and spatial scale, respectively. Besides, only a few recent works of literature contribute to this task, as conventional methods deal with SR or deblurring separately. We focus on designing a novel Pixel-Guided dual-branch attention network (PDAN) that handles both tasks jointly to address this issue. Then, we propose a novel loss function better focus on large and medium range errors. Extensive experiments demonstrated that the proposed PDAN with the novel loss function not only generates remarkably clear HR images and achieves compelling results for joint image deblurring and SR tasks. In addition, our method achieves second place in NTIRE 2021 Challenge on track 1 of the Image Deblurring Challenge.
We propose a novel method to reconstruct volumetric flows from sparse views via a global transport formulation. Instead of obtaining the space-time function of the observations, we reconstruct its motion based on a si...
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ISBN:
(纸本)9781665445092
We propose a novel method to reconstruct volumetric flows from sparse views via a global transport formulation. Instead of obtaining the space-time function of the observations, we reconstruct its motion based on a single initial state. In addition we introduce a learned self-supervision that constrains observations from unseen angles. These visual constraints are coupled via the transport constraints and a differentiable rendering step to arrive at a robust end-to-end reconstruction algorithm. This makes the reconstruction of highly realistic flow motions possible, even from only a single input view We show with a variety of synthetic and real flows that the proposed global reconstruction of the transport process yields an improved reconstruction of the fluid motion.
The paper presents the research of computational complexity optimization of a virtual detector method for video-based vehicle recognition. This method enables a more flexible and adaptive analytical research and optim...
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The expansion of smart cities requires the integration of advanced security technologies, including gait recognition, which is increasingly valued for enhancing urban surveillance. This paper introduces a novel distri...
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ISBN:
(纸本)9798350355291;9798350355284
The expansion of smart cities requires the integration of advanced security technologies, including gait recognition, which is increasingly valued for enhancing urban surveillance. This paper introduces a novel distributed edge computing framework specifically designed for appearance-based real-time gait recognition, utilizing deep learning techniques to ensure efficient and accurate analysis. The framework leverages a distributed architecture that minimizes latency and distributes computational loads across multiple devices, effectively balancing processing demands. We also introduce the OAKGait16 dataset, which comprises video sequences of various individuals from multiple view angles and in different clothing variants, providing a robust basis for comprehensive analysis. Our evaluations demonstrate that our approach not only achieves high efficiency and accuracy in controlled settings-but also keeps the processing speed at 29 frames per second, showcasing its potential for real-world application in smart city security systems. This work establishes a foundational framework intended to guide further advancements in practical, scalable gait recognition systems.
Deep learning and patternrecognition in smart farming has seen rapid growth as a building bridge between crop science and computervision. One of the important application is anomaly segmentation in agriculture like ...
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ISBN:
(纸本)9781665448994
Deep learning and patternrecognition in smart farming has seen rapid growth as a building bridge between crop science and computervision. One of the important application is anomaly segmentation in agriculture like weed, standing water, cloud shadow, etc. Our research work focuses on aerial farmland image dataset known as Agriculture vision. We propose to have data fusion of R, G, B, and NIR modalities that enhances the feature extraction and also propose Efficient Fused Pyramid Network (Fuse-PN) for anomaly pattern segmentation. The proposed encoder module is a bottom-up pathway having a compound scaled network and decoder module is a top-down pyramid network enhancing features at different scales having rich semantic features with lateral connections of low-level features. This proposed approach achieved a mean dice similarity score of 0.8271 for six agricultural anomaly patterns of Agriculture vision dataset and outperforms various approaches in literature.
In the present research work, a system is developed that can detect objects in real-time using a combination of the ESP32 CAM module and Python programming. The goal was to show how affordable hardware and free softwa...
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ISBN:
(纸本)9798331540661;9798331540678
In the present research work, a system is developed that can detect objects in real-time using a combination of the ESP32 CAM module and Python programming. The goal was to show how affordable hardware and free software can be used to make a system that recognizes objects quickly and accurately. By using computervision and machine learning tricks, the proposed system can figure out the different objects with great precision. The setup process involves putting some code onto the ESP32 CAM module, finding its IP address, and then making it work smoothly with Python. The proposed system was tested in different situations, like watching for things in surveillance, making tasks easier with automation, and helping out in assistive technologies.
This paper presents the development of a portable and interactive musical instrument using edge devices and various sensors. The goal is to create a versatile device that allows users to play different notes and melod...
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