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共 8406 条记录，以下是441-450 订阅

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Combining Weight Pruning and Knowledge Distillation For CNN Compression

Combining Weight Pruning and Knowledge Distillation For CNN ...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Aghli, Nima Ribeiro, Eraldo Florida Inst Technol 150 W Univ Blvd Melbourne FL 32901 USA

ISBN: (纸本)9781665448994

Complex deep convolutional neural networks such as ResNet require expensive hardware such as powerful GPUs to achieve real-time performance. This problem is critical for applications that run on low-end embedded GPU or CPU systems with limited resources. As a result, model compression for deep neural networks becomes an important research topic. Popular compression methods such as weight pruning remove redundant neurons from the CNN without affecting the network's output accuracy. While these pruning methods work well on simple networks such as VGG or AlexNet, they are not suitable for compressing current state-of-the-art networks such as ResNets because of these networks' complex architectures with dimensionality dependencies. This dependency results in filter pruning breaking the structure of ResNets leading to an untrainable network. In this paper, we first use the weight pruning method only on a selective number of layers in the ResNet architecture to avoid breaking the network structure. Second, we introduce a knowledge distillation architecture and a loss function to compress the untouched layers during the pruning. We test our method on both image-based regression and classification networks for head-pose estimation and image classification. Our compression method reduces the models' size significantly while maintaining the accuracy very close to the baseline model.

关键词： image coding Neurons Estimation Graphics processing.units computer architecture Real-time systems Convolutional neural networks

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Design of image recognition Monitoring System of Hydrological Monitoring Station Based on Edge Intelligence

Design of Image Recognition Monitoring System of Hydrologica...

引用

2022 International conference on Automation, Robotics and computer Engineering, ICARCE 2022

作者： Tan, Liwu Yao, Xianzhe Long, Yuan Zhang, Zhizheng Li, Zhiwei Li, Yan Nanchang Institute of Technology School of Electrical Engineering Nanchang China Nanchang Institute of Technology School of Information Engineering Nanchang China

ISBN: (纸本)9781665475488

In the safety monitoring of hydrological monitoring stations, the target detection algorithm can be used to avoid unnecessary trouble caused by artificial supervision through intelligent monitoring. However, the accuracy of target recognition and time - delay is always a contradiction in security monitoring. SSD target detection algorithm is the latest target recognition algorithm after Faster RCNN and YOLOv1 algorithm, combining the advantages of both. The algorithm is faster than the fast RCNN algorithm and has higher accuracy than the YOLOv1 algorithm. In this paper, a PSO intelligent algorithm based on hyperlight fast generic face detection and 1mb network convergence is proposed, called PSO-1MB. The device is deployed in or near the hydrologic monitoring station on the edge node server for calculation and processing. In this paper, the test whether the staff wear a hard hat as an example, using the Pytorch environmental framework, experiment simulation. Experimental results show that this model and algorithm can detect helmet features more accurately and quickly, and can better meet the engineering requirements. © 2022 ieee.

关键词： Face recognition

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Low-Power pattern recognition System Using Spintronics Compute-in-memory Architecture

Low-Power Pattern Recognition System Using Spintronics Compu...

引用

ieee Nanotechnology Materials and Devices conference (NMDC)

作者： Sonal Shreya Yaseer Rezaeiyan Maryam Sadeghi Brajesh Kaushik Farshad Moradi Electrical and Computer Engineering Department Aarhus University Aarhus Denmark Electronics and Communication Engineering Department Indian Institute of Technology Roorkee India

pattern recognition plays an important role in image recognition, classification, and information processing. In this work, energy energy-efficient spintronics-based pattern recognition design using in-memory computing architecture is presented. The paper presents a faster, energy-efficient, and area-efficient pattern recognition approach with three computational steps per pixel recognition process using the training and mean image size. The reduction in critical switching current owing to the voltage-controlled switching mechanism results in a 43% reduction in power consumption as compared to all-spin logic-based design approach for recognizing a $3\times 3$ pixel image size pattern. This circuitry saves 33% area overhead when compared to conventional compute-in-memory (CiM) architectures based on spin orbit torque. The proposed CiM architectures can further be used for real-time pattern recognition for several applications.

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Vehicle Re-Identification based on Ensembling Deep Learning Features including a Synthetic Training Dataset, Orientation and Background Features, and Camera Verification.

Vehicle Re-Identification based on Ensembling Deep Learning ...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Fernandez, Marta Moral, Paula Garcia-Martin, Alvaro Martinez, Jose M. Univ Autonoma Madrid Video Proc & Understanding Lab Madrid Spain

ISBN: (纸本)9781665448994

Vehicle re-identification has the objective of finding a specific vehicle among different vehicle crops captured by multiple cameras placed at multiple intersections. Among the different difficulties, high intra-class variability and high inter-class similarity can be highlighted. Moreover, the resolution of the images can be different, which also means a challenge in the re-identification task. Intending to face these problems, we use as baseline our previous work based on obtaining different deep learning features and ensembling them to get a single, stable and robust feature vector. It also includes post-processing.techniques that explode all the information provided by the CityFlowV2-ReID dataset, including a re-ranking step. Then, in this paper, several newly included improvements are described. Background and orientation similarity matrices are added to the system to reduce bias towards these characteristics. Furthermore, we take into account the camera labels to penalize the gallery images that share camera with the query image. Additionally, to improve the training step, a synthetic dataset is added to the original one.

关键词： Training Deep learning image resolution image color analysis Cameras Feature extraction pattern recognition

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Collaborative Visual Place recognition through Federated Learning

Collaborative Visual Place Recognition through Federated Lea...

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Mattia Dutto Gabriele Berton Debora Caldarola Eros Fanì Gabriele Trivigno Carlo Masone Politecnico di Torino

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Visual Place recognition (VPR) aims to estimate the location of an image by treating it as a retrieval problem. VPR uses a database of geo-tagged images and leverages deep neural networks to extract a global representation, called descriptor, from each image. While the training data for VPR models often originates from diverse, geographically scattered sources (geo-tagged images), the training process itself is typically assumed to be centralized. This research revisits the task of VPR through the lens of Federated Learning (FL), addressing several key challenges associated with this adaptation. VPR data inherently lacks well-defined classes, and models are typically trained using contrastive learning, which necessitates a data mining step on a centralized database. Additionally, client devices in federated systems can be highly heterogeneous in terms of their processing.capabilities. The proposed FedVPR framework not only presents a novel approach for VPR but also introduces a new, challenging, and realistic task for FL research, paving the way to other image retrieval tasks in FL.

关键词： Performance evaluation Training Adaptation models image resolution Federated learning Databases image retrieval

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Encoder Fusion Network with Co-Attention Embedding for Referring image Segmentation

Encoder Fusion Network with Co-Attention Embedding for Refer...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Feng, Guang Hu, Zhiwei Zhang, Lihe Lu, Huchuan Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Dalian Univ Technol Ningbo Inst Dalian Peoples R China

ISBN: (纸本)9781665445092

Recently, referring image segmentation has aroused widespread interest. Previous methods perform the multi-modal fusion between language and vision at the decoding side of the network. And, linguistic feature interacts with visual feature of each scale separately, which ignores the continuous guidance of language to multi-scale visual features. In this work, we propose an encoder fusion network (EFN), which transforms the visual encoder into a multi-modal feature learning network, and uses language to refine the multi-modal features progressively. Moreover, a co-attention mechanism is embedded in the EFN to realize the parallel update of multi-modal features, which can promote the consistent of the cross-modal information representation in the semantic space. Finally, we propose a boundary enhancement module (BEM) to make the network pay more attention to the fine structure. The experiment results on four benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance under different evaluation metrics without any post-processing.

关键词： Measurement Visualization image segmentation Grounding Semantics Transforms Information representation

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Multi-Scale Transformer with Explicit Boundary Constraint for Semantic Segmentation

Multi-Scale Transformer with Explicit Boundary Constraint fo...

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2022 ieee International conference on Multimedia and Expo, ICME 2022

作者： Mi, Baotong Liang, Xun School of Information Department of Computer Science Renmin University of China China Beijing Jushi Intelligent Technology Co. Ltd. China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Semantic segmentation is a dense pixel-level prediction task, which requires both strong classification and locality ability simultaneously. Previous methods tackle this challenging problem by enlarging receptive field, incorporating contextual cues, fusing multi-level features, etc. Recently, several pure Transformer methods push this forward by its intrinsic characteristics of global relation modeling between any pixels. The receptive field is enlarged to the whole image as while. However, current pure Transformer methods still has shortcomings, such as poor ability for pixel-level locality because of single-scale image patch splitting, the simple upsampling of bilinear interpolation and discrete positional encoding. To this end, we propose a pure multi-scale Transformer architecture with pixel-shuffle upsampling kernel and semantically continuous positional encoding. In addition, current Transformers naturally lack the ability to encode object shape due to the one-dimensional processing. Hence we propose to explicitly enforce the boundary constraints on the decoder. Experimental results demonstrate our method achieves state-of-art results on both Cityscapes and ADE20K datasets. © 2022 ieee.

关键词： Pixels

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Fast and Accurate image Stitching for High-Resolution images

Fast and Accurate Image Stitching for High-Resolution Images

引用

pattern recognition and Artificial Intelligence (PRAI), International conference on

作者： Haiyan Jiang Mingwei Cao School of Computer Science and Technology Anhui University Hefei China

The purpose of image stitching is to generate a panoramic image with a large viewing angle from a series of images. Panoramic images can be applied to many practical scenes, such as military, simulation, augmented reality, and virtual reality. At present, the accuracy and efficiency of existing image stitching methods need to be improved for generating high-quality panoramic images from high-resolution images. To solve the above problems, in this paper, we propose a novel image stitching method for processing.high-resolution images, to quickly generate high-quality panoramic images. The core of the proposed method includes real-time image matching, image space optimization, and panoramic image post-processing. Based on the proposed method, a high-precision, and fast image stitching system, namely, MetaPano, is developed. Experimental results show that the MetaPano system can quickly generate high-quality panoramic images on some challenging image data sets.

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3D Remote Scene Reconstruction via Graph Convolution 10

3D Remote Scene Reconstruction via Graph Convolution

引用

10th International conference on Virtual Reality, ICVR 2024

作者： Li, Xing Sun, Mingyu Ou, Qiaofeng Mo, Yan Liu, Sikun Rao, Zhibo Nanchang Hangkong University School of Information Engineering Nanchang China University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350364231

3D urban scene reconstruction is a significant remote sensing topic that provides enriched context on 3D spatial information. However, obtaining a watertight, lightweight, and detailed mesh object from remote sensing images is challenging due to sparse point clouds and inaccurate normals. This paper introduces a 3D mesh reconstruction framework to recover 3D scene information from pairwise remote sensing images, comprising three main stages. Firstly, our previous multi-task network (BGA-Net) is employed to predict the disparity maps from pairwise remote sensing images. Next, we utilize camera parameters and interpolation to reconstruct and fill point clouds, mitigating sparse point clouds (e.g., building edges or unsmooth areas). Finally, a graph convolution network (GCN) is adopted to recover the mesh object from the filled point clouds, reducing the dependency on precise normals. Extensive experiments demonstrate that our framework can qualitatively produce watertight, lightweight, and detailed mesh models. © 2024 ieee.

关键词： Mesh generation

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A Joint Spatial and Magnification Based Attention Framework for Large Scale Histopathology Classification

A Joint Spatial and Magnification Based Attention Framework ...

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Zhang, Jingwei Ma, Ke Van Arnam, John Gupta, Rajarsi Saltz, Joel Vakalopoulou, Maria Samaras, Dimitris SUNY Stony Brook Stony Brook NY 11794 USA Univ Paris Saclay Cent Supelec Paris France

ISBN: (纸本)9781665448994

Deep learning has achieved great success in processing.large size medical images such as histopathology slides. However, conventional deep learning methods cannot handle the enormous image sizes;instead, they split the image into patches which are exhaustively processed, usually through multi-instance learning approaches. Moreover and especially in histopathology, determining the most appropriate magnification to generate these patches is also exhaustive: a model needs to traverse all the possible magnifications to select the optimal one. These limitations make the application of deep learning on large medical images and in particular histopathological images markedly inefficient. To tackle these problems, we propose a novel spatial and magnification based attention sampling strategy. First, we use a down-sampled large size image to estimate an attention map that represents a spatial probability distribution of informative patches at different magnifications. Then a small number of patches are cropped from the large size medical image at certain magnifications based on the obtained attention. The final label of the large size image is predicted solely by these patches using an end-to-end training strategy. Our experiments on two different histopathology datasets, the publicly available BACH and a subset of the TCGA-PRAD dataset, demonstrate that the proposed method runs 2.5 times faster with automatic magnification selection in training and at least 1.6 times faster than using all patches in inference as the most of state-of-the-art methods do, without loosing in performance.

关键词： Training Deep learning Histopathology Microscopy Tools Probability distribution pattern recognition

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