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When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search

When NAS Meets Trees: An Efficient Algorithm for Neural Arch...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Qian, Guocheng Zhang, Xuanyang Li, Guohao Zhao, Chen Chen, Yukang Zhang, Xiangyu Ghanem, Bernard Sun, Jian King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia MEGVII Technol Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The key challenge in neural architecture search (NAS) is designing how to explore wisely in the huge search space. We propose a new NAS method called TNAS (NAS with trees), which improves search efficiency by exploring only a small number of architectures while also achieving a higher search accuracy. TNAS introduces an architecture tree and a binary operation tree, to factorize the search space and substantially reduce the exploration size. TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture. Impressively, TNAS finds the global optimal architecture on CIFAR-10 with test accuracy of 94.37% in four GPU hours in NAS-Bench-201. The average test accuracy is 94.35%, which outperforms the state-of-the-art. Code is available at: https://***/guochengqian/TNAS.

关键词： computer vision Costs Codes conferences Graphics processing units computer architecture pattern recognition

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Unsupervised Salient Object Detection with Spectral Cluster Voting

Unsupervised Salient Object Detection with Spectral Cluster ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shin, Gyungin Albanie, Samuel Xie, Weidi Univ Oxford Visual Geometry Grp Oxford England Univ Cambridge Dept Engn Cambridge England Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we tackle the challenging task of unsupervised salient object detection (SOD) by leveraging spectral clustering on self-supervised features. We make the following contributions: (i) We revisit spectral clustering and demonstrate its potential to group the pixels of salient objects across various self-supervised features, e.g., MoCov2, SwAV, and DINO;(ii) Given mask proposals from multiple applications of spectral clustering on image features computed from different self-supervised models, we propose a simple but effective winner-takes-all voting mechanism for selecting the salient masks, leveraging object priors based on framing and distinctiveness;(iii) Using the selected object segmentation as pseudo groundtruth masks, we train a salient object detector, termed SELF-MASK, which outperforms prior approaches on three unsupervised SOD benchmarks.

关键词： conferences Object detection Object segmentation Detectors Benchmark testing Feature extraction pattern recognition

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Image Reconstruction from Neuromorphic Event Cameras using Laplacian-Prediction and Poisson Integration with Spiking and Artificial Neural Networks

Image Reconstruction from Neuromorphic Event Cameras using L...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Duwek, Hadar Cohen Shalumov, Albert Tsur, Elishai Ezra Open Univ Israel Neurobiomorph Engn Lab NBEL Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781665448994

Event cameras are robust neuromorphic visual sensors, which communicate transients in luminance as events. Current paradigm for image reconstruction from event data relies on direct optimization of artificial Convolutional Neural Networks (CNNs). Here we proposed a two-phase neural network, which comprises a CNN, optimized for Laplacian prediction followed by a Spiking Neural Network (SNN) optimized for Poisson integration. By introducing Laplacian prediction into the pipeline, we provide image reconstruction with a network comprising only 200 parameters. We converted the CNN to SNN, providing a full neuromorphic implementation. We further optimized the network with Mish activation and a novel convoluted CNN design, proposing a hybrid of spiking and artificial neural network with < 100 parameters. Models were evaluated on both N-MNIST and N-Caltech101 datasets.

关键词： Visualization Laplace equations Neuromorphics Pipelines Cameras Sensors pattern recognition

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Continual Hippocampus Segmentation with Transformers

Continual Hippocampus Segmentation with Transformers

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ranem, Amin Gonzalez, Camila Mukhopadhyay, Anirban Tech Univ Darmstadt GRIS Karolinenpl 5 D-64289 Darmstadt Germany

ISBN: (纸本)9781665487399

In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most existing work focuses on convolutional architectures and image classification. Instead, radiologists prefer to work with segmentation models that outline specific regions-of-interest, for which Transformer-based architectures are gaining traction. The self-attention mechanism of Transformers could potentially mitigate catastrophic forgetting, opening the way for more robust medical image segmentation. In this work, we explore how recently-proposed Transformer mechanisms for semantic segmentation behave in sequential learning scenarios, and analyse how best to adapt continual learning strategies for this setting. Our evaluation on hippocampus segmentation shows that Transformer mechanisms mitigate catastrophic forgetting for medical image segmentation compared to purely convolutional architectures, and demonstrates that regularising ViT modules should be done with caution.

关键词： Image segmentation Semantics Sociology computer architecture Artificial neural networks Transformers Task analysis

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Self-supervised vision Transformers for Land-cover Segmentation and Classification

Self-supervised Vision Transformers for Land-cover Segmentat...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Scheibenreif, Linus Hanna, Joelle Mommert, Michael Borth, Damian Univ St Gallen AIML Lab Sch Comp Sci Rosenbergstr 30 St Gallen Switzerland

ISBN: (纸本)9781665487399

Transformer models have recently approached or even surpassed the performance of ConvNets on computer vision tasks like classification and segmentation. To a large degree, these successes have been enabled by the use of large-scale labelled image datasets for supervised pre-training. This poses a significant challenge for the adaption of vision Transformers to domains where datasets with millions of labelled samples are not available. In this work, we bridge the gap between ConvNets and Transformers for Earth observation by self-supervised pre-training on large-scale unlabelled remote sensing data(1). We show that self-supervised pre-training yields latent task-agnostic representations that can be utilized for both land cover classification and segmentation tasks, where they significantly outperform the fully supervised baselines. Additionally, we find that subsequent fine-tuning of Transformers for specific downstream tasks performs on-par with commonly used ConvNet architectures. An ablation study further illustrates that the labelled dataset size can be reduced to one-tenth after self-supervised pre-training while still maintaining the performance of the fully supervised approach.

关键词： Training Earth computer vision Image segmentation Computational modeling conferences Transformers

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Gated Recurrent Unit-Based RNN for Remote Photoplethysmography Signal Segmentation

Gated Recurrent Unit-Based RNN for Remote Photoplethysmograp...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sabour, Rita Meziati Benezeth, Yannick Univ Bourgogne Franche Comte Dijon France Univ Bourgogne Franche Comte ImViA Lab Dijon France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Remote Photoplethysmography (rPPG) enables quantifying blood volume variations in the skin tissues from an input video recording, using a regular RGB camera. Obtained pulse signals often contain noisy portions due to motion, leading researchers to put aside a great number of rPPG signals in their studies. In this paper, an approach using a Gated Recurrent Unit-based neural network model in order to identify reliable portions in rPPG signals is proposed. This is done by classifying rPPG signal samples into reliable and unreliable samples. For this purpose, rPPG and electrocardiography signals (ECG) were collected from 11 participants, rPPG signal samples were labeled (ECG was used as ground truth), and data were augmented to reach a total number of 11000 1-minute-long rPPG signals. We developed a model composed of a unidimensional CNN and a Bidirectional GRU (1D-CNN+B-GRU) for this study, and obtained an accuracy rate of 85.88%.

关键词： computer network reliability Neural networks Electrocardiography Logic gates Photoplethysmography Skin pattern recognition

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Overhead-based image and video geo-localization framework

Overhead-based image and video geo-localization framework

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Hammoud, Riad I. Kuzdeba, Scott A. Berard, Brian Tom, Victor Ivey, Richard Bostwick, Renu HandUber, Jason Vinciguerra, Lori Shnidman, Nathan Smiley, Byron BAE Syst Burlington MA 01803 USA

ISBN: (纸本)9780769549903

This paper presents a geo-localization framework of street-level outdoor images using multiple sources of overhead reference imagery including LIDAR, Digital Elevation Maps and Multi-Spectral Land Cover/Use imagery. We describe five different matchers and an adaptive linear fusion process which combines individual matchers' probability maps into a single map. These matchers exploit mountain elevation profiles, rendered camera views, landmarks, landuse classes and building heights. We successfully validated our framework on 100 queries with geographic truth in two world regions (each of 10, 000km(2)) in the USA.

关键词： digital elevation models geographic information systems geophysical image processing optical radar probability rendering (computer graphics) terrain mapping video signal processing

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Learned Compression of High Dimensional Image Datasets

Learned Compression of High Dimensional Image Datasets

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cole, Elizabeth Meng, Qingxi Pauly, John Vasanawala, Shreyas Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Radiol Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In many applications, such as burst photography and magnetic resonance imaging (MRI), multiple images are acquired to reduce the noise of the eventual reconstructed image. However, this leads to very high dimensional datasets which have redundant information across the various acquired images. In MRI, multiple images are acquired via multiple RF coil arrays in the scanner. Afterwards, coil compression is performed to convert the original set of coil images into a smaller set of virtual coil images to enable smaller datasets and faster computation time. However, traditional iterative coil compression methods are lossy and time-consuming. In this work, we propose a novel neural network-based coil compression method in pursuit of higher reconstruction accuracy and faster coil compression. Our learned compression method achieves up to 1.5x lower NRMSE and up to 10 times runtime speed compared to traditional methods on a benchmark test dataset.

关键词： Radio frequency Photography Image coding Runtime Magnetic resonance imaging conferences pattern recognition

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Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers

Adapting a Pedestrian Detector by Boosting LDA Exemplar Clas...

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Xu, Jiaolong Vazquez, David Ramos, Sebastian Lopez, Antonio M. Ponsa, Daniel Autonomous Univ Barcelona Comp Vis Ctr E-08193 Barcelona Spain

ISBN: (纸本)9780769549903

Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%.

关键词： computer vision object detection pedestrians statistical analysis

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Semantic Binary Segmentation using Convolutional Networks without Decoders 31

Semantic Binary Segmentation using Convolutional Networks wi...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Aich, Shubhra van der Kamp, William Stavness, Ian Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we propose an efficient architecture for semantic image segmentation using the depth-to-space (D2S) operation. Our D2S model is comprised of a standard CNN encoder followed by a depth-to-space reordering of the final convolutional feature maps. Our approach eliminates the decoder portion of traditional encoder-decoder segmentation models and reduces the amount of computation almost by half. As a participant of the DeepGlobe Road Extraction competition, we evaluate our models on the corresponding road segmentation dataset. Our highly efficient D2S models exhibit comparable performance to standard segmentation models with much lower computational cost.

关键词： Image segmentation Decoding Convolution computer architecture Computational modeling Roads Semantics

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