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Cross-dataset Learning for Generalizable Land Use Scene Classification

Cross-dataset Learning for Generalizable Land Use Scene Clas...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gominski, Dimitri Gouet-Brunet, Valerie Chen, Liming Univ Copenhagen Geog Copenhagen Denmark IGN LaSTIG St Mande France Ecole Cent Lyon LIRIS Ecully France

ISBN: (纸本)9781665487399

Few-shot and cross-domain land use scene classification methods propose solutions to classify unseen classes or unseen visual distributions, but are hardly applicable to realworld situations due to restrictive assumptions. Few-shot methods involve episodic training on restrictive training subsets with small feature extractors, while cross-domain methods are only applied to common classes. The underlying challenge remains open: can we accurately classify new scenes on new datasets? In this paper, we propose a new framework for few-shot, cross-domain classification. Our retrieval-inspired approach(1) exploits the interrelations in both the training and testing data to output class labels using compact descriptors. Results show that our method can accurately produce land-use predictions on unseen datasets and unseen classes, going beyond the traditional few-shot or cross-domain formulation, and allowing cross-dataset training.

关键词： Training Visualization computer vision Image analysis conferences Image retrieval Feature extraction

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Faster, Lighter, Robuster: A Weakly-Supervised Crowd Analysis Enhancement Network and A Generic Feature Extraction Framework

Faster, Lighter, Robuster: A Weakly-Supervised Crowd Analysi...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wu, Shaokai Liu, Zhaogeng Pei, Wencheng Hong, Jianbo Li, Zhanshan Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Jilin Univ Sch Artificial Intelligence Changchun Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With bounding box labels needed for training, object detection is viewed unfavorably in terms of crowd analysis, due to the intensive labor for labeling and the unsatisfactory performance in clutters and severe occlusions. Another feasible method, density-based regression, despite its proficiency in counting and only point-level labels used for training, cannot get the location of each person, and the time and space consumption is relatively high. In this paper, we propose a generic feature extraction framework, Adaptive Pyramid Score (APS), based on object detection and designed specifically for extracting quantitative and spatial-semantic features. Moreover, as an intuitive and feasible solution regarding crowd analysis, we propose the weakly-supervised Confidence-Threshold-Foresight Network (CTFNet) under our APS feature extraction framework, which only needs count-level labels for training and improves the performance of various methods dramatically. Our system realizes the triple enhancement of counting, localization, and detection, which is also proved to be faster than advanced crowd analysis methods, lighter to be transplanted to various object detection methods, and robuster to tackle tasks of extreme scenes. Furthermore, the weakly-supervised paradigm leverage the intensive labor for labeling profoundly.

关键词： Training Location awareness computer vision conferences Object detection Feature extraction pattern recognition

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Remote Estimation of Continuous Blood Pressure by a Convolutional Neural Network Trained on Spatial patterns of Facial Pulse Waves

Remote Estimation of Continuous Blood Pressure by a Convolut...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Iuchi, Kaito Miyazaki, Ryogo Cardoso, George C. Ogawa-Ochiai, Keiko Tsumura, Norimichi Chiba Univ Grad Sch Sci & Engn Dept Imaging Sci Chiba Japan Univ Sao Paulo Phys Dept FFCLRP Sao Paulo Brazil Hiroshima Univ Hosp Dept Gen Med Hiroshima Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a remote method to estimate continuous blood pressure based on spatial information of a pulse wave at a single point in time. By setting regions of interest to cover a face in a mutually exclusive and collectively exhaustive manner, RGB facial video is converted into a spatial pulse wave signal. The spatial pulse wave signal is converted into spatial signals of contours of each segmented pulse beat and relationships of each segmented pulse beat. The spatial signal is represented as a time-continuous value based on a representation of a pulse contour in a time axis and a phase axis and an interpolation along with the time axis. A relationship between the spatial signals and blood pressure is modeled by a convolutional neural network. A dataset was built to demonstrate the effectiveness of the proposed method. The dataset consists of continuous blood pressure and facial RGB videos of ten healthy volunteers. A comparison of conventional methods with the proposed method shows superior error for the latter. The results show an adequate estimation of the performance of the proposed method, when compared to the ground truth in mean blood pressure, in both the correlation coefficient (0.85) and mean absolute error (5.4 mmHg).

关键词： Correlation coefficient Interpolation computer vision Face recognition conferences Estimation Blood pressure

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A Categorized Reflection Removal Dataset with Diverse Real-world Scenes

A Categorized Reflection Removal Dataset with Diverse Real-w...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Lei, Chenyang Huang, Xuhua Qi, Chenyang Zhao, Yankun Sun, Wenxiu Yan, Qiong Chen, Qifeng HKUST Hong Kong Peoples R China CMU Pittsburgh PA USA SenseTime Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amount of real-world data, which makes it difficult to evaluate the strengths or weaknesses of different reflection removal methods thoroughly. Furthermore, existing real-world benchmarks and datasets do not categorize image data based on the types and appearances of reflection (e.g., smoothness, intensity), making it hard to analyze reflection removal methods. Hence, we construct a new reflection removal dataset that is categorized, diverse, and real-world (CDR). A pipeline based on RAW data is used to capture perfectly aligned input images and transmission images. The dataset is constructed using diverse glass types under various environments to ensure diversity. By analyzing several reflection removal methods and conducting extensive experiments on our dataset, we show that state-of-the-art reflection removal methods generally perform well on blurry reflection but fail in obtaining satisfying performance on other types of real-world reflection. We believe our dataset can help develop novel methods to remove real-world reflection better.

关键词： computer vision conferences Pipelines Glass Benchmark testing Reflection pattern recognition

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SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation

SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Gene...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Zhixuan Schaldenbrand, Peter Okogwu, Beverley-Claire Peng, Wenxuan Yun, Youngsik Hundt, Andrew Kim, Jihie Oh, Jean Carnegie Mellon Univ Pittsburgh PA 15213 USA Nanyang Technol Univ Singapore Singapore Dongguk Univ Seoul South Korea

ISBN: (纸本)9798350353006

Accurate representation in media is known to improve the well-being of the people who consume it. Generative image models trained on large web-crawled datasets such as LAION are known to produce images with harmful stereotypes and misrepresentations of cultures. We improve inclusive representation in generated images by (1) engaging with communities to collect a culturally representative dataset that we call the Cross-Cultural Under-standing Benchmark (CCUB) and (2) proposing a novel Self- Contrastive Fine-Tuning (SCoFT, pronounced /soft/) method that leverages the model's known biases to self-improve. SCoFT is designed to prevent overfitting on small datasets, encode only high-level information from the data, and shift the generated distribution away from misrepresentations encoded in a pretrained model. Our user study conducted on 51 participants from 5 different countries based on their self-selected national cultural affiliation shows that fine-tuning on CCUB consistently generates images with higher cultural relevance and fewer stereotypes when compared to the Stable Diffusion baseline, which is further improved with our SCoFT technique. Resources and code are at https://***/SCoFT.

关键词： computer vision for Social Good Image Synthesis

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Exploring Robustness Connection between Artificial and Natural Adversarial Examples

Exploring Robustness Connection between Artificial and Natur...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Agarwal, Akshay Ratha, Nalini Vatsa, Mayank Singh, Richa Buffalo Buffalo NY 14260 USA IIT Jodhpur Jodhpur Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Although recent deep neural network algorithm has shown tremendous success in several computer vision tasks, their vulnerability against minute adversarial perturbations has raised a serious concern. In the early days of crafting these adversarial examples, artificial noises are optimized through the network and added in the images to decrease the confidence of the classifiers against the true class. However, recent efforts are showcasing the presence of natural adversarial examples which can also be effectively used to fool the deep neural networks with high confidence. In this paper, for the first time, we have raised the question that whether there is any robustness connection between artificial and natural adversarial examples. The possible robustness connection between natural and artificial adversarial examples is studied in the form that whether an adversarial example detector trained on artificial examples can detect the natural adversarial examples. We have analyzed several deep neural networks for the possible detection of artificial and natural adversarial examples in seen and unseen settings to set up a robust connection. The extensive experimental results reveal several interesting insights to defend the deep classifiers whether vulnerable against natural or artificially perturbed examples. We believe these findings can pave a way for the development of unified resiliency because defense against one attack is not sufficient for real-world use cases.

关键词： Deep learning computer vision Neural networks Semantics Transformers Robustness Convolutional neural networks

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Semantic Segmentation for Thermal Images: A Comparative Survey

Semantic Segmentation for Thermal Images: A Comparative Surv...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kutuk, Zulfiye Algan, Gorkem Aselsan Inc Dept Image Proc & Comp Vis Technol Ankara Turkey

ISBN: (纸本)9781665487399

Semantic segmentation is a challenging task since it requires excessively more low-level spatial information of the image compared to other computer vision problems. The accuracy of pixel-level classification can be affected by many factors, such as imaging limitations and the ambiguity of object boundaries in an image. Conventional methods exploit three-channel RGB images captured in the visible spectrum with deep neural networks (DNN). Thermal images can significantly contribute during the segmentation since thermal imaging cameras are capable of capturing details despite the weather and illumination conditions. Using infrared spectrum in semantic segmentation has many real-world use cases, such as autonomous driving, medical imaging, agriculture, defense industry, etc. Due to this wide range of use cases, designing accurate semantic segmentation algorithms with the help of infrared spectrum is an important challenge. One approach is to use both visible and infrared spectrum images as inputs. These methods can accomplish higher accuracy due to enriched input information, with the cost of extra effort for the alignment and processing of multiple inputs. Another approach is to use only thermal images, enabling less hardware cost for smaller use cases. Even though there are multiple surveys on semantic segmentation methods, the literature lacks a comprehensive survey centered explicitly around semantic segmentation using infrared spectrum. This work aims to fill this gap by presenting algorithms in the literature and categorizing them by their input images.

关键词： Image segmentation computer vision Costs Semantics Neural networks Lighting Robustness

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An Effective Framework of Multi-Class Product Counting and recognition for Automated Retail Checkout

An Effective Framework of Multi-Class Product Counting and R...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wan, Junfeng Qian, Shuhao Tian, Zihan Zhao, Yanyun Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

As the field of computer vision grows, Automated Retail Checkout has become a highly anticipated development goal. The key of this task is to improve the accuracy rate. If there is an error, it will bring serious losses to the business and awful experience for customers which is not our expected. This competition gives us an opportunity to simulate check-out in a real world scenario, so that we can identify problems and solve them, not only for the competition, but also for the practical application. As one of the participating teams in this task, we pursue the goal of avoiding misdetection and misclassification, and build a complete set of framework to achieve high-precise, high-recall performance. In addition, there is an excessive difference between the training data and test data. How to use limited data to make up for the differences in this part is also one of the highlights of our framework. In general, our framework consists of three main parts. Firstly, the Pre-Processing module to make up for the differences between training and test data. The DTC module completes the overall process of automatic recognition. Finally the MTCR module is proposed to post-process the output of the DTC module. On the TestA data of AICITY2022 Task 4, we have achieved significant result compared to the other teams. Finally, our model is ranked 1st in AICITY2022 Task 4 [17].

关键词： Training computer vision Codes conferences Training data Trajectory pattern recognition

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CityDreamer: Compositional Generative Model of Unbounded 3D Cities

CityDreamer: Compositional Generative Model of Unbounded 3D ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xie, Haozhe Chen, Zhaoxi Hong, Fangzhou Liu, Ziwei Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350353006

3D city generation is a desirable yet challenging task, since humans are more sensitive to structural distortions in urban environments. Additionally, generating 3D cities is more complex than 3D natural scenes since buildings, as objects of the same class, exhibit a wider range of appearances compared to the relatively consistent appearance of objects like trees in natural scenes. To address these challenges, we propose CityDreamer, a compositional generative model designed specifically for unbounded 3D cities. Our key insight is that 3D city generation should be a composition of different types of neural fields: 1) various building instances, and 2) background stuff, such as roads and green lands. Specifically, we adopt the bird's eye view scene representation and employ a volumetric render for both instance-oriented and stuff-oriented neural fields. The generative hash grid and periodic positional embedding are tailored as scene parameterization to suit the distinct characteristics of building instances and background stuff. Furthermore, we contribute a suite of CityGen Datasets, including OSM and GoogleEarth, which comprises a vast amount of real-world city imagery to enhance the realism of the generated 3D cities both in their layouts and appearances. CityDreamer achieves state-of-the-art performance not only in generating realistic 3D cities but also in localized editing within the generated cities.

关键词： 3D vision AIGC City Generation Scene Generation

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Few-Shot Image Classification Along Sparse Graphs

Few-Shot Image Classification Along Sparse Graphs

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Comer, Joseph F. Jacobson, Philip L. Hoffmann, Heiko HRL Labs LLC 3011 Malibu Canyon Rd Malibu CA 90265 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Few-shot learning remains a challenging problem, with unsatisfactory 1-shot accuracies for most real-world data. Here, we present a new perspective for data distributions in the feature space of a deep network and show how to exploit this perspective for few-shot learning. First, we observe that nearest neighbors in the feature space are with high probability members of the same class while generally two random points from one class are not much closer to each other than two points between classes. This observation suggests that classes in feature space form sparse, loosely connected graphs instead of dense clusters. To exploit this property, we propose using label propagation to the nearest unlabeled data and then using a kernel PCA reconstruction error as decision boundary in feature-space for the data distribution of each class. Using this method, which we call "K-Prop," we demonstrate largely improved few-shot learning performances (e.g., 83% accuracy for 1-shot 5-way classification on the RESISC45 satellite-images dataset) for datasets for which a backbone network can be trained to produce high within-class nearest-neighbor probabilities. We demonstrate this relationship using six different datasets.

关键词： Target tracking Limiting Shape Training data Streaming media pattern recognition Reliability

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