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Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation

Nerfels: Renderable Neural Codes for Improved Camera Pose Es...

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

作者： Avraham, Gil Straub, Julian Shen, Tianwei Yang, Tsun-Yi Germain, Hugo Sweeney, Chris Balntas, Vasileios Novotny, David DeTone, Daniel Newcombe, Richard Monash Univ Clayton Vic Australia Ecole Ponts Champs Sur Marne France Facebook Real Labs Menlo Pk CA USA Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism. Our proposed 3D scene representation, Nerfels, is locally dense yet globally sparse. As opposed to existing invertible neural rendering systems which overfit a model to the entire scene, we adopt a feature-driven approach for representing scene-agnostic, local 3D patches with renderable codes. By modelling a scene only where local features are detected, our framework effectively generalizes to unseen local regions in the scene via an optimizable code conditioning mechanism in the neural renderer, all while maintaining the low memory footprint of a sparse 3D map representation. Our model can be incorporated to existing state-of-the-art hand-crafted and learned local feature pose estimators, yielding improved performance when evaluating on ScanNet for wide camera baseline scenarios.

关键词： Solid modeling Three-dimensional displays Codes conferences Pose estimation Rendering (computer graphics) Cameras

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Cross-dataset Learning for Generalizable Land Use Scene Classification

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

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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...

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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...

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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...

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

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

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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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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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Perfusion assessment via local remote photoplethysmography (rPPG)

Perfusion assessment via local remote photoplethysmography (...

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

作者： Kossack, Benjamin Wisotzky, Eric Eisert, Peter Schraven, Sebastian P. Globke, Brigitta Hilsmann, Anna Fraunhofer Heinrich Hertz Inst Berlin Germany Humboldt Univ Berlin Germany Rostock Univ Dept Otorhinolaryngol Head & Neck Surg Otto Korne Med Ctr Rostock Germany Charite Univ Med Berlin Dept Surg Berlin Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents an approach to assess the perfusion of visible human tissue from RGB video files. We propose metrics derived from remote photoplethysmography (rPPG) signals to detect whether a tissue is adequately supplied with blood. The perfusion analysis is done in three different scales, offering a flexible approach for different applications. We perform a plane-orthogonal-to-skin rPPG independently for locally defined regions of interest on each scale. From the extracted signals, we derive the signal-to-noise ratio, magnitude in the frequency domain, heart rate, perfusion index as well as correlation between specific rPPG signals in order to locally assess the perfusion of a specific region of human tissue. We show that locally resolved rPPG has a broad range of applications. As exemplary applications, we present results in intraoperative perfusion analysis and visualization during skin and organ transplantation as well as an application for liveliness assessment for the detection of presentation attacks to authentication systems.

关键词： Visualization Surgery Authentication Photoplethysmography Skin pattern recognition Monitoring

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PromptKD: Unsupervised Prompt Distillation for vision-Language Models

PromptKD: Unsupervised Prompt Distillation for Vision-Langua...

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

作者： Li, Zheng Li, Xiang Fu, Xinyi Zhang, Xin Wang, Weiqiang Chen, Shuo Yang, Jian Nankai Univ Coll Comp Sci PCA Lab VCIP Tianjin Peoples R China NKIARI Shenzhen Futian Peoples R China Ant Grp Tiansuan Lab Hangzhou Peoples R China RIKEN Wako Saitama Japan

ISBN: (纸本)9798350353006

Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses on designing various learning forms of prompts, neglecting the potential of prompts as effective distillers for learning from larger teacher models. In this paper, we introduce an unsupervised domain prompt distillation framework, which aims to transfer the knowledge of a larger teacher model to a lightweight target model through prompt-driven imitation using unlabeled domain images. Specifically, our framework consists of two distinct stages. In the initial stage, we pre-train a large CLIP teacher model using domain (few-shot) labels. After pre-training, we leverage the unique decoupled-modality characteristics of CLIP by pre-computing and storing the text features as class vectors only once through the teacher text encoder. In the subsequent stage, the stored class vectors are shared across teacher and student image encoders for calculating the predicted logits. Further, we align the logits of both the teacher and student models via KL divergence, encouraging the student image encoder to generate similar probability distributions to the teacher through the learnable prompts. The proposed prompt distillation process eliminates the reliance on labeled data, enabling the algorithm to leverage a vast amount of unlabeled images within the domain. Finally, the well-trained student image encoders and pre-stored text features (class vectors) are utilized for inference. To our best knowledge, we are the first to (1) perform unsupervised domain-specific prompt-driven knowledge distillation for CLIP, and (2) establish a practical pre-storing mechanism of text features as shared class vectors between teacher and student. Extensive experiments on 11 datasets demonstrate the effectiveness of our method. Code is publicly available at https://***/zhengli97/PromptKD.

关键词： knowledge distillation prompt learning vision-language models zero-shot learning

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