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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 ieee/CVF conference on computer vision and pattern recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 ieee.

关键词： Autonomous driving

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DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM

DTLLM-VLT: Diverse Text Generation for Visual Language Track...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xuchen Li Xiaokun Feng Shiyu Hu Meiqi Wu Dailing Zhang Jing Zhang Kaiqi Huang CRISE Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Visual Language Tracking (VLT) enhances single object tracking (SOT) by integrating natural language descriptions from a video, for the precise tracking of a specified object. By leveraging high-level semantic information, VLT guides object tracking, alleviating the constraints associated with relying on a visual modality. Nevertheless, most VLT benchmarks are annotated in a single granularity and lack a coherent semantic framework to provide scientific guidance. Moreover, coordinating human annotators for high-quality annotations is laborious and time-consuming. To address these challenges, we introduce DTLLM-VLT, which automatically generates extensive and multi-granularity text to enhance environmental diversity. (1) DTLLM-VLT generates scientific and multi-granularity text descriptions using a cohesive prompt framework. Its succinct and highly adaptable design allows seamless integration into various visual tracking benchmarks. (2) We select three prominent benchmarks to deploy our approach: short-term tracking, long-term tracking, and global instance tracking. We offer four granularity combinations for these benchmarks, considering the extent and density of semantic information, thereby showcasing the practicality and versatility of DTLLM-VLT. (3) We conduct comparative experiments on VLT benchmarks with different text granularities, evaluating and analyzing the impact of diverse text on tracking performance. Conclusionally, this work leverages LLM to provide multi-granularity semantic information for VLT task from efficient and diverse perspectives, enabling fine-grained evaluation of multi-modal trackers. In the future, we believe this work can be extended to more datasets to support vision datasets understanding.

关键词： Visualization computer vision Annotations conferences Semantics Natural languages Benchmark testing

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Codebook VQ-VAE Approach for Prostate Cancer Diagnosis using Multiparametric MRI

Codebook VQ-VAE Approach for Prostate Cancer Diagnosis using...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ekaterina Redekop Mara Pleasure Zichen Wang Karthik V Sarma Adam Kinnaird William Speier Corey W Arnold University of California Los Angeles USA University of Alberta Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multiparametric magnetic resonance imaging (mpMRI) plays an essential role in prostate cancer diagnosis as it can noninvasively localize and grade lesions based on their suspicion of representing clinically significant prostate cancer (csPCa). With the development of deep learning, automatic solutions for csPCa detection based on mpMRI have been developed; however, mpMRI data introduces several difficulties, including data scarcity, heterogeneity in image quality across institutions, and missing modalities. This work addresses these difficulties by building a radiology-based foundational model for prostate cancer mpMRI. Foundation models are deep learning models pretrained on a large-scale dataset and they have recently gained significant interest in computer vision and natural language applications. After pretraining, these models are often adapted for a variety of downstream tasks using smaller datasets from within the same domain. In this work, a large prostate multiparametric MRI (mpMRI) dataset was collected by combining data from our institution with two publicly available datasets. Joint modeling of all mpMRI modalities is essential for accurate prostate cancer diagnosis; however, some of these modalities may be missing. Using unsupervised learning, we pretrained modality-specific vector quantized variational autoencoders (VQ-VAE) to form a radiology foundational model. The learned codebook from VQ-VAE was then used to train a multimodal transformer to perform the diagnosis of clinically significant prostate cancer (csPCa). The proposed multimodal transformer models long-range dependencies between latent representations of input modalities and is augmented with modality-level dropout to increase the model robustness to incomplete modalities. Our framework outperforms previously published work and achieves an average AUC/sensitivity/specificity of 0.764/0.690/0.781. Our results show that pretraining on a larger dataset in combination with the power of transfor

关键词： Deep learning Adaptation models computer vision Frequency modulation Computational modeling Magnetic resonance imaging Radiology

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NTIRE 2023 Challenge on Image Denoising: Methods and Results

NTIRE 2023 Challenge on Image Denoising: Methods and Results

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yawei Li Yulun Zhang Radu Timofte Luc Van Gool Zhijun Tu Kunpeng Du Hailing Wang Hanting Chen Wei Li Xiaofei Wang Jie Hu Yunhe Wang Xiangyu Kong Jinlong Wu Dafeng Zhang Jianxing Zhang Shuai Liu Furui Bai Chaoyu Feng Hao Wang Yuqian Zhang Guangqi Shao Xiaotao Wang Lei Lei Rongjian Xu Zhilu Zhang Yunjin Chen Dongwei Ren Wangmeng Zuo Qi Wu Mingyan Han Shen Cheng Haipeng Li Ting Jiang Chengzhi Jiang Xinpeng Li Jinting Luo Wenjie Lin Lei Yu Haoqiang Fan Shuaicheng Liu Aditya Arora Syed Waqas Zamir Javier Vazquez-Corral Konstantinos G. Derpanis Michael S. Brown Hao Li Zhihao Zhao Jinshan Pan Jiangxin Dong Jinhui Tang Bo Yang Jingxiang Chen Chenghua Li Xi Zhang Zhao Zhang Jiahuan Ren Zhicheng Ji Kang Miao Suiyi Zhao Huan Zheng YanYan Wei Kangliang Liu Xiangcheng Du Sijie Liu Yingbin Zheng Xingjiao Wu Cheng Jin Rajeev Irny Sriharsha Koundinya Vighnesh Kamath Gaurav Khandelwal Sunder Ali Khowaja Jiseok Yoon Ik Hyun Lee Shijie Chen Chengqiang Zhao Huabin Yang Zhongjian Zhang Junjia Huang Yanru Zhang Computer Vision Lab ETH Zurich

This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising.

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NTIRE 2021 Depth Guided Image Relighting Challenge

NTIRE 2021 Depth Guided Image Relighting Challenge

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

作者： El Helou, Majed Zhou, Ruofan Susstrunk, Sabine Timofte, Radu Suin, Maitreya Rajagopalan, A. N. Wang, Yuanzhi Lu, Tao Zhang, Yanduo Wu, Yuntao Yang, Hao-Hsiang Chen, Wei-Ting Kuo, Sy-Yen Luo, Hao-Lun Zhang, Zhiguang Luo, Zhipeng He, Jianye Zhu, Zuo-Liang Li, Zhen Qiu, Jia-Xiong Kuang, Zeng-Sheng Lu, Cheng-Ze Cheng, Ming-Ming Shao, Xiu-Li Li, Chenghua Ding, Bosong Qian, Wanli Li, Fangya Li, Fu Deng, Ruifeng Lin, Tianwei Liu, Songhua Li, Xin He, Dongliang Yazdani, Amirsaeed Guo, Tiantong Monga, Vishal Nsampi, Ntumba Elie Hu, Zhongyun Wang, Qing Nathan, Sabari Kansal, Priya Zhao, Tongtong Zhao, Shanshan Ecole Polytech Fed Lausanne Lausanne Switzerland Swiss Fed Inst Technol Zurich Switzerland ASUSTeK Comp Inc ASUS Intelligent Cloud Serv Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Chinese Acad Sci Inst Automat Beijing Peoples R China Couger Inc Tokyo Japan DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Penn State Univ Sch Elect Engn & Comp Sci Informat Proc & Algorithms Lab iPAL University Pk PA 16802 USA Indian Inst Technol Madras Chennai Tamil Nadu India Nankai Univ Tianjin Peoples R China Northwestern Polytech Univ Sch Comp Sci Comp Vis & Computat Photog Grp Xian Peoples R China Dalian Maritime Univ Dalian Peoples R China China Everbright Bank Shanghai Peoples R China Baidu Inc Dept Comp Vis Vis Beijing Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan Peoples R China

ISBN: (纸本)9781665448994

Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge. We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the input image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team submissions in the final competition stage. The competitions, methods, and final results are presented in this paper.

关键词： Geometry computer vision Target tracking Image color analysis conferences Lighting Transforms

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2012 ieee computer society conference on computer vision and pattern recognition workshops, CVPRW 2012

2012 IEEE Computer Society Conference on Computer Vision and...

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2012 ieee computer society conference on computer vision and pattern recognition workshops, CVPRW 2012

ISBN: (纸本)9781467316118

The proceedings contain 143 papers. The topics discussed include: single-view obstacle detection for smart back-up camera systems;high-speed line-scan camera with multi-line CMOS color sensor;stereo vision embedded system for augmented reality;feature detection and matching on an SIMD/MIMD hybrid embedded processor;head-tracking virtual 3-D display for mobile devices;spatiotemporal multiple persons tracking using dynamic vision sensor;real-time body motion analysis for dance pattern recognition;embedded smart sensor for outdoor parking lot lighting control;embedded fall detection with a neural network and bio-inspired stereo vision;a GPU accelerated fast directional chamfer matching algorithm and a detailed comparison with a highly optimized CPU implementation;event-driven embodied system for feature extraction and object recognition in robotic applications;and a CPU-GPU hybrid people counting system for real-world airport scenarios using arbitrary oblique view cameras.

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Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt

Breaking Through the Haze: An Advanced Non-Homogeneous Dehaz...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Han Zhou Wei Dong Yangyi Liu Jun Chen Department of Electrical and Computer Engineering Mcmaster University Hamilton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Haze usually leads to deteriorated images with low contrast, color shift and structural distortion. We observe that many deep learning based models exhibit exceptional performance on removing homogeneous haze, but they usually fail to address the challenge of non-homogeneous dehazing. Two main factors account for this situation. Firstly, due to the intricate and. non uniform distribution of dense haze, the recovery of structural and chromatic features with high fidelity is challenging, particularly in regions with heavy haze. Secondly, the existing small scale datasets for non-homogeneous dehazing are inadequate to support reliable learning of feature mappings between hazy images and. their corresponding haze-free counterparts by convolutional neural network (CNN)-based models. To tackle these two challenges, we propose a novel two branch network that leverages 2D discrete wavelete transform (DWT), fast Fourier convolution (FFC) residual block and a pretrained. ConvNeXt model. Specifically, in the DWT-FFC frequency branch, our model exploits DWT to capture more high-frequency features. Moreover, by taking advantage of the large receptive field, provided, by FFC residual blocks, our model is able to effectively explore global contextual information and. produce images with better perceptual quality In the prior knowledge branch, an ImageNet pretrained ConvNeXt as opposed, to Res2Net is adopted. This enables our model to learn more supplementary information and. acquire a stronger generalization ability. The feasibility and effectiveness of the proposed, method, is demonstrated, via extensive experiments and. ablation studies. The code is available at https://***/zhouhll5/DWT-FFC.

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Gate-Shift-Pose: Enhancing Action recognition in Sports with Skeleton Information

Gate-Shift-Pose: Enhancing Action Recognition in Sports with...

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ieee Winter Applications and computer vision workshops (WACVW)

作者： Edoardo Bianchi Oswald Lanz Free University of Bozen-Bolzano Piazza Università 1 Bolzano Italy

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

This paper introduces Gate-Shift-Pose, an enhanced version of Gate-Shift-Fuse networks, designed for athlete fall classification in figure skating by integrating skeleton pose data alongside RGB frames. We evaluate two fusion strategies: early-fusion, which combines RGB frames with Gaussian heatmaps of pose keypoints at the input stage, and latefusion, which employs a multi-stream architecture with attention mechanisms to combine RGB and pose features. Experiments on the FR-FS dataset demonstrate that Gate-Shift-Pose significantly outperforms the RGB-only baseline, improving accuracy by up to 40% with ResNet18 and 20% with ResNet50. Early-fusion achieves the highest accuracy (98.08%) with ResNet50, leveraging the model's capacity for effective multimodal integration, while latefusion is better suited for lighter backbones like ResNet18. These results highlight the potential of multimodal architectures for sports action recognition and the critical role of skeleton pose information in capturing complex motion patterns.

关键词： Heating systems Accuracy conferences computer architecture Logic gates Skeleton Multisensory integration pattern recognition Sports Residual neural networks

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Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results

Wild Face Anti-Spoofing Challenge 2023: Benchmark and Result...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Dong Wang Jia Guo Qiqi Shao Haochi He Zhian Chen Chuanbao Xiao Ajian Liu Sergio Escalera Hugo Jair Escalante Zhen Lei Jun Wan Jiankang Deng MoreDian InsightFace CASIA Computer Vision Center (UAB) University of Barcelona INAOE

Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during training or saturation during testing. In terms of quantity, the number of spoof subjects is a critical determinant. Most datasets comprise fewer than 2,000 subjects. With regard to diversity, the majority of datasets consist of spoof samples collected in controlled environments using repetitive, mechanical processes. This data collection methodology results in homogenized samples and a dearth of scenario diversity. To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Our dataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 images of 148,169 live subjects, representing a substantial increase in quantity. Moreover, our dataset incorporates spoof data obtained from the internet, spanning a wide array of scenarios and various commercial sensors, including 17 presentation attacks (PAs) that encompass both 2D and 3D forms. This novel data collection strategy markedly enhances FAS data diversity. Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the CVPR2023 workshop. Additionally, we meticulously evaluate representative methods using Protocol 1 and Protocol 2 (Unknown-Type). Through an in-depth examination of the challenge outcomes and benchmark baselines, we provide insightful analyses and propose potential avenues for future research. The dataset is released under Insightface 1 .

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NPBG++: Accelerating Neural Point-Based Graphics

NPBG++: Accelerating Neural Point-Based Graphics

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2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Rakhimov, Ruslan Ardelean, Andrei-Timotei Lempitsky, Victor Burnaev, Evgeny Skolkovo Institute of Science and Technology Russia Yandex Russia Artificial Intelligence Research Institute Russia

ISBN: (纸本)9781665469463

We present a new system $(NPBG++)$ for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time. Our method efficiently lever-ages the multiview observations and the point cloud of a static scene to predict a neural descriptor for each point, improving upon the pipeline of Neural Point-Based Graph-ics [1] in several important ways. By predicting the descrip-tors with a single pass through the source images, we lift the requirement of per-scene optimization while also making the neural descriptors view-dependent and more suit-able for scenes with strong non-Lambertian effects. In our comparisons, the proposed system outperforms previous NVS approaches in terms of fitting and rendering runtimes while producing images of similar quality. Project page: https://***/npbgpp/. © 2022 ieee.

关键词： Rendering (computer graphics)

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