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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

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Robust Data Augmentation and Ensemble Method for Object Detection in Fisheye Camera Images

Robust Data Augmentation and Ensemble Method for Object Dete...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Viet Hung Duong Duc Quyen Nguyen Thien Van Luong Huan Vu Tien Cuong Nguyen VNPT Group VNPT AI Hanoi Vietnam Faculty of Computer Science Phenikaa University Hanoi Vietnam University of Transport and Communications Hanoi Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, traffic surveillance systems have begun leveraging fisheye lenses to minimize the requisite number of cameras for comprehensive coverage of streets and intersections. However, as fisheye images have large radial distortion, they pose new challenges to standard object detection algorithms. In this study, we propose a robust object detection method in traffic scenarios using fisheye cameras. Specifically, we develop a novel data augmentation method, which is applied to VisDrone dataset. Note that we select this dataset for augmentation, since it bears resemblances to the Fisheye8K dataset. Furthermore, we leverage pseudo labels generated by a pre-trained object detection model based on the Fisheye8K and original VisDrone dataset to further enrich the training data. Finally, we utilize various state-of-the-art object detection models trained with different combinations of the proposed augmented data, which are then combined with robust ensemble techniques to further enhance the overall object detection performance. As a result, our proposed method achieves a final F1 score of 64.06% on the 2024 AI City Challenge - Track 4 and ranks first among the competing teams.

关键词： Surveillance Urban areas Training data Object detection Traffic control Data augmentation Cameras

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2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation

2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mansi Sharma Rohit Choudhary Rithvik Anil Thapar Institute of Engineering & Technology Patiala Punjab India Indian Institute of Technology Madras India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Stereo correspondence matching is an essential part of the multi-step stereo depth estimation process. This paper revisits the depth estimation problem, avoiding the explicit stereo-matching step using a simple two-tower convolutional neural network. The proposed algorithm is entitled as 2T-UNet. The idea behind 2T-UNet is to replace cost volume construction with twin convolution towers. These towers have an allowance for different weights between them. Additionally, the input for twin encoders in 2T-UNet are different compared to the existing stereo methods. Generally, a stereo network takes a right and left image pair as input to determine the scene geometry. However, in the 2T-UNet model, the right stereo image is taken as one input and the left stereo image along with its monocular depth clue information, is taken as the other input. Depth clues provide complementary suggestions that help enhance the quality of predicted scene geometry. The 2T-UNet surpasses state-of-the-art monocular and stereo depth estimation methods on the challenging Scene flow dataset, both quantitatively and qualitatively. The architecture performs incredibly well on complex natural scenes, highlighting its usefulness for various real-time applications. Pretrained weights and code will be made readily available.

关键词： Geometry Solid modeling Costs Three-dimensional displays Poles and towers Estimation computer architecture

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Salient Object Detection in Low Contrast Images via Global Convolution and Boundary Refinement

Salient Object Detection in Low Contrast Images via Global C...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nan Mu Xin Xu Xiaolong Zhang School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Benefit from the powerful features created by using deep learning technology, salient object detection has recently witnessed remarkable progresses. However, it is difficult for a deep network to achieve satisfactory results in low contrast images, due to the low signal to noise ratio property, thus previous deep learning based saliency methods may output maps with ambiguous salient objects and blurred boundaries. To address this issue, we propose a deep fully convolutional framework with a global convolutional module (GCM) and a boundary refinement module (BRM) for saliency detection. Our model drives the network to learn the local and global information to discriminate pixels belonging to salient objects or not, thus can produce more uniform saliency map. To refine the localization and classification performance of the network, five GCMs are integrated to preserve more spatial knowledge of feature maps and enable the densely connections with classifiers. Besides, to propagate saliency information with rich boundary content, a BRM is embed behind each convolutional layer. Experiments on six challenging datasets show that the proposed saliency model achieves state-of-the-art performance compared to nine existing approaches in terms of nine evaluation metrics.

关键词： Convolution Feature extraction Object detection Machine learning Semantics Kernel Visualization

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i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?

i-MAE: Are Latent Representations in Masked Autoencoders Lin...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Kevin Zhang Zhiqiang Shen Peking University KNQ.AI Mohamed bin Zayed University of AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Masked image modeling (MIM) has been recognized as a strong self-supervised pre-training approach in the vision domain. However, the mechanism and properties of the learned representations by such a scheme, as well as how to further enhance the representations are so far not well-explored. In this paper, we aim to explore an interactive Masked Autoencoders (i-MAE) framework to enhance the representation capability from two aspects: (1) employing a two-way image reconstruction and a latent feature reconstruction with distillation loss to learn better features; (2) proposing a semantics-enhanced sampling strategy to boost the learned semantics in MAE. Upon the proposed i-MAE architecture, we can address two critical questions to explore the behaviors of the learned representations in MAE: (1) Whether the separability of latent representations in Masked Autoencoders is helpful for model performance? We study it by forcing the input as a mixture of two images instead of one. (2) Whether we can enhance the representations in the latent feature space by controlling the degree of semantics during sampling on Masked Autoencoders? To this end, we propose a sampling strategy within a mini-batch based on the semantics of training samples to examine this aspect. Extensive experiments are conducted on CIFAR-10/100, Tiny-ImageNet and ImageNet-1K datasets to verify the observations we discovered. Furthermore, in addition to qualitatively analyzing the characteristics of the latent representations, we examine the existence of linear separability and the degree of semantics in the latent space by proposing two evaluation schemes. The surprising and consistent results across the qualitative and quantitative experiments demonstrate that i-MAE is a superior framework design for understanding MAE frameworks, as well as achieving better representational ability.

关键词： Training Measurement computer vision Image recognition conferences Semantics Aerospace electronics

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M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Real-World Applications

M2U-Net: Effective and Efficient Retinal Vessel Segmentation...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tim Laibacher Tillman Weyde Sepehr Jalali City University of London University College London

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems. The M2U-Net has a new encoder-decoder architecture that is inspired by the U-Net. It adds pretrained components of MobileNetV2 in the encoder part and novel contractive bottleneck blocks in the decoder part that, combined with bilinear upsampling, drastically reduce the parameter count to 0.55M compared to 31.03M in the original U-Net. We have evaluated its performance against a wide body of previously published results on three public datasets. On two of them, the M2U-Net achieves new state-of-the-art performance by a considerable margin. When implemented on a GPU, our method is the first to achieve real-time inference speeds on high-resolution fundus images. We also implemented our proposed network on an ARM-based embedded system where it segments images in between 0.6 and 15 sec, depending on the resolution. Thus, the M2U-Net enables a number of applications of retinal vessel structure extraction, such as early diagnosis of eye diseases, retinal biometric authentication systems, and robot assisted microsurgery.

关键词： Image segmentation Image resolution Retinal vessels computer architecture Training Neural networks

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Multi-modal Aerial View Image Challenge: Sensor Domain Translation

Multi-modal Aerial View Image Challenge: Sensor Domain Trans...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Spencer Low Oliver Nina Dylan Bowald Angel D. Sappa Nathan Inkawhich Peter Bruns Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH ESPOL Polytechnic University Ecuador Computer Vision Center Spain Air Force Research Laboratory Rome NY University of Utah Salt Lake UT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper describes the design, outcomes, and top methods of the 2nd annual Multi-modal Aerial View Image Challenge (MAVIC) aimed at cross modality aerial image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. Specifically, the challenge centers on translation between synthetic aperture radar (SAR), electro-optical (EO), camera (RGB), and infrared (IR) sensor modalities, a budding area of research that has begun to garner attention. While last year’s inaugural challenge demonstrated the feasibility of SAR→EO translation, this year’s challenge made significant improvements in dataset coverage, sensor variation, experimental design, and methods covering the tasks of SAR→EO, SAR→RGB, SAR→IR, RGB→IR introducing a new dataset called translation. By Multi-modal Aerial Gathered Image Composites (MAGIC); multimodal image translation is available for different comparisons. With a more rigorous set of translation performance metrics, winners were determined from aggregation of L1-norm, LPIPS (Learned Perceptual Image Patch Similarity, and FID (Frechet Inception Distance) scores. The wining methods included the pix2pixHD and LPIPS metrics as loss functions with an aggregated score 5% better separated by the SAR→EO and RGB→IR translation scores.

关键词： Measurement computer vision Design methodology conferences Cameras pattern recognition Synthetic aperture radar

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Fourier Prior-Based Two-Stage Architecture for Image Restoration

Fourier Prior-Based Two-Stage Architecture for Image Restora...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hemkant Nehete Amit Monga Partha Kaushik Brajesh Kumar Kaushik Indian Institute of Technology Roorkee India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This work presents a novel two stage architecture designed to enhance degraded images affected by environmental factors such as haze, blur, fog, and rain. Despite the dominance of deep Convolutional Neural Networks (CNNs) and Transformers in single image restoration tasks, existing methods neglect the intrinsic priors for physical properties of degradation. To enhance the generalization ability of image restoration models, we propose Fourier prior based on a key observation that substituting the Fourier amplitude of degraded images with that of clean images effectively mitigates degradation. Therefore, amplitude contains degradation information, while the phase retains background structures. Consequently, a two-stage model is proposed, that consists of Amplitude Refinement Unit (ARU) and the Phase Refinement Unit (PRU), that separately restore both amplitude and phase information, respectively. ARU and PRU leverage a CNN-Transformer-based architecture to extract local and global features, overcoming computational constraints posed by large image sizes in Transformers. Additionally, a multi-scale approach in ARU refines amplitude features at coarse and fine levels, improving restoration efficiency. Experimental results across multiple image restoration tasks, like image deraining, dehazing, and low-light enhancement, indicate that the proposed architecture improved the performance in terms of PSNR, SSIM, and computational efficiency compared to state-of-the-art Transformer approaches.

关键词： Degradation Rain Computational modeling Frequency-domain analysis computer architecture Transformers Feature extraction

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Unsupervised Multi-Person 3D Human Pose Estimation From 2D Poses Alone

Unsupervised Multi-Person 3D Human Pose Estimation From 2D P...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Peter Hardy Hansung Kim School of Electronics and Computer Science University of Southampton UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Current unsupervised 2D-3D human pose estimation (HPE) methods do not work in multi-person scenarios due to perspective ambiguity in monocular images. Therefore, we present one of the first studies investigating the feasibility of unsupervised multi-person 2D-3D HPE from just 2D poses alone, focusing on reconstructing human interactions. To address the issue of perspective ambiguity, we expand upon prior work by predicting the cameras’ elevation angle relative to the subjects’ pelvis. This allows us to rotate the predicted poses to be level with the ground plane, while obtaining an estimate for the vertical offset in 3D between individuals. Our method involves independently lifting each subject’s 2D pose to 3D, before combining them in a shared 3D coordinate system. The poses are then rotated and offset by the predicted elevation angle before being scaled. This by itself enables us to retrieve an accurate 3D reconstruction of their poses. We present our results on the CHI3D dataset, introducing its use for unsupervised 2D-3D pose estimation with three new quantitative metrics, and establishing a benchmark for future research.

关键词： Measurement Three-dimensional displays Accuracy Pose estimation Focusing Benchmark testing Real-time systems

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Salient Object-Aware Background Generation using Text-Guided Diffusion Models

Salient Object-Aware Background Generation using Text-Guided...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Amir Erfan Eshratifar João V. B. Soares Kapil Thadani Shaunak Mishra Mikhail Kuznetsov Yueh-Ning Ku Paloma De Juan Yahoo Research Amazon ByteDance

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating background scenes for salient objects plays a crucial role across various domains including creative design and e-commerce, as it enhances the presentation and context of subjects by integrating them into tailored environments. Background generation can be framed as a task of text-conditioned outpainting, where the goal is to extend image content beyond a salient object’s boundaries on a blank background. Although popular diffusion models for text-guided inpainting can also be used for outpainting by mask inversion, they are trained to fill in missing parts of an image rather than to place an object into a scene. Consequently, when used for background creation, inpainting models frequently extend the salient object’s boundaries and thereby change the object’s identity, which is a phenomenon we call "object expansion." This paper introduces a model for adapting inpainting diffusion models to the salient object outpainting task using Stable Diffusion and ControlNet architectures. We present a series of qualitative and quantitative results across models and datasets, including a newly proposed metric to measure object expansion that does not require any human labeling. Compared to Stable Diffusion 2.0 Inpainting, our proposed approach reduces object expansion by 3.6× on average with no degradation in standard visual metrics across multiple datasets.

关键词： Measurement Degradation Adaptation models Visualization conferences Diffusion models pattern recognition

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Classification of 2D Ultrasound Breast Cancer Images with Deep Learning

Classification of 2D Ultrasound Breast Cancer Images with De...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jack Ellis Kofi Appiah Emmanuel Amankwaa-Frempong Sze Chai Kwok Department of Computer Science University of York York UK Sweden-Ghana Medical Center Accra Ghana Division of Natural & Applied Sciences Duke Kunshan University China Affiliated Mental Health Center School of Psychology & Cognitive Sci. ECNU China Shanghai Changning Mental Health Center Shanghai China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Breast cancer is the second most prevalent form of cancer and is the “leading cause of most cancer-related deaths in women”. Most women living in low- and middle-income countries (LMIC) have limited access to the existing poor health systems, restricted access to treatment facilities, and in general lack of breast cancer screening programmes. The likelihood of women living in LMIC attending a health facility with advanced-stage breast cancer is very high and the chances of them being able to afford treatment at that stage, even if the treatment is available, is very low. In this work, we evaluate the capabilities of deep learning as a classification tool with the aim of detecting cancerous ultrasound breast images. We aim to deploy a simple classifier on a mobile device with an inexpensive handheld ultrasound imaging system to pick up breast cancer cases that will need medical attention. We demonstrate in this work that with minimal ultrasound images, a de novo system trained from scratch can achieve accuracy of close to 64% and about 78% when the same model is pre-trained.

关键词： Deep learning Ultrasonic imaging Accuracy Computational modeling Transfer learning Breast cancer Mobile handsets

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