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Learning to See in Nighttime Driving Scenes with Inter-frequency Priors

Learning to See in Nighttime Driving Scenes with Inter-frequ...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Fan, Zhentao Wu, Xianhao Chen, Xiang Li, Yufeng Shenyang Aerospace University College of Electronic and Information Engineering China Nanjing University of Science and Technology School of Computer Science and Engineering China

ISBN: (纸本)9798350302493

Currently, image-to-image translation methods have achieved significant performance with the help of deep CNNs and GANs, but most existing models are not suitable for autonomous driving scenarios due to their interpretability. In this paper, we develop a high-quality image translator (i.e. night → day), N2D-LPNet, to facilitate nighttime driving scene dperception. Instead of pursuing a complex network structure, we first attempt to explore the inter-frequency relation knowledge to simplify image translation process. Specifically, the lightweight Laplace pyramid is introduced as the backbone architecture to decompose the feature maps of nighttime image into high- and low-frequency components. Considering the similar morphological properties of different frequency components, we design a flexible inter-frequency guiding strategy which utilizes each lower frequency information to refine the higher frequency feature in a progressive manner. Benefiting from the advantage of constraint from inter-frequency priors, our method can process the nighttime image better under the practical driving system while still persevere competitive results. We also discuss the potential value of N2D-LPNet for other high-level vision tasks. © 2023 IEEE.

关键词： Complex networks

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Consistency and Accuracy of CelebA Attribute Values

Consistency and Accuracy of CelebA Attribute Values

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wu, Haiyu Bezold, Grace Günther, Manuel Boult, Terrance King, Michael C. Bowyer, Kevin W. University of Notre Dame United States University of Zurich Switzerland University of Colorado Colorado Springs United States Florida Institute of Technology United States

ISBN: (纸本)9798350302493

We report the first systematic analysis of the experimental foundations of facial attribute classification. Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with ≥ 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency. Of 5,068 duplicate face appearances in CelebA, attributes have contradicting values on from 10 to 860 of the 5,068 duplicates. Manual audit of a subset of CelebA estimates error rates as high as 40% for (no beard=false), even though the labeling consistency experiment indicates that no beard could be assigned with ≥ 95% consistency. Selecting the mouth slightly open (MSO) for deeper analysis, we estimate the error rate for (MSO=true) at about 20% and (MSO=false) at about 2%. A corrected version of the MSO attribute values enables learning a model that achieves higher accuracy than previously reported for MSO. Corrected values for CelebA MSO are available at https://***/HaiyuWu/CelebAMSO. © 2023 IEEE.

关键词： computer vision

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On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild

On the Real-Time Semantic Segmentation of Aphid Clusters in ...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Rahman, Raiyan Indris, Christopher Zhang, Tianxiao Li, Kaidong McCornack, Brian Flippo, Daniel Sharda, Ajay Wang, Guanghui Toronto Metropolitan University Department of Computer Science Toronto Canada University of Kansas Department of Electrical Engineering and Computer Science Lawrence United States Kansas State University Department of Entomology Manhattan United States Kansas State University Department of Biological and Agricultural Engineering Manhattan United States

ISBN: (纸本)9798350302493

Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. As a result, a considerable amount of pesticide is wasted on areas without pests, while inadequate amounts are applied to areas with severe infestations. The paper focuses on the urgent need for an intelligent autonomous system that can locate and spray infestations within complex crop canopies, reducing pesticide use and environmental impact. We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids. A multiscale dataset is generated to allow for learning the clusters at different scales. We compare the segmentation speeds and accuracy of four state-of-the-art real-time semantic segmentation models on the aphid cluster dataset, benchmarking them against nonreal-time models. The study results show the effectiveness of a real-time solution, which can reduce inefficient pesticide use and increase crop yields, paving the way towards an autonomous pest detection system. © 2023 IEEE.

关键词： Semantics

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Camera-based Recovery of Cardiovascular Signals from Unconstrained Face Videos using an Attention Network

Camera-based Recovery of Cardiovascular Signals from Unconst...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Deshpande, Yogesh Thapa, Surendrabikram Sarkar, Abhijit Abbott, A. Lynn Virginia Tech Bradley Department of Electrical and Computer Engineering United States Virginia Tech Department of Computer Science United States Virginia Tech Transportation Institute United States

ISBN: (纸本)9798350302493

This paper addresses the problem of recovering the shape morphology of blood volume pulse (BVP) information from a video of a person's face. Video-based remote plethysmography methods have shown promising results in estimating vital signs such as heart rate and breathing rate. However, recovering the instantaneous pulse rate signals is still a challenge for the community. This is due to the fact that most of the previous methods concentrate on capturing the temporal average of the cardiovascular signals. In contrast, we present an approach in which BVP signals are extracted with a focus on the recovery of the signal shape morphology as a generalized form for the computation of physiological metrics. We also place emphasis on allowing natural movements by the subject. Furthermore, our system is capable of extracting individual BVP instances with sufficient signal detail to facilitate candidate re-identification. These improvements have resulted in part from the incorporation of a robust skin-detection module into the overall imaging-based photoplethysmography (iPPG) framework. We present extensive experimental results using the challenging UBFC-Phys dataset and the well-known COHFACE dataset. The source code is available at https://***/yogeshd21/CVPM-2023-iPPG-Paper. © 2023 IEEE.

关键词： Recovery

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A System for Dense Monocular Mapping with a Fisheye Camera

A System for Dense Monocular Mapping with a Fisheye Camera

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Gallagher, Louis Sistu, Ganesh Horgan, Jonathan McDonald, John B. Maynooth University Department of Computer Science Co Kildare Ireland Valeo Vision Systems Tuam Ireland

ISBN: (纸本)9798350302493

We introduce a novel dense mapping system that uses a single monocular fisheye camera as the sole input sensor and incrementally builds a dense surfel representations of the scene's 3D geometry. We extend an existing hybrid sparse-dense monocular SLAM system, reformulating the mapping pipeline in terms of the Kannala-Brandt fisheye camera model. Each frame is processed in its original undistorted fisheye form, with no attempt to remove distortion. To estimate depth, we introduce a new version of the PackNet depth estimation neural network adapted for fisheye inputs. We reformulate PackNet's multi-view stereo self-supervised loss in terms of the Kannala-Brandt fisheye camera model. To encourage the network to learn metric depth during training, the pose network is weakly supervised with the camera's ground-truth inter-frame velocity. To improve overall performance, we additionally provide sparse depth supervision from dataset LiDAR and SICK laser scanners. We demonstrate our system's performance on the realworld KITTI-360 benchmark dataset. Our experimental results show that our system is capable of accurate, metric camera tracking and dense surface reconstruction within local windows. Our system operates within real-time processing rates and in challenging conditions. We direct the reader to the following video where the system can be seen in operation: https://***/Y-9q-wfqocs. © 2023 IEEE.

关键词： Mapping

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Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment

Enhancing Multi-Camera People Tracking with Anchor-Guided Cl...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Huang, Hsiang-Wei Yang, Cheng-Yen Jiang, Zhongyu Kim, Pyong-Kun Lee, Kyoungoh Kim, Kwangju Ramkumar, Samartha Mullapudi, Chaitanya Jang, In-Su Huang, Chung-I Hwang, Jenq-Neng University of Washington Information Processing Lab United States Electronics and Telecommunications Research Institute Korea Republic of National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

Multi-camera multiple people tracking has become an increasingly important area of research due to the growing demand for accurate and efficient indoor people tracking systems, particularly in settings such as retail, healthcare centers, and transit hubs. We proposed a novel multi-camera multiple people tracking method that uses anchor-guided clustering for cross-camera re-identification and spatio-temporal consistency for geometry-based cross-camera ID reassigning. Our approach aims to improve the accuracy of tracking by identifying key features that are unique to every individual and utilizing the overlap of views between cameras to predict accurate trajectories without needing the actual camera parameters. The method has demonstrated robustness and effectiveness in handling both synthetic and real-world data. The proposed method is evaluated on CVPR AI City Challenge 2023 dataset, achieving IDF1 of 95.36% with the first-place ranking in the challenge. The code is available at: https://***/ipl-uw/AIC23-Track1-UWIPL-ETRI. © 2023 IEEE.

关键词： computer vision

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Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network

Asynchronous Events-based Panoptic Segmentation using Graph ...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Kachole, Sanket Alkendi, Yusra Baghaei Naeini, Fariborz Makris, Dimitrios Zweiri, Yahya Kingston University Department of Computer Science London United Kingdom Abu Dhabi United Arab Emirates Khalifa University of Science and Technology Department of Aerospace Engineering Abu Dhabi United Arab Emirates Ipsotek An Eviden Company London United Kingdom

ISBN: (纸本)9798350302493

In the context of robotic grasping, object segmentation encounters several difficulties when faced with dynamic conditions such as real-time operation, occlusion, low lighting, motion blur, and object size variability. In response to these challenges, we propose the Graph Mixer Neural Network that includes a novel collaborative contextual mixing layer, applied to 3D event graphs formed on asynchronous events. The proposed layer is designed to spread spatiotemporal correlation within an event graph at four nearest neighbor levels parallelly. We evaluate the effectiveness of our proposed method on the Event-based Segmentation (ESD) Dataset, which includes five unique image degradation challenges, including occlusion, blur, brightness, trajectory, scale variance, and segmentation of known and unknown objects. The results show that our proposed approach outperforms state-of-the-art methods in terms of mean intersection over the union and pixel accuracy. Code available at: https://***/sanket0707/*** © 2023 IEEE.

关键词： Image segmentation

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Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection

Back to the Feature: Classical 3D Features are (Almost) All ...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Horwitz, Eliahu Hoshen, Yedid The Hebrew University of Jerusalem School of Computer Science and Engineering Israel

ISBN: (纸本)9798350302493

Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity. First, we present a surprising finding: standard color-only methods outperform all current methods that are explicitly designed to exploit 3D information. This is counter-intuitive as even a simple inspection of the dataset shows that color-only methods are insufficient for images containing geometric anomalies. This motivates the question: how can anomaly detection methods effectively use 3D information? We investigate a range of shape representations including hand-crafted and deep-learning-based;we demonstrate that rotation invariance plays the leading role in the performance. We uncover a simple 3D-only method that beats all recent approaches while not using deep learning, external pre-training datasets, or color information. As the 3D-only method cannot detect color and texture anomalies, we combine it with color-based features, significantly outperforming previous state-of-the-art. Our method, dubbed BTF (Back to the Feature) achieves pixel-wise ROCAUC: 99.3% and PRO: 96.4% on MVTec 3D-AD. © 2023 IEEE.

关键词： Anomaly detection

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Spatial-Angular Multi-Scale Mechanism for Light Field Spatial Super-Resolution

Spatial-Angular Multi-Scale Mechanism for Light Field Spatia...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Gao, Chen Lin, Youfang Chang, Song Zhang, Shuo School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining China

ISBN: (纸本)9798350302493

Light Field (LF) cameras are promising due to their ability to capture both spatial and angular information of scenes. However, the trade-off between spatial and angular resolution significantly limits the real-world applications. In this paper, we propose a spatial-angular multi-scale decoupling network to reconstruct high-resolution LF images. Considering the epipolar geometry, we propose a spatial-angular multi-scale processing approach to explore the correspondence of sub-pixel information with different disparity ranges between sub-aperture images in LFs. We extract sub-pixel information from various dimensions and fuse it to generate global high-frequency details. Finally, we combine upsampled low-frequency and high-frequency details to generate high resolution results. To further filter the correct interpolation information, we use the shear operation to change the disparity range of the LF images and fine-tune the results. Experimental results on synthetic and real-world datasets demonstrate that our method outperforms other state-of-the-art methods in visual and numerical evaluations, especially on datasets with small disparity ranges. Furthermore, our approach fully considers the epipolar geometry of the LF image, enabling us to recover information that better maintains the imaging consistency of the LF. © 2023 IEEE.

关键词： Numerical methods

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Dilated Convolutional Transformer for High-Quality Image Deraining

Dilated Convolutional Transformer for High-Quality Image Der...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Li, Yufeng Lu, Jiyang Chen, Hongming Wu, Xianhao Chen, Xiang Shenyang Aerospace University College of Electronic and Information Engineering China Nanjing University of Science and Technology School of Computer Science and Engineering China

ISBN: (纸本)9798350302493

Convolutional neural networks (CNNs) and Transformers have achieved significant success in image signal processing. However, little effort has been made to effectively combine the properties of these two architectures to satisfy image deraining. In this paper, we propose an effective de-raining method, dilated convolutional Transformer (DCT), which can enlarge the receptive fields of the network to aggregate global information. The fundamental building unit of our approach is the dilformer block containing multi-dilconv sparse attention (MDSA) and multi-dilconv feed-forward network (MDFN). The MDSA calculates the multi-scale query to generate accurate similarity map so that rich multi-scale information can be better utilized for the high-quality image reconstruction. In addition, we adopt ReLU to replace the original softmax to enforce sparsity in the Transformer for better feature aggregation. The MDFN is further established to better integrate the rain information of different scales in the feature transformation. Extensive experiments on the benchmarks show the favorable performance against state-of-the-art approaches. © 2023 IEEE.

关键词： Image reconstruction

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