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Learning a Proposal Classifier for Multiple Object Tracking

Learning a Proposal Classifier for Multiple Object Tracking

引用

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

作者： Dai, Peng Weng, Renliang Choi, Wongun Zhang, Changshui He, Zhangping Ding, Wei Tsinghua Univ Beijing Peoples R China Aibee Inc Beijing Peoples R China

ISBN: (纸本)9781665445092

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. However, it is not trivial to solve the data-association problem in an end-to-end fashion. In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph. This framework is similar to the two-stage object detector Faster RCNN, and can solve the MOT problem in a data-driven way. For proposal generation, we propose an iterative graph clustering method to reduce the computational cost while maintaining the quality of the generated proposals. For proposal scoring, we deploy a trainable graph-convolutional-network (GCN) to learn the structural patterns of the generated proposals and rank them according to the estimated quality scores. For trajectory inference, a simple deoverlapping strategy is adopted to generate tracking output while complying with the constraints that no detection can be assigned to more than one track. We experimentally demonstrate that the proposed method achieves a clear performance improvement in both MOTA and IDF1 with respect to previous state-of-the-art on two public benchmarks.

关键词： Deep learning computer vision Detectors Market research Trajectory Computational efficiency Proposals

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RPN Prototype Alignment For Domain Adaptive Object Detector

RPN Prototype Alignment For Domain Adaptive Object Detector

引用

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

作者： Zhang, Yixin Wang, Zilei Mao, Yushi Univ Sci & Technol China Dept Automat Hefei Peoples R China

ISBN: (纸本)9781665445092

Recent years have witnessed great progress of object detection. However, due to the domain shift problem, applying the knowledge of an object detector learned from one specific domain to another one often suffers severe performance degradation. Most existing methods adopt feature alignment either on the backbone network or instance classifier to increase the transferability of object detector. Differently, we propose to perform feature alignment in the RPN stage such that the foreground and background RPN proposals in target domain can be effectively distinguished. Specifically, we first construct one set of learnable RPN prototpyes, and then enforce the RPN features to align with the prototypes for both source and target domains. It essentially cooperates the learning of RPN prototypes and features to align the source and target RPN features. Particularly, we propose a simple yet effective method suitable for RPN feature alignment to generate high-quality pseudo label of proposals in target domain, i.e., using the filtered detection results with IoU. Furthermore, we adopt Grad CAM to find the discriminative region within a foreground proposal and use it to increase the discriminability of RPN features for alignment. We conduct extensive experiments on multiple cross-domain detection scenarios, and the results show the effectiveness of our proposed method against previous stateof-the-art methods.

关键词： Degradation computer vision Prototypes Detectors Object detection Feature extraction pattern recognition

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Photorealistic Arm Robot Simulation for 3D Plant Reconstruction and Automatic Annotation using Unreal Engine 5

Photorealistic Arm Robot Simulation for 3D Plant Reconstruct...

引用

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

作者： Xingjian Li Jeremy Park Chris Reberg-Horton Steven Mirsky Edgar Lobaton Lirong Xiang Department of Biological and Agricultural Engineering North Carolina State University Department of Electrical and Computer Engineering North Carolina State University Department of Computer Science North Carolina State University Department of Crop and Soil Science North Carolina State University Sustainable Agricultural Systems Laboratory USDA-ARS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Robotics paired with computer vision are widely used in precision agriculture. Simulations are critical for safety and performance estimation by verifying their routine in a virtual world before real-world testing and deployment. However, many simulators used in agricultural robots lack photorealism in their virtual worlds compared to the real world. We implemented Unreal Engine 5 (UE5) and the Robot Operating System (ROS) to develop a robot simulator tailored to agricultural tasks and synthetic data generation with RGB, segmentation, and depth images. We designed a method for assigning multiple segmentation labels within a single plant mesh. We experimented with a semi-spherical routine for two robot arms to perform 3D point cloud reconstruction across 10 plant assets. We showed our simulator produces much more accurate segmentation images and reconstruction compared to existing UE5 solutions. We extend our results with Neural Radiance Field (NeRF) reconstructions. The packaged simulator, UE5 project, and ROS package with the Python routine can be found at https://***/NCSU-BAE-ARLab/AgriRoboSimUE5.

关键词： Image segmentation computer vision Three-dimensional displays Computational modeling Neural radiance field Manipulators Image reconstruction

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Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation patterns

Run-time Monitoring of 3D Object Detection in Automated Driv...

引用

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

作者： Hakan Yekta Yatbaz Mehrdad Dianati Konstantinos Koufos Roger Woodman WMG University of Warwick School of Electronics Electrical Engineering and Computer Science (EEECS) Queen's University of Belfast

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring the integrity of object detection for errors within the perception module of automated driving systems (ADS) is paramount for ensuring safety. Despite recent advancements in deep neural network (DNN)-based object detectors, their susceptibility to detection errors, particularly in the less-explored realm of 3D object detection, remains a significant concern. State-of-the-art integrity monitoring (also known as introspection) mechanisms in 2D object detection mainly utilise the activation patterns in the final layer of the DNN-based detector’s backbone. However, that may not sufficiently address the complexities and sparsity of data in 3D object detection. To this end, we conduct, in this article, an extensive investigation into the effects of activation patterns extracted from various layers of the backbone network for introspecting the operation of 3D object detectors. Through a comparative analysis using Kitti and NuScenes datasets with PointPillars and CenterPoint detectors, we demonstrate that using earlier layers’ activation patterns enhances the error detection performance of the integrity monitoring system, yet increases computational complexity. To address the real-time operation requirements in ADS, we also introduce a novel introspection method that combines activation patterns from multiple layers of the detector’s backbone and report its performance.

关键词： computer vision Three-dimensional displays conferences Object detection Detectors Real-time systems Safety

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Generalizing to the Open World: Deep Visual Odometry with Online Adaptation

Generalizing to the Open World: Deep Visual Odometry with On...

引用

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

作者： Li, Shunkai Wu, Xin Cao, Yingdian Zha, Hongbin Peking Univ Sch EECS Key Lab Machine Percept MOE PKU SenseTime Machine Vis Joint Lab Beijing Peoples R China

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Despite learning-based visual odometry (VO) has shown impressive results in recent years, the pretrained networks may easily collapse in unseen environments. The large domain gap between training and testing data makes them difficult to generalize to new scenes. In this paper, we propose an online adaptation framework for deep VO with the assistance of scene-agnostic geometric computations and Bayesian inference. In contrast to learning-based pose estimation, our method solves pose from optical flow and depth while the single-view depth estimation is continuously improved with new observations by online learned uncertainties. Meanwhile, an online learned photometric uncertainty is used for further depth and pose optimization by a differentiable Gauss-Newton layer. Our method enables fast adaptation of deep VO networks to unseen environments in a self-supervised manner. Extensive experiments including Cityscapes to KITTI and outdoor KITTI to indoor TUM demonstrate that our method achieves state-of-the-art generalization ability among self-supervised VO methods.

关键词： Training Optical filters Uncertainty Pose estimation Bayes methods pattern recognition Optimization

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Multiresolution Knowledge Distillation for Anomaly Detection

Multiresolution Knowledge Distillation for Anomaly Detection

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

作者： Salehi, Mohammadreza Sadjadi, Niousha Baselizadeh, Soroosh Rohban, Mohammad H. Rabiee, Hamid R. Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781665445092

Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. The challenges to learn such a representation are two-fold. Firstly, the sample size is not often large enough to learn a rich generalizable representation through conventional techniques. Secondly, while only normal samples are available at training, the learned features should be discriminative of normal and anomalous samples. Here, we propose to use the "distillation" of features at various layers of an expert network, which is pre-trained on ImageNet, into a simpler cloner network to tackle both issues. We detect and localize anomalies using the discrepancy between the expert and cloner networks' intermediate activation values given an input sample. We show that considering multiple intermediate hints in distillation leads to better exploitation of the expert's knowledge and a more distinctive discrepancy between the two networks, compared to utilizing only the last layer activation values. Notably, previous methods either fail in precise anomaly localization or need expensive region-based training. In contrast, with no need for any special or intensive training procedure, we incorporate interpretability algorithms in our novel framework to localize anomalous regions. Despite the striking difference between some test datasets and ImageNet, we achieve competitive or significantly superior results compared to SOTA on MNIST, F-MNIST, CIFAR-10, MVTecAD, Retinal-OCT, and two other medical datasets on both anomaly detection and localization.

关键词： Training Location awareness Knowledge engineering computer vision Image resolution pattern recognition Task analysis

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PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers

PCLs: Geometry-aware Neural Reconstruction of 3D Pose with P...

引用

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

作者： Yu, Frank Salzmann, Mathieu Fua, Pascal Rhodin, Helge Univ British Columbia Vancouver BC Canada Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781665445092

Local processing is an essential feature of CNNs and other neural network architectures-it is one of the reasons why they work so well on images where relevant information is, to a large extent, local. However, perspective effects stemming from the projection in a conventional camera vary for different global positions in the image. We introduce Perspective Crop Layers (PCLs)-a form of perspective crop of the region of interest based on the camera geometry- and show that accounting for the perspective consistently improves the accuracy of state-of-theart 3D pose reconstruction methods. PCLs are modular neural network layers, which, when inserted into existing CNN and MLP architectures, deterministically remove the location-dependent perspective effects while leaving end-to-end training and the number of parameters of the underlying neural network unchanged. We demonstrate that PCL leads to improved 3D human pose reconstruction accuracy for CNN architectures that use cropping operations, such as spatial transformer networks (STN), and, somewhat surprisingly, MLPs used for 2D-to-3D keypoint lifting. Our conclusion is that it is important to utilize camera calibration information when available, for classical and deep-learning-based computer vision alike. PCL offers an easy way to improve the accuracy of existing 3D reconstruction networks by making them geometry-aware.

关键词： Training computer vision Three-dimensional displays Neural networks Crops computer architecture Cameras

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HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms

HistoGAN: Controlling Colors of GAN-Generated and Real Image...

引用

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

作者： Afifi, Mahmoud Brubaker, Marcus A. Brown, Michael S. York Univ N York ON Canada

ISBN: (纸本)9781665445092

While generative adversarial networks (GANs) can successfully produce high-quality images, they can be challenging to control. Simplifying GAN-based image generation is critical for their adoption in graphic design and artistic work. This goal has led to significant interest in methods that can intuitively control the appearance of images generated by GANs. In this paper, we present HistoGAN, a color histogram-based method for controlling GAN-generated images' colors. We focus on color histograms as they provide an intuitive way to describe image color while remaining decoupled from domain-specific semantics. Specifically, we introduce an effective modifi- cation of the recent StyleGAN architecture [31] to control the colors of GAN-generated images specified by a target color histogram feature. We then describe how to expand HistoGAN to recolor real images. For image recoloring, we jointly train an encoder network along with HistoGAN. The recoloring model, ReHistoGAN, is an unsupervised approach trained to encourage the network to keep the original image's content while changing the colors based on the given target histogram. We show that this histogram-based approach offers a better way to control GAN-generated and real images' colors while producing more compelling results compared to existing alternative strategies.

关键词： Graphics Histograms computer vision Image color analysis Image synthesis Semantics Generative adversarial networks

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Image-to-image Translation via Hierarchical Style Disentanglement

Image-to-image Translation via Hierarchical Style Disentangl...

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

作者： Li, Xinyang Zhang, Shengchuan Hu, Jie Cao, Liujuan Hong, Xiaopeng Mao, Xudong Huang, Feiyue Wu, Yongjian Ji, Rongrong Xiamen Univ Sch Informat Xiamen Peoples R China Xi An Jiao Tong Univ Xian Peoples R China Peng Cheng Lab Shenzhen Peoples R China Xiamen Univ Tencent Youtu Lab Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China

ISBN: (纸本)9781665445092

Recently, image-to-image translation has made significant progress in achieving both multi-label (i.e., translation conditioned on different labels) and multi-style (i.e., generation with diverse styles) tasks. However, due to the unexplored independence and exclusiveness in the labels, existing endeavors are defeated by involving uncontrolled manipulations to the translation results. In this paper, we propose Hierarchical Style Disentanglement (HiSD) to address this issue. Specifically, we organize the labels into a hierarchical tree structure, in which independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. Correspondingly, a new translation process is designed to adapt the above structure, in which the styles are identified for controllable translations. Both qualitative and quantitative results on the CelebA-HQ dataset verify the ability of the proposed HiSD. The code has been released at https://***/imlixinyang/HiSD.

关键词： Training computer vision Codes Scalability Process control Semisupervised learning pattern recognition

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Event Camera Demosaicing via Swin Transformer and Pixel-focus Loss

Event Camera Demosaicing via Swin Transformer and Pixel-focu...

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

作者： Yunfan Lu Yijie Xu Wenzong Ma Weiyu Guo Hui Xiong AI Thrust Hong Kong University of Science and Technology (Guangzhou)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent research has highlighted improvements in high-quality imaging guided by event cameras, with most of these efforts concentrating on the RGB domain. However, these advancements frequently neglect the unique challenges introduced by the inherent flaws in the sensor design of event cameras in the RAW domain. Specifically, this sensor design results in the partial loss of pixel values, posing new challenges for RAW domain processes like demosaicing. The challenge intensifies as most research in the RAW domain is based on the premise that each pixel contains a value, making the straightforward adaptation of these methods to event camera demosaicing problematic. To end this, we present a Swin-Transformer-based backbone and a pixel-focus loss function for demosaicing with missing pixel values in RAW domain processing. Our core motivation is to refine a general and widely applicable foundational model from the RGB domain for RAW domain processing, thereby broadening the model’s applicability within the entire imaging process. Our method harnesses multi-scale processing and space-to-depth techniques to ensure efficiency and reduce computing complexity. We also proposed the Pixel-focus Loss function for network fine-tuning to improve network convergence based on our discovery of a long-tailed distribution in training loss. Our method has undergone validation on the MIPI Demosaic Challenge dataset, with subsequent analytical experimentation confirming its efficacy. All code and trained models are released here: https://***/yunfanLu/ev-demosaic.

关键词： Training computer vision Computational modeling conferences computer architecture Cameras Transformers

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