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BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training

BigDetection: A Large-scale Benchmark for Improved Object De...

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

作者： Cai, Likun Zhang, Zhi Zhu, Yi Zhang, Li Li, Mu Xue, Xiangyang Fudan Univ Shanghai Peoples R China Amazon Inc Seattle WA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multiple datasets and open challenges for object detection have been introduced in recent years. To build more general and powerful object detection systems, in this paper, we construct a new large-scale benchmark termed BigDetection. Our goal is to simply leverage the training data from existing datasets (LVIS, OpenImages and Object365) with carefully designed principles, and curate a larger dataset for improved detector pre-training. Specifically, we generate a new taxonomy which unifies the heterogeneous label spaces from different sources. Our BigDetection dataset has 600 object categories and contains over 3.4M training images with 36M bounding boxes. It is much larger in multiple dimensions than previous benchmarks, which offers both opportunities and challenges. Extensive experiments demonstrate its validity as a new benchmark for evaluating different object detection methods and its effectiveness as a pre-training dataset. The code and models are available at https://***/amazonresearch/bigdetection.

关键词： Training computer vision conferences Taxonomy Training data Object detection Detectors

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Texture Complexity based Redundant Regions Ranking for Object Proposal 29

Texture Complexity based Redundant Regions Ranking for Objec...

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29th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Ke, Wei Zhang, Tianliang Chen, Jie Wan, Fang Ye, Qixiang Han, Zhenjun Univ Chinese Acad Sci Beijing Peoples R China Univ Oulu CMV SF-90100 Oulu Finland

ISBN: (纸本)9781509014378

Object proposal has been successfully applied in recent visual object detection approaches and shown improved computational efficiency. The purpose of object proposal is to use as few as regions to cover as many as objects. In this paper, we propose a strategy named Texture Complexity based Redundant Regions Ranking (TCR) for object proposal. Our approach first produces rich but redundant regions using a color segmentation approach, i.e. Selective Search. It then uses Texture Complexity (TC) based on complete contour number and Local Binary pattern (LBP) entropy to measure the objectness score of each region. By ranking based on the TC, it is expected that as many as true object regions are preserved, while the number of the regions is significantly reduced. Experimental results on the PASCAL VOC 2007 dataset show that the proposed TCR significantly improves the baseline approach by increasing AUC (area under recall curve) from 0.39 to 0.48. It also outperforms the state-of-the-art with AUC and uses fewer detection proposals to achieve comparable recall rates.

关键词： Local binary pattern

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Assistive Signals for Deep Neural Network Classifiers

Assistive Signals for Deep Neural Network Classifiers

引用

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

作者： Pestana, Camilo Liu, Wei Glance, David Owens, Robyn Mian, Ajmal Univ Western Australia 35 Stirling Hwy Crawley WA 6009 Australia

ISBN: (纸本)9781665448994

Deep Neural Networks are brittle in that small changes in the input can drastically affect their prediction outcome and confidence. Consequently, research in this area mainly focus on adversarial attacks and defenses. In this paper, we take an alternative stance and introduce the concept of Assistive Signals, which are perturbations optimized to improve a model's confidence score regardless if it's under attack or not. We analyze some interesting properties of these assistive perturbations and extend the idea to optimize them in the 3D space simulating different lighting conditions and viewing angles. Experimental evaluations show that the assistive signals generated by our optimization method increase the accuracy and confidence of deep models more than those generated by conventional methods that work in the 2D space. 'Assistive Signals' also illustrate bias of ML models towards certain patterns in real-life objects.

关键词： Deep learning computer vision Three-dimensional displays Perturbation methods conferences Optimization methods Lighting

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Learning to predict crop type from heterogeneous sparse labels using meta-learning

Learning to predict crop type from heterogeneous sparse labe...

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

作者： Tseng, Gabriel Kerner, Hannah Nakalembe, Catherine Becker-Reshef, Inbal NASA Harvest College Pk MD 20742 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665448994

There are many labelled datasets relating to land cover and crop type mapping that cover diverse geographies, agroecologies and land uses. However, these labels are often extremely sparse, particularly in low- and middle-income regions, with as few as tens of examples for certain crop types. This makes it challenging to train supervised machine learning models to detect specific crops in satellite observations of these regions. We investigate the utility of model-agnostic meta-learning (MAML) to learn from diverse global datasets and improve performance in data-sparse regions. We find that in a variety of countries (Togo, Kenya and Brazil) and across a variety of tasks (crop type mapping, crop vs. non-crop mapping), MAML improves performance compared to pretrained and random initial weights. We also investigate the utility of MAML for different target data-size regimes. We find MAML outperforms other methods for a wide range of training set sizes and positive to negative label ratios, indicating its general suitability for land use and crop type mapping.

关键词： Training Geography computer vision Satellites conferences Machine learning Agriculture

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Multi-Scale Selective Residual Learning for Non-Homogeneous Dehazing

Multi-Scale Selective Residual Learning for Non-Homogeneous ...

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

作者： Jo, Eunsung Sim, Jae-Young Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence Ulsan South Korea Ulsan Natl Inst Sci & Technol Dept Elect Engn Ulsan South Korea

ISBN: (纸本)9781665448994

As the particles in hazy medium cause the absorption and scattering of light, the images captured under such environment suffer from quality degradation such as low contrast and color distortion. While numerous single image dehazing methods have been proposed to reconstruct clean images from hazy images, non-homogeneous dehazing has been rarely studied. In this paper, we design an end-to-end network to remove non-homogeneous dense haze. We employ the selective residual blocks to adaptively improve the visibility of resulting images, where the input feature and the residual feature are combined with fully trainable weights. Experimental results including the ablation study demonstrate that the proposed method is a promising tool for non-homogeneous dehazing that enhances the contrast of hazy images effectively while restoring colorful appearance faithfully.

关键词： Degradation computer vision Image color analysis conferences Scattering Tools Distortion

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Towards autonomous navigation of miniature UAV

Towards autonomous navigation of miniature UAV

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Brockers, Roland Humenberger, Martin Weiss, Stephan Matthies, Larry Jet Prop Lab Pasadena CA 91109 USA Austrian Inst Technol Vienna Austria

ISBN: (纸本)9781479943098

Micro air vehicles such as miniature rotorcrafts require high-precision and fast localization updates for their control, but cannot carry large payloads. Therefore, only small and light-weight sensors and processing units can be deployed on such platforms, favoring vision-based solutions that use light weight cameras and run on small embedded computing platforms. In this paper, we propose a navigation framework to provide a small quadrotor UAV with accurate state estimation for high speed control including 6DoF pose and sensor self-calibration. Our method allows very fast deployment without prior calibration procedures literally rendering the vehicle a throw-and-go platform. Additionally, we demonstrate hazard-avoiding autonomous landing to showcase a high-level navigation capability that relies on the low-level pose estimation results and is executed on the same embedded platform. We explain our hardware-specific implementation on a 12g processing unit and show real-world end-to-end results.

关键词： Micro Air Vehicle autonomous landing autonomous navigation embedded computing vision based state estimation

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Detection of Distracted Driver using Convolutional Neural Network 31

Detection of Distracted Driver using Convolutional Neural Ne...

引用

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

作者： Baheti, Bhakti Gajre, Suhas Talbar, Sanjay SGGS Inst Engn & Technol Ctr Excellence Signal & Image Proc Nanded Maharashtra India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Number of road accidents is continuously increasing in last few years worldwide. As per the survey of National Highway Traffic Safety Administrator, nearly one in five motor vehicle crashes are caused by distracted driver. We attempt to develop an accurate and robust system for detecting distracted driver and warn him against it. Motivated by the performance of Convolutional Neural Networks in computer vision, we present a CNN based system that not only detects the distracted driver but also identifies the cause of distraction. VGG-16 architecture is modified for this particular task and various regularization techniques are implied in order to improve the performance. Experimental results show that our system outperforms earlier methods in literature achieving an accuracy of 96.31% and processes 42 images per second on GPU. We also study the effect of dropout, L2 regularization and batch normalisation on the performance of the system. Next, we present a modified version of our architecture that achieves 95.54% classification accuracy with the number of parameters reduced from 140M in original VGG-16 to 15M only.

关键词： Vehicles computer architecture Vehicle crash testing Convolutional neural networks Task analysis Wheels Roads

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Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing

Understanding Knowledge Gaps in Visual Question Answering: I...

引用

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

作者： Bajaj, Goonmeet Bandyopadhyay, Bortik Schmidt, Daniel Maneriker, Pranav Myers, Christopher Parthasarathy, Srinivasan Ohio State Univ OSU Columbus OH 43210 USA Wright State Univ Dayton OH 45435 USA Air Force Res Lab AFRL Wright Patterson AFB OH USA

ISBN: (纸本)9781728193601

Traditional Visual Question Answering (VQA) datasets typically contain questions related to the spatial information of objects, object attributes, or general scene questions. Recently, researchers have recognized the need to improve the balance of such datasets to reduce the system's dependency on memorized linguistic features and statistical biases, while aiming for enhanced visual understanding. However, it is unclear whether any latent patterns exist to quantify and explain these failures. As an initial step towards better quantifying our understanding of the performance of VQA models, we use a taxonomy of Knowledge Gaps (KGs) to tag questions with one or more types of KGs. Each KG describes the reasoning abilities needed to arrive at a resolution, and failure to resolve gaps indicates an absence of the required reasoning ability. After identifying KGs for each question, we examine the skew in the distribution of questions for each KG. We then introduce a targeted question generation model to reduce this skew, which allows us to generate new types of questions for an image.

关键词： computer vision

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VSpSR: Explorable Super-Resolution via Variational Sparse Representation

VSpSR: Explorable Super-Resolution via Variational Sparse Re...

引用

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

作者： Zhou, Hangqi Huang, Chao Gao, Shangqi Zhuang, Xiahai Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (纸本)9781665448994

Super-resolution (SR) is an ill-posed problem, which means that infinitely many high-resolution (HR) images can be degraded to the same low-resolution (LR) image. To study the one-to-many stochastic SR mapping, we implicitly represent the non-local self-similarity of natural images and develop a Variational Sparse framework for Super-Resolution (VSpSR) via neural networks. Since every small patch of a HR image can be well approximated by the sparse representation of atoms in an over-complete dictionary, we design a two-branch module, i.e., VSpM, to explore the SR space. Concretely, one branch of VSpM extracts patch-level basis from the LR input, and the other branch infers pixel-wise variational distributions with respect to the sparse coefficients. By repeatedly sampling coefficients, we could obtain infinite sparse representations, and thus generate diverse HR images. According to the preliminary results of NTIRE 2021 challenge on learning SR space, our team ranks 7-th in terms of released scores.

关键词： Visualization computer vision Dictionaries conferences Superresolution Neural networks Space exploration

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Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner

Training Domain-invariant Object Detector Faster with Featur...

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

作者： Lee, Chaehyeon Seo, Junghoon Jung, Heechul Kyungpook Natl Univ Dept Artificial Intelligence Daegu South Korea SI Analyt Co Ltd Daejeon South Korea SI Analyt Daejeon South Korea

ISBN: (纸本)9781665448994

In deep learning-based object detection on remote sensing domain, nuisance factors, which affect observed variables while not affecting predictor variables, often matters because they cause domain changes. Previously, nuisance disentangled feature transformation (NDFT) was proposed to build domain-invariant feature extractor with with knowledge of nuisance factors. However, NDFT requires enormous time in a training phase, so it has been impractical. In this paper, we introduce our proposed method, A-NDFT, which is an improvement to NDFT. A-NDFT utilizes two acceleration techniques, feature replay and slow learner. Consequently, on a large-scale UAVDT benchmark, it is shown that our framework can reduce the training time of NDFT from 31 hours to 3 hours while still maintaining the performance. The code will be made publicly available online(1).

关键词： Training computer vision Vehicle detection conferences Object detection Detectors Benchmark testing

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