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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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DOTA: A Large-scale Dataset for Object Detection in Aerial Images 31

DOTA: A Large-scale Dataset for Object Detection in Aerial I...

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31st IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xia, Gui-Song Bai, Xiang Ding, Jian Zhu, Zhen Belongie, Serge Luo, Jiebo Datcu, Mihai Pelillo, Marcello Zhang, Liangpei Wuhan Univ Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Cornell Univ Ithaca NY 14853 USA Rochester Univ Rochester NY USA German Aerosp Ctr DLR Cologne Germany Univ Venice Venice Italy

ISBN: (纸本)9781538664209

Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of well-annotated datasets of objects in aerial scenes. To advance object detection research in Earth vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). To this end, we collect 2806 aerial images from different sensors and platforms. Each image is of the size about 4000 x 4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using 15 common object categories. The fully annotated DOTA images contains 188, 282 instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral. To build a baseline for object detection in Earth vision, we evaluate state-ofthe-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth vision applications and are quite challenging.

关键词： Object detection Earth Sports computer vision Sensors Marine vehicles Image sensors

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Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks 31

Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neur...

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31st IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhou, Aojun Yao, Anbang Wang, Kuan Chen, Yurong Intel Labs Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781538664209

Benefiting from tens of millions of hierarchically stacked learnable parameters, Deep Neural Networks (DNNs) have demonstrated overwhelming accuracy on a variety of artificial intelligence tasks. However reversely, the large size of DNN models lays a heavy burden on storage, computation and power consumption, which prohibits their deployments on the embedded and mobile systems. In this paper;we propose Explicit Loss-error-aware Quantization (ELQ), a new method that can train DNN models with very low-bit parameter values such as ternary and binary ones to approximate 32-bit floating-point counterparts without noticeable loss of predication accuracy. Unlike existing methods that usually pose the problem as a straightforward approximation of the layer-wise weights or outputs of the original full precision model (specifically, minimizing the error of the layer-wise weights or inner products of the weights and the inputs between the original and respective quantized models), our ELQ elaborately bridges the loss perturbation from the weight quantization and an incremental quantization strategy to address DNN quantization. Through explicitly regularizing the loss perturbation and the weight approximation error in an incremental way, we show that such a new optimization method is theoretically reasonable and practically effective. As validated with two mainstream convolutional neural network families (i.e., fully convolutional and non-fully convolutional), our ELQ shows better results than state-of-the-art quantization methods on the large scale ImageNet classification dataset. Code will be made publicly available.

关键词： computer vision pattern recognition

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DeSI: Deepfake Source Identifier for Social Media

DeSI: Deepfake Source Identifier for Social Media

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

作者： Narayan, Kartik Agarwal, Harsh Mittal, Surbhi Thakral, Kartik Kundu, Suman Vatsa, Mayank Singh, Richa IIT Jodhpur Karwar Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Social media holds the power to influence a significant change in the population. Through social media, people all around the world can connect and share their views. However, this social space is now infected due to the infiltration of fraudulent, obscene, fake and possibly, influential media. According to a UNESCO report, prevalence of fake news and deepfake content possess the potential of spreading fake propaganda and can lead to political and social unrest. Trust on social media is an emerging problem and there is an urgent need to address the same. There has been some research around approaches that detect fake news and deepfakes, however, identification of the source of these deepfakes posted on social media platforms is an equally important but relatively unexplored challenge. This paper proposes a novel Deepfake Source Identification (DeSI) algorithm that identifies the sources of deepfakes posted on Twitter. The proposed DeSI algorithm allows for two input modalities - text and images. We rigorously test our algorithm in both constrained and unconstrained experimental setups and report the observed results. In the constrained setting, the algorithm correctly identifies all the deepfake tweets as well their sources. The complete framework is further encased in a web portal to facilitate intuitive use and analysis of the results.

关键词： Deepfakes computer vision Social networking (online) Scalability conferences Blogs Sociology

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BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

BCI: Breast Cancer Immunohistochemical Image Generation thro...

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

作者： Liu, Shengjie Zhu, Chuang Xu, Feng Jia, Xinyu Shi, Zhongyue Jin, Mulan Beijing Univ Posts & Telecommun Beijing Peoples R China Capital Med Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels. Based on BCI, as a minor contribution, we further build a pyramid pix2pix image generation method, which achieves better HE to IHC translation results than the other current popular algorithms. Extensive experiments demonstrate that BCI poses new challenges to the existing image translation research. Besides, BCI also opens the door for future pathology studies in HER2 expression evaluation based on the synthesized IHC images. BCI dataset can be downloaded from https://***/BCI.

关键词： Pathology computer vision Image synthesis conferences Breast tissue Epidermis Breast cancer

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Learning Metrics for Diversity in Instance Retrieval 5

Learning Metrics for Diversity in Instance Retrieval

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5th National conference on computer vision, pattern recognition, Image Processing and Graphics (NCVPRIPG)

作者： Rao, Vidyadhar Gupta, Ajitesh Chari, Visesh Jawahar, C. V. IIIT Hyderabad Ctr Visual Informat Technol Hyderabad Andhra Pradesh India

ISBN: (纸本)9781467385640

Instance retrieval (IR) is the problem of retrieving specific instances of a particular object, like a monument, from a collection of images. Currently, the most popular methods for IR use Bag of words (BoW) features for retrieval. However, a prominent problem for IR remains the tendency of BoW based methods to retrieve near-identical images as most relevant results. In this paper, we define diversity in IR as variation of physical properties among most relevant retrieved results for a query image. To achieve this, we propose both an ITML algorithm that re-fashions the BoW feature space into one that appreciates diversity better, and a measure to evaluate diversity in retrieval results for IR applications. Additionally, we also generate 200 hand-labeled images from the Paris dataset, for use in further research in this area. Experiments on the popular Paris dataset show that our method outperforms the standard BoW model in many cases.

关键词： computer vision

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Towards Evaluating and Training Verifiably Robust Neural Networks

Towards Evaluating and Training Verifiably Robust Neural Net...

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

作者： Lyu, Zhaoyang Guo, Minghao Wu, Tong Xu, Guodong Zhang, Kehuan Lin, Dahua SenseTime CUHK Joint Lab Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Ctr Perceptual & Interact Intelligence Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Recent works have shown that interval bound propagation (!BP) can be used to train verifiably robust neural networks. Reseachers observe an intriguing phenomenon on these IBP trained networks: CROWN, a bounding method based on tight linear relaxation, often gives very loose bounds on these networks. We also observe that most neurons become dead during the IBP training process, which could hurt the representation capability of the network. In this paper, we study the relationship between IBP and CROWN, and prove that CROWN is always tighter than !BP when choosing appropriate bounding lines. We further propose a relaxed version of CROWN, linear bound propagation (LBP), that can be used to verify large networks to obtain lower verified errors than IBP We also design a new activation function, parameterized ramp function (ParamRamp), which has more diversity of neuron status than ReLU. We conduct extensive experiments on MNIST, CIFAR-10 and Tiny-ImageNet with ParamRamp activation and achieve state-of-the-art verified robustness. Code is available at https://github com/ZhaoyangLyu/VerifiablyRobustNN.

关键词： Training computer vision Codes Scalability Neurons Robustness pattern recognition

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Self-generated Defocus Blur Detection via Dual Adversarial Discriminators

Self-generated Defocus Blur Detection via Dual Adversarial D...

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

作者： Zhao, Wenda Shang, Cai Lu, Huchuan Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China

ISBN: (纸本)9781665445092

Although existing fully-supervised defocus blur detection (DBD) models significantly improve performance, training such deep models requires abundant pixel-level manual annotation, which is highly time-consuming and error-prone. Addressing this issue, this paper makes an effort to train a deep DBD model without using any pixel-level annotation. The core insight is that a defocus blur region/focused clear area can be arbitrarily pasted to a given realistic full blurred image/full clear image without affecting the judgment of the full blurred image/full clear image. Specifically, we train a generator G in an adversarial manner against dual discriminators D-c and D-b. G learns to produce a DBD mask that generates a composite clear image and a composite blurred image through copying the focused area and unfocused region from corresponding source image to another full clear image and full blurred image. Then, D-c and D-b can not distinguish them from realistic full clear image and full blurred image simultaneously, achieving a self-generated DBD by an implicit manner to define what a defocus blur area is. Besides, we propose a bilateral triplet-excavating constraint to avoid the degenerate problem caused by the case one discriminator defeats the other one. Comprehensive experiments on two widely-used DBD datasets demonstrate the superiority of the proposed approach. Source codes are available at: https://***/shangcail/SG.

关键词： Training computer vision Codes Annotations Manuals Generators pattern recognition

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A Fully Progressive Approach to Single-Image Super-Resolution 31

A Fully Progressive Approach to Single-Image Super-Resolutio...

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

作者： Wang, Yifan Perazzi, Federico McWilliams, Brian Sorkine-Hornung, Alexander Sorkine-Hornung, Olga Schroers, Christopher Swiss Fed Inst Technol Zurich Switzerland Disney Res Zurich Switzerland Oculus Menlo Pk CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case it remains challenging to achieve high quality results for large upsampling factors. To this end, we propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning. To obtain more photorealistic results, we design a generative adversarial network (GAN), named ProGanSR, that follows the same progressive multi-scale design principle. This not only allows to scale well to high upsampling factors (e.g., 8x) but constitutes a principled multi-scale approach that increases the reconstruction quality for all upsampling factors simultaneously. In particular ProSR ranks 2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge [35]. Compared to the top-ranking team, our model is marginally lower, but runs 5 times faster.

关键词： Training Gallium nitride Image resolution Image reconstruction Generative adversarial networks computer architecture Generators

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Picture: A Probabilistic Programming Language for Scene Perception

<i>Picture</i>: A Probabilistic Programming Language for Sce...

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IEEE conference on computer vision and pattern recognition (CVPR)

作者： Kulkarni, Tejas D. Kohli, Pushmeet Tenenbaum, Joshua B. Mansinghka, Vikash MIT Cambridge MA 02139 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781467369640

Recent progress on probabilistic modeling and statistical learning, coupled with the availability of large training datasets, has led to remarkable progress in computer vision. Generative probabilistic models, or "analysis-by-synthesis" approaches, can capture rich scene structure but have been less widely applied than their discriminative counterparts, as they often require considerable problem-specific engineering in modeling and inference, and inference is typically seen as requiring slow, hypothesize-and-test Monte Carlo methods. Here we present Picture, a probabilistic programming language for scene understanding that allows researchers to express complex generative vision models, while automatically solving them using fast general-purpose inference machinery. Picture provides a stochastic scene language that can express generative models for arbitrary 2D/3D scenes, as well as a hierarchy of representation layers for comparing scene hypotheses with observed images by matching not simply pixels, but also more abstract features (e.g., contours, deep neural network activations). Inference can flexibly integrate advanced Monte Carlo strategies with fast bottom-up data-driven methods. Thus both representations and inference strategies can build directly on progress in discriminatively trained systems to make generative vision more robust and efficient. We use Picture to write programs for 3D face analysis, 3D human pose estimation, and 3D object reconstruction - each competitive with specially engineered baselines.

关键词： picture computer Programming Languages Probabilistic Model Statistical learning Programming image matching Monte Carlo technique Social Hierarchy

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Modulated Contrast for Versatile Image Synthesis

Modulated Contrast for Versatile Image Synthesis

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

作者： Zhan, Fangneng Zhang, Jiahui Yu, Yingchen Wu, Rongliang Lu, Shijian Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Perceiving the similarity between images has been a long-standing and fundamental problem underlying various visual generation tasks. Predominant approaches measure the inter-image distance by computing pointwise absolute deviations, which tends to estimate the median of instance distributions and leads to blurs and artifacts in the generated images. This paper presents MoNCE, a versatile metric that introduces image contrast to learn a calibrated metric for the perception of multifaceted inter-image distances. Unlike vanilla contrast which indiscriminately pushes negative samples from the anchor regardless of their similarity, we propose to re-weight the pushing force of negative samples adaptively according to their similarity to the anchor, which facilitates the contrastive learning from informative negative samples. Since multiple patch-level contrastive objectives are involved in image distance measurement, we introduce optimal transport in MoNCE to modulate the pushing force of negative samples collaboratively across multiple contrastive objectives. Extensive experiments over multiple image translation tasks show that the proposed MoNCE outperforms various prevailing metrics substantially. The code is available at MoNCE.

关键词： Photography Visualization computer vision Codes Image synthesis Force Performance gain

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