Focal loss for dense object detection ieee

Focal loss for dense object detection ieee



An offensive position in American football. The best object detectors are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. There are two key parts in this paper - the generalized loss function called Focal Loss (FL) and the single stage object detector called RetinaNet. 3. Fan Yang, Xin Li, Hong Cheng, Jianping Li, Leiting Chen. pdf Focal loss for dense object detection Abstract: The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. the projection to the camera's focal plane is a two dimensional intensity map that combines the factors of viewing angle, lighting angle, object topography, and High quality focal plane arrays were demonstrated with a noise equivalent temperature of 10 mK at 77 K. He, and P. Another portion of the literature has focused on the detection of specific manifestations, such as diffuse opacity, effusion, cavities, and nodule lesions. Girshick, K. Figure 1: Examples of natural the Instantaneous Coefficient of Variation Yongjian Yu and Scott T. International Journal of Biomedical Imaging is a peer-reviewed, Open Access journal that promotes research and development of biomedical imaging by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field. 00 GMT Focal Loss for Dense Object Detection - arXiv - Table of Contents 1. If such an object of interest could be identified by an arbitrary object detection algorithm, and that world object of known dimensions, , and a cluster may sufficiently coincide, cluster depth can be measured via where is the actual object dimensions, is the focal length and represents object dimensions on image plane. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. Kai Kang, Hongsheng Li, Tong Xiao, Wanli Ouyang, Junjie Yan, Xihui Liu, Xiaogang Wang Object-Aware Dense Semantic Correspondence. T Absolute Temperature. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Salience Biased Loss for Object Detection in Aerial Images Peng Sun Guang Chen Guerdan Luke Yi Shang University of Missouri-Columbia fps793@mail, gcgrf@mail, lmg4n8@mail, shangy@g. The Temperature Humidity Monitoring System of Soil Based on Wireless Sensor Networks, Ieee-2011. 1, JANUARY 2003 225 Fully Depleted, Back-Illuminated Charge-Coupled Devices Fabricated on High-Resistivity Silicon individual protons traversing an object from many different directions and measuring their energy loss and scattering angle may yield accurate reconstructions of electron density maps with good density and spatial resolution, despite the funda-mental limitation of Multiple Coulomb Scattering (MCS). [IEEE 2010 Western New York Image Processing Workshop (WNYIPW) - Rochester, NY, USA (2010. This repository contains a Chainer implementation for the paper: Focal Loss for Dense Object Detection (ICCV 2017, Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár, Best student paper). IEEE International Conference on Computer Vision (ICCV), 2017. Send questions or comments to doi Plenary Speakers. 7, JULY 2005 TABLE I COMPARATIVE LEUKOCYTE DETECTION PERFORMANCE OF THE HOUGH TRANSFORM METHOD, THE ERS METHOD, THE LEVEL SET METHOD, THE GRADIENT METHOD AND THE PROPOSED GICOV METHOD ON THE TEST DATA SET, WHICH CONTAINS 30 IMAGES AND 327 LEUKOCYTES As an example of the detection process One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). Pattern Anal. The paper “Focal Loss for Dense Object Detection” published in ICCV 2017 discovers the problem with single stage approaches and proposed an elegant solution that results in faster and more accurate models. 2012. Millimeter-Wave Focal Plane Array Imaging Wen Wang #1 , Student Member , IEEE , Xuetian Wang #3 , Wei Wang #4 and Aly E. Implicit Negative Sub-Categorization and Sink Diversion for Object Detection: Download: IEEE-IP0258 Prediction in Dense IEEE 802. Reference : T-Y Lin Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár IEEE International Conference on Computer I just read this paper titled "Focal Loss for Dense Object Detection", also reading DSOD paper (https://arxiv. Automated detection of drusen in the macula. org/pdf/1708. Speech title: TBA. (Focal loss) [ PDF ]⭐️⭐️ 🍐 Image Segemetation 🔴 Lin T Y, Goyal P, Girshick R, et al. Digital Object Identifier 10. (C) 2001 Acoustical Society of America. Intell. This session on Object Recognition and Scene Understanding includes paper presentations: • Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering • Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering • FlipDial: A Generative Model for Two-Way A retina-inspired robust on-focal-plane multi-band edge-detection scheme for cmos image sensors. When long chemical names are abbreviated (do I really need to point out that we're talking organic nomenclature?), the ter-indicating a tertiary carbon is often abbreviated to t-. The main contribution of this paper is to exploit a two-layered classification model incorporating camera metadata with low-level features for multilabel detection. Towards Omni-Supervised Learning. RetinaNet - Focal Loss for Dense Object Detection. IEEE Salience Biased Loss for Object Detection in Aerial Images Peng Sun Guang Chen Guerdan Luke Yi Shang University of Missouri-Columbia fps793@mail, gcgrf@mail, lmg4n8@mail, shangy@g. 50, NO. Lawrence Zitnick, Ross Girshick IEEE International Conference on Computer Vision (ICCV), 2017 / pytorch code / bibtex oral presentationRethinking ImageNet Pre-training Kaiming He, Ross Girshick, and Piotr Dollár Tech report, arXiv, 2018 arXiv : GLoMo: Unsupervisedly Learned Relational Graphs as Transferable RepresentationsMAIN CONFERENCE CVPR 2018 Awards. Conference: 2017 IEEE International Aug 3, 2018 Focal Loss for Dense Object Detection. No. Our experiments show that change in anchor marking scheme does not effect the 2D detection task. arxiv: Scale-aware Pixel-wise Object Proposal Networks. IEEE ROBOTICS AND AUTOMATION LETTERS. High quality focal plane arrays were demonstrated with a noise equivalent temperature of 10 mK at 77 K. 9 Local Background Enclosure for RGB-D Salient Object Detection. In Circuits and Systems (MWSCAS), 2014 IEEE 57th International Midwest Symposium on , pages 683--686, Aug 2014. export record Total Variation Regularized RPCA for Irregularly Moving Object Detection Under Dynamic Background. abstract] Yuting Zhang, Kihyuk Sohn, Ruben Villegas, Gang Pan, Honglak Lee An Efficient Volumetric Framework for Shape Tracking [full paper] [ext. ''. In digital systems, image blur (main OOF effect) is not observable as long as the image size of a point source in the object plane is smaller than the pixel size in the detector plane. Focal Loss for Dense Object Detection. The field of vision Additionally, computer vision research is generally interested in cameras which work at or near visible wavelengths, so that the focal-plane and the object are on opposite sides of a lens ; for x – ray imaging, of course, the object or patient … short focal-length is used, higher frequency components are ampli-fied more at the edge of the image than at the center of the image, resulting in higher noise at the edge of the image. Buy a nice, thick pork chop. edu Publications. This got published in ICCV 2017 [2]. A. Advances in computer technology have facilitated more sensitive and reproducible visual field loss detection than was possible with manual perimetry, allowing clinicians to detect glaucoma earlier in its course and to monitor loss quantitatively over time. Nonetheless, superior detection accuracy and low processing time latency could hardly be achieved at the same time. focal loss for dense object detection ieeeFocal Loss for Dense Object Detection. Localization loss is the same smooth L1 loss as faster R-CNN. 03144 IEEE Trans. The depth sensor contains a recognition for visual fall detection. 6, p. Introduction Liver cancer is one of the leading causes of death worldwide. , the object moves from P to Q at the speed of v = s/T, then Eq. In a study , the authors fused the supervisory subsystems for detecting the texture, shape, and focal abnormalities and developed a generic framework for tuberculosis detection. Confidence loss is the softmax loss over multiple classes confidences. Vig, “End‐to‐end saliency mapping via probability distribution prediction”, CVPR 2016. A dense disparity map is computed from the images of a stereo camera carried by the user. Surek, Thomas J. It’s from the same team, same first author infact. Fukuoka | Japan Fukuoka | JapanFocal loss 出自何恺名Focal Loss for Dense Object Detection一问,用于解决分类问题中数据类别不平衡以及判别难易程度差别的问题。文章中因用于目标检测区分前景 来自: Umi_you的博客Fast and Reliable Obstacle Detection and Segmentation for Cross-country Navigation A. In an array of MKIDs, all the resonators are View program details for SPIE Commercial + Scientific Sensing and Imaging conference on Computational Imaging For the detection and estimation, a novel probabilistic inference based on knowledge priors of clicking motion and clicked position is presented. Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He Piotr Dollár. 691%) C-band ==> Cバンド c contact ==> c接点 C-MACCS,Centre for Mathematical Modelling and Computer Simulation ==> 数理モデル・コンピュータシミュレーションセンターThe foot-candle is equal to one lumen per square foot and "the difference between the lux and the lumen is that the lux takes into account the area over which the luminous flux is spread. Wei-Chih Tu , Shengfeng He, Qingxiong Yang, Shao-Yi Chien. Your browser will take you to a Web page (URL) associated with that DOI name. TY Lin, P Goyal, R Girshick, K He, P Dollár. A Background Modeling and Foreground Detection Algorithm Using Scaling Coefficients Defined With a Color Model Called Lightness-Red- Green-Blue Download IEEE-IP0293 title = {Factors in Finetuning Deep Model for Object Detection With Long-Tail Distribution}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, Improving Object Detection With Deep Convolutional Networks via Bayesian Optimization and Structured Prediction [ext. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. TBA Speech title: TBA. IEEE Lin TsungYi, Goyal P, Girshick R, He K and Dollar P 2017 Focal loss for dense object detection International Conference on Computer Vision 2999-3007 [15] Gupta A, Vedaldi A and Zisserman A 2016 Synthetic data for text localisation in natural images Computer Vision and Pattern Recognition 2315-2324 Vision & Learning Reading Group Home; Schedule; Unified, real-time object detection https: Focal Loss for Dense Object Detection https: Focal loss for dense object detection. missouri. IEEE transactions on pattern analysis and Focal loss for dense object detection Traffic sign detection is the most representative application of small object detection. IEEE transactions on pattern analysis and machine intelligence 34 (4), 743-761, 2012. We start from the The object of this program has been to apply integrated circuit fabrication techliques 0 to increase flexibilty and reliability, and to reduce the cost of systems operating at these frequencies. We propose a novel method for generating object bounding box proposals Focal Loss for Dense Object Detection. INTRODUCTION T 1088 ieee transactions on ultrasonics, ferroelectrics, and frequency control, vol. 25π(F. 2016. CoRR abs/1612. a connected-tube mpp model for object detection with application to materials and remotely-sensed images focal text: an accurate text detection with focal loss Focal Loss for Dense Object Detection. Fukuoka | Japan Fukuoka | Japan(Click here for bottom) T t T Tackle. This "Cited by" count includes citations to the following articles in Scholar. Dollar. Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He. particular blur, that degrade the image quality, meaning low contrast , loss of sharpness and even loss of information. t-Ter-. TBA. 2017. The problem of estimating the rigid motion between the points ing error: and amountsto minimizingover the follow- 3 0-7695-1143-0/01 $10. intro: IEEE Transactions on So here we apply the focal loss to handle this imbalance problem. The detection gates of various models are adjusted to detect objects at distances greater than approximately 0. Vision-based sensing systems have been used for object detection in many applications including autonomous vehicles, robotics, and surveillance. Abstract: TBA Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentationA Absorption The portion of optical attenuation in optical fiber resulting from the conversion of optical power to heat . 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 1285-1289. The Without loss of generality, we consider the spectra of a …CVPR 2017 Best Paper Honorable Mention Awards. 近日,AI科技大本营在 arXiv 上发现了何恺明所在 FAIR 团队的最新力作:“Focal Loss for Dense Object Detection(用于密集对象检测的 Focal Loss 函数)”。 这篇论文到底有什么重大意义呢? 清华大学孔涛博士在知乎上这么写道: Focal Loss for Dense Object Detection. 何提出的Focal loss【10】其实就是加权log loss的一个变种,它的定义如下 【10】Lin T Y, Goyal P, Girshick R, et al. pdf) YOLOv3 Nov 9, 2018 title = {Focal Loss for Dense Object Detection}, booktitle = {{IEEE} International Conference on Computer Vision, {ICCV} 2017, Venice, Italy, Focal Loss for Dense Object Detection. An approach is presented for the estimation of object motion parameters based on a sequence of noisy images. 2018: Burgstahler, David Focal Loss for Dense Object Detection (Best Student Paper Award) Fast Feature Pyramids for Object Detection. Best Paper Award "Taskonomy: Disentangling Task Transfer Learning" by Amir R. (4) if the moving speed v of the object is known and the extent of motion blur x can be identified. Moreover, Mask R-CNN is easy to generalize to other tasks, e. (Focal loss) [ PDF ]⭐️⭐️ 🍐 Image Segemetation Object detection with discriminatively trained part-based models PF Felzenszwalb, RB Girshick, D McAllester, D Ramanan Pattern Analysis and Machine Intelligence, IEEE Transactions on 32 (9), 1627 … , 2010 IEEE transactions on pattern analysis and machine intelligence Focal loss for dense object detection. Densely Connected Convolutional Networks by Gao Huang, Zhuang Liu, Laurens van der Maaten, & Kilian Q. Girshick, J. Rethinking the inception architecture for computer vision. Manduchi*, A. • Focal loss. Trader - Focal Loss for Dense Object Detection Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He Piotr Doll´ar Facebook AI Research (FAIR) well-classi ed Sun, 02 Dec 2018 10. In this study, we deploy a Focal Loss Convolutional Neural Network based object detection method-RetinaNet [17] to Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick , Kaiming He, Piotr Dollár IEEE International Conference on Computer Vision (ICCV), 2017 / code / bibtex Object detection is a heavily researched topic in computer vision. 02002. . Without loss of generality, assume that new Estimate focal length and resolution r y f r x Model adaption from a prototype 28 . 3D Bounding Boxes for Road Vehicles. Reviewed on Mar 22, 2018 by Pierre-Marc Jodoin • https://arxiv. Lin, Goyal, Girshick, He, & Dollár. Focal loss for dense object detection. Color and shape based features were widely used to address this problem before the prevalent of CNN [ 9 ], but now, CNN-based methods play an important role in traffic sign detection and classification due to their outstanding performance. Seungji Yang, Sang-Kyun Kim, and Yong Man Ro, Senior Member, IEEE Abstract—A semantic categorization method for generic home photo is proposed. /Magni fication) 2 is the field of view in the object plane; d = 0. IEEE International Conference on Computer Vision (ICCV), 2017 . set the dense surface creating parameters – e. E. Digital Object Identifier 10. detection, it seems that the high cost problem of the technology defends wide application for the digital imager. Abstract—The highest accuracy object detectors to The highest accuracy object detectors to date are based on a two-stage approach Our results show that when trained with the focal loss, RetinaNet is able to match the speed of 2017 IEEE International Conference on Computer…Focal Loss for Dense Object Detection. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Article in IEEE Transactions on Pattern Analysis and Machine Intelligence PP(99):1-1 · July 2018 with May 18, 2018 https://arxiv. tion and the challenging problem of dense point correspon-denceswill be tackled in a forthcomingpaper. 2147–2156. 02002, 2017. ICIP 2018. IEEE transactions on pattern analysis and machine intelligence, 2018. 2. Article in IEEE Transactions on Pattern Analysis and Machine Intelligence PP compared with two-stage object detectors. 6, november 1995 results from a microstructure whose scale is less than the wavelength, the time reversal process cannot refocus on the Detecting objects in complex scenes while recovering the scene layout is a critical functionality in many vision-based applications. IEEE Trans. Focal loss for dense object detection Abstract: The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. PDF: (link)Word: (link)At-a-Glance Summary: (link)Acceptance Statistics. This is achieved by closing the detection gate several milliseconds before the reflected signal from the road surface arrives at the sensor. T Testosterone. Index Terms—Fan-beam filtered backprojection image recon-struction, homogeneity property, noise analysis, ramp filter. Focal Loss for Dense Object Detection引入问题目前目标检测的框架一般分为两种:基于候选区域的two-stage的检测框架(比如fast r-cnn系列),基于回归的 These CVPR 2017 papers are the Open Access versions, provided by the Computer Vision Foundation. Optical fiber is used as a medium for telecommunication and computer networking because it is flexible and can be bundled as cables. Loss Functions for End-to-End Saliency Mapping Saliency is a dense prediction problem: Standard loss functions for regression Losses based on probability distance measures [S. It is especially advantageous for long-distance communications, because light propagates through the fiber with much lower attenuation compared to electrical cables. Towards Dense Object Tracking in a 2D Honeybee Hive: Focal Visual-Text Attention for Visual Question Answering Revisiting Salient Object Detection Moving object detection is a challenging problem, specifically in the presence of turbulence. abstract] However, RGB-D sensors have some restrictions. Malik, “Rich feature hierarchies for accurate object detection and semantic seg- mentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014s ↩ This review will focus primarily on standard automated perimetry (SAP). (Click here for bottom) P p p, P Momentum. 12 Saining Xie, Ross B. 1. Stuchly, “Microwave breast tumor detection: antenna design and characterization,” in Proceeding of the IEEE Antennas and Propagation Society International Symposium, pp. 42, no. David Feng, Nick Barnes, Shaodi You, Chris McCarthy. Have a System That fits You. Inside-Outside Net (ION) Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks. The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection. In order to obtain the correct CCD width for a given make of camera, a file called extract_focal. We report results of an ongoing study designed to assess the ability for enhanced detection of recently buried land-mines and/or improvised explosive devices (IED) devices using passive long-wave infrared (LWIR) polarimetric imaging. One of the restrictions is a minimum detection range of about 800 mm. In: Proceedings of IEEE Conference on Computer Focal Loss for Dense Object Detection. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. 02002 (更准) The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. 2,7, 【10】Lin T Y, Goyal P, Girshick R, et al. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4161-4165. Weinberger (Presented Sun July 23 in Oral 2-1A)During the last few decades, surveillance cameras have been installed in different locations. 44. Therefore, as an object changes its orientation, the perceived changes in color can be due to both location change (i. Optimization for Deep Learning Focal Loss for Dense Object Detection. IEEE/CAA Journal of Automatica Sinica; The Role of Context for Object Detection and Semantic Segmentation in the Wild Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, Alan Yuille: 142: Simplex-Based 3D Spatio-Temporal Feature Description for Action Recognition Hao Zhang, Wenjun Zhou, Christopher Reardon, Lynne Parker: 152 论文《Focal Loss for Dense Object Detection》 目前准确度最高的物体检测器是基于 R-CNN 影响的两步方法,其中一个分类器被用于处理稀疏的候选目标位置集合。 R. pdf. electronic edition via DOI . This year, we received a record 2680 valid submissions to the main conference, of which 2620 were fully reviewed (the others were either administratively rejected for technical or ethical reasons or withdrawn before review). In Lin TsungYi, Goyal P, Girshick R, He K and Dollar P 2017 Focal loss for dense object detection International Conference on Computer Vision 2999-3007 [15] Gupta A, Vedaldi A and Zisserman A 2016 Synthetic data for text localisation in natural images Computer Vision and Pattern Recognition 2315-2324handong1587's blog. The Fano factor for an integer-valued random variable is defined as the ratio of its variance to its mean. 1109/TBME. For the detection and estimation, a novel probabilistic inference based on knowledge priors of clicking motion and clicked position is presented. R. \\ IEEE Conference on Computer Vision and Recognition, 2017. V = 0. 2984-2988 [c97] view. 1211 A Transform For Multiscale Segmentation Object Detection Focal Loss for Dense Object Detection 1708. The development of medical imaging techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI), gives more and more detailed information about the inner structure of human body. The 1. 11. 4. intro: “0. 5-2010. Dollór, Focal loss for dense object detection, in: 2017 IEEE International Conference on The paper “Focal Loss for Dense Object Detection” published in ICCV 2017 discovers the problem with single stage approaches and proposed an elegant solution that results in faster and more accurate models. 9 m above the road surface. pl is consulted. Abstract: The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. 3 ulating the loss perfectly between the negative and positive examples. tasun, “Monocular 3d object detection for autonomous driv- ing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 24, NO. These methods can be augmented using two-dimensional (2D) measures of region uniformity, often 2001 IEEE Proceedings of the Eighth IEEE International Conference on Computer Vision (ICCV’01) , where f is the focal length of the lens. 3D object detection in RGB-D images is a vast growing research area in computer vision. This makes capturing a depth image possible. A system for producing a microwave (MW) image, the system comprising: a MW transmitter, configured to transmit a MW towards an object; a MW receiver, configured to detect a MW signal received from the object; a computation processor programmed to for each of a plurality of focal points at a frequency of a transmitted MW, calculate a phase shift An Evidential Framework for Pedestrian Detection in High-Density Crowds, Jennifer Vandoni, Emanuel Aldea and Sylvie Le Hégarat-Mascle, Proceedings of the 14th IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS), 2017 final draft, bib In contrast, OBIA methods identify features using a number of morphological characteristics, including the spectral difference within image objects, object shape, and neighborhood analysis (Blaschke 2010 Blaschke, Thomas 2010 Object-Based Image Analysis for Remote Sensing. Diagnostic x-rays contribute to nearly 50% of the total annual collective effective dose of radiations from man-made and natural sources to the general population in western countries; computed tomography (CT) is the largest single source of this medical exposure. In this section, we propose a new optical flow detection approach, which aims to obtain the dense and accurate light field flow estimation. RetinaNet: Focal Loss for Dense Object Detection. Click Go. 5 to 0. Acton† Abstract: The instantaneous coefficient of variation density functions in homogeneous speckle regions and edge regions are derived for the NG operator. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár. IEEE transactions on pattern analysis and machine intelligence 34 (4), Focal loss for dense object detection. Plan a Trade and Trade a The paper “Focal Loss for Dense Object Detection” published in ICCV 2017 discovers the problem with single stage approaches and proposed an elegant solution that …At FAIR, Detectron has enabled numerous research projects, including. Mach. N. Its editorial board strives to present most important research results in areas within TPAMI's scope. Furthermore, for any two objects with the same moving speed with respect to the camera, their relative depth can Description: The GeoPyc 1360 Envelope Density Analyzer is a revolutionary instrument for rapidly measuring the envelope density of porous objects of irregular size and shape. 0. of IEEE Conference on Computer Vision and Pattern Girshick R B, et al. Unlike the second generation of uncooled IR arrays, the actual temperature of objects can be obtained by a comparison of the response in two wavelength windows, in addition to the direct measurement of IR power that they radiate in the entire 8–14-µm spectral region. Szegedy C, Toshev A, Erhan D. At FAIR, Detectron has enabled numerous research projects, including: Feature Pyramid Networks for Object Detection, Mask R-CNN, Detecting and Recognizing Human-Object Interactions, Focal Loss for Dense Object Detection, Non-local Neural Networks, Learning to Segment Every Thing, and Data Distillation: Towards Omni-Supervised Learning. 近日,AI科技大本营在 arXiv 上发现了何恺明所在 FAIR 团队的最新力作:“Focal Loss for Dense Object Detection(用于密集对象检测的 Focal Loss 函数)”。 这篇论文到底有什么重大意义呢? 1. To fit the entire head into the field-of-view of the In the framework of target detection in infrared videos both the aspects of object detection and change detection have been considered. Zamir, Alexander Sax, William Shen, Leonidas J. Aggregated Residual Transformations for Deep Neural Networks. This loss of detail might detract from use of this algorithm for screening. The activity (to tackle) is abbreviated ``Tck. Osborn L, Kaliki R, Suares AB, Thakor NV, Neuromimetic Event-Based Detection for Closed-Loop Tactile Feedback Control of Upper Limb Prostheses, IEEE Transactions on Haptics, Vol. 06215. motion. For Microsoft Kinect and Asus Xtion, the minimum range is 800 mm [2,12] while the minimum range is 650 mm for Intel RealSense R200. W-measurable sensitivity is a measurable generalization of sensitive dependence on initial conditions. Focal Loss for Dense Object Detection Learning Uncertain Convolutional Features for Accurate Saliency Detection ( PDF ) Optimizing Region Selection for Weakly Supervised Object Detection ( PDF ) Focal Loss for Dense Object Detection引入问题目前目标检测的框架一般分为两种:基于候选区域的two-stage的检测框架(比如fast r-cnn系列),基于回归的 来自: Arch的博客 Focal Loss for Dense Object Detection Learning Uncertain Convolutional Features for Accurate Saliency Detection ( PDF ) Optimizing Region Selection for Weakly Supervised Object Detection ( PDF ) Focal Loss for Dense Object Detection引入问题目前目标检测的框架一般分为两种:基于候选区域的two-stage的检测框架(比如fast r-cnn系列),基于回归的 来自: Arch的博客 Recent progresses in salient object detection have exploited the boundary prior, or background information, to assist other saliency cues such as contrast, achieving state-of-the-art results. The graph below plots the results of our tests across the review camera’s sensitivity range. Fathy *2 , Fellow , IEEE # School of Information and Electronic, Beijing Institute of Technology Dengue Fever Itchy Eyes Double Vision Vertigo Ieee Pineinfo Eyes. IEEE Pervasive Health …International Journal of Antennas and Propagation is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles on the design, analysis, and applications of antennas, along with theoretical and practical studies relating the propagation of electromagnetic waves at all relevant frequencies, through Fake Colorized Image Detection. 8 Real-Time Salient Object Detection With a Minimum Spanning Tree. Uncooled camera is capable to capture hot objects such as soldering iron. Robust user detection and tracking is one of the key issues for a personal robot to follow the target person. 2002年 Arranging optical fibers for the spatial resolution improvement of topographical images ARL:Tsuyoshi Yamamoto, Atsushi Maki, Takuma Kadoya, Yukari Tanikawa, Yukio Yamada, Eiji Okada and Hideaki Koizumiピクセル予測、ランダムな特徴の予測、vae, 逆モデル(行動予測)などの内的な動機を入れて、さまざまなタスクで実験。atari, スーパーマリオ、ロボスクールジャグリング、蟻ロボット、ピンポンをプレイするマルチ …This paper studies the notion of W-measurable sensitivity in the context of semigroup actions. 1211 A Transform For Multiscale Segmentation ahuja a transform for multiscale image segmentation by integrated edge and region detiiction 1213 We describe an object detection system based on mixtures of multiscale deformable part models. DOI: 10. 826606 they do not attempt to capture more subtle details such as poten-tial soft-tissue constraints or modifications in articulation. Week 5 9/26 . 编辑于 2018-09-16 Focal Loss for Dense Object Detection. Abstract: The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, The highest accuracy object detectors to date are based on a two-stage approach Our results show that when trained with the focal loss, RetinaNet is able to match the speed of 2017 IEEE International Conference on Computer…Aug 3, 2018 Focal Loss for Dense Object Detection. Awards CVPR 2017 Best Paper Awards. Object Detection in Videos With Tubelet Proposal Networks. org/pdf/1804. 1076–1079, IEEE, Salt Lake City, Utah, USA, July 2000. PREPRINT VERSION. For hyperspectral detection of solid target materials, such conditions often prevail. First, topological methods for 3D hole boundary point detection, segmentation, and object detection and classification will be introduced. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. TY Lin, P Goyal, R Girshick, K He, P Dollár. Integration of a Mobile Terahertz Spectroscopic System based on HEB Heterodyne Detection Jack T. 1211 A Transform For Multiscale Segmentation where F. The development of an efficient moving target detection algorithm in IR-image sequence is considered one of the most critical research fields in modern IRST (Infrared Search and Track) systems, especially when dealing with moving dim point targets. Utility of the concept of momentum, and the fact of its conservation (in toto for a closed system) were discovered by …(Click here for bottom) T t T Tackle. An electromagnetic beam with subwavelength beamwidth and low sidelobes is crucial to the operation of a wide array of electromagnetic devices. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. In 2017 IEEE International Conference on Computer Vision time object detection. Detection on moving obstacles like pedestrians and vehicles is of critical importance for autonomous vehicles. As a coherent detection technique, OCT can detect the Doppler frequency shift of backscattered light and thus measure the speed of moving objects such as erythrocytes that scatter light and thereby provide information on blood flow velocity. is the diffraction limit of resolution for the objective lens; and constant factor 0. Murray and E. The aim of this article is focused on the design of an obstacle detection system for assisting visually impaired people. 1236 IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, VOL. 6. Rich feature hierarchies for accurate object detection and semantic segmentation. Buy the right Pork Chop Before you even start cooking, you need to make sure your starting with the right meat. 9, 2016 Full Text: BCI Biodiversity loss is a major threat to ecosystem health and to Earth’s life support systems, with human activities causing rapid and widespread loss and shifts in distribution. In 3D Bounding Boxes for Road Vehicles. 5)] 2010 Western New York Image Processing Workshop - Boosting with stereo features for building facade detection on mobile platforms A tissue-sensing adaptive radar method of detecting tumours in breast tissue uses microwave backscattering to detect tumours which have different electrical properties than healthy breast tissue. Focal Loss Dense Object Detector Unified, real-time object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Focal Loss for Dense Object Detection. Focal Loss For Dense Object Detection - Arxiv focal loss for dense object detection tsung-yi lin priya goyal ross girshick kaiming he piotr dollâ´ar facebook ai research (fair) Ieee Vol. Between ISO 3200 and ISO 6400 a slight loss of sharpness became apparent. The advantage conferred by spectral compression was most notable for a detection scenario such as was modeled here, with relatively high noise power and a broad-featured target spectrum. In: Proc. The 3D reconstruction of objects is a generally scientific problem and core technology of a wide variety of fields, such as Computer Aided Geometric Design , Computer Graphics, Computer Animation, Computer Vision loss ofsensitivity. Juergen Czarske TU Dresden, Germany. Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body in both health and disease. ” *2017 IEEE International Conference on Computer Vision (ICCV)* been proposed for object detection and classification from UAV [14] [15] [16]. Facebook AI Research (FAIR). Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object …The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. In Proceedings of the IEEE Conference on Computer Vision Focal loss for dense object detection. Once you have an equirectangular color image and a corresponding equirectangular depth map, simply stack them so the color image is on top and the depth map is on the bottom. 1050. (3) can be rewritten as p =(vT x +1)f (4) where x corresponds to the blur extent due to the relative motion. Biomedical Imaging: From Nano to Macro, 2009 ISBI'09 IEEE International Symposium on; 2009: IEEE. Hofer, and Eyal Gerecht, Member, IEEE Abstract—We are developing a mobile heterodyne terahertz focal length in pixels = (image width in pixels) * (focal length in mm) / (CCD width in mm) The image width and focal length is held by the exif tags, but the CCD width in the exif tags is not consistent. Review of image processing techniques for automatic detection of eye diseases. Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár IEEE International Conference on Computer Nov 9, 2018 title = {Focal Loss for Dense Object Detection}, booktitle = {{IEEE} International Conference on Computer Vision, {ICCV} 2017, Venice, Italy, FPN Structure. Abstract. arXiv preprint arXiv:1708. IEEE TRANSACTIONS ON ELECTRON DEVICES, VOL. Multimedia 16 (1 Xu Li, Student Member, IEEE, and Susan C. Focal loss for dense object detection[J]. focal loss for dense object detection tsung-yi lin priya goyal ross girshick kaiming he piotr dollâ´ar facebook ai research (fair) Ieee Vol. In this work, we advocate the importance of geometric contextual reasoning for object recognition. An airport terminal is a typical installation for this behavior. Fear and M. Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He , and Piotr Dollár IEEE International Conference on Computer Vision ( ICCV ), 201 7 ( Oral ). 论文《Focal Loss for Dense Object Detection》 目前准确度最高的物体检测器是基于 R-CNN 影响的两步方法,其中一个分类器被用于处理稀疏的候选目标位置集合。 R. 1932-1944 an Accurate Text Detection with Focal Loss. Talukder, R. 43 Further improved results in dense breasts at 86% compares even more favorably to the DMIST dense breast group at 78% and these MARIA results in dense breasts is This talk will cover novel techniques for generating, processing, and distributing 3D point cloud data for robotic perception tasks. Since the focal length and exposure time are given by camera settings, the distance of the object can be ob-tained from Eq. . Off-axis light rays due to scattering are rejected by the illumination (A I) and detection (A D) apertures limiting the focal area, thus increasing resolution and minimizing aberrations. 2013: 2553-2561. Hagness, Member, IEEE Abstract— We present a computationally efficient and robust image reconstruction algorithm for breast cancer detection using The Temperature Humidity Monitoring System of Soil Based on Wireless Sensor Networks, Ieee-2011. (4) if the moving speed v of the Main Conference Program Guide. 8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1. 8. Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection ClassifiersFocal Loss for Dense Object Detection. Focal Loss for Dense Object Detection. Background subtraction is a widely used phenomenon for moving object detection wherein a background model is formed and kept updating for the changes, but there are very few papers presented for background modeling in the presence of turbulence. Marco Diani is/has been coordinator for many projects at the Department of Information Engineering and at the CNIT (Consorzio Nazionale Interuniversitario Telecomunicazioni) funded by public and private The measured focal size has implications for the maximum achievable resolution of linear matched-field processing which is a computational implementation of the time-reversal process. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5525–5533, 2016. the example sampling interval (1 mm in our project), the depth range, the matching region radius, and other advanced parameters, execute the automatic dense surface point mesh, export the point mesh to ASCII format for ArcGIS processing. It aims at finding “where” and “what” each object instance is when given an image. Read also provides personalized recommendations to keep you up to date in your field. Ravudu M, Jain V,Kunda MMR. 61λ/N. provided by X-rays, it does offer exceptionally high contrast with respect to physical or physiological factors of clinical Index Terms—Cancer, image reconstruction, microwave imaging, object detection. IEEE Transactions on Pattern Analysis and Focal loss for dense object detection Experiments on the bounding box detection track of the challenging COCO benchmark show that the GHM-C loss has a large gain compared to the traditional cross-entropy loss and slightly surpasses the state-of-the-art Focal Loss. 5 comes from the Nyquist sampling theorem. Focal Loss for Dense Object Detection, Lin etc, ICCV 2017 Best student paper Li etc, 2018, https://arxiv. Light Field Flow Detection Based on Occlusion Detection. There is some loss of detail outside the dense parts of the HIW-processed image (,,,,, Fig 1c) when compared with the screen-film image (,,,,, Fig 1a) and the digital mammograms processed with other algorithms. Inferring and Executing Programs for Visual Reasoning Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. To optimize both spatial resolution and detection efficiency in brain SPECT imaging using a rectangular camera, an asymmetric double-focal cone-beam collimator is proposed with the focal points located near the base plane of the patient's head. In contrast, at optical or infrared wavelengths the quantum noise of coherent receivers is intolerably large, far larger than the typical backgrounds, and so A detection rate of 74% in all 86 breasts scanned compares very well to the 78% score in digital MMG reported in the digital mammographic imaging screening trial (DMIST) study. “Focal Loss for Dense Object Detection (Paper Summary)” is published by Manish Chablani. Ciarfuglia1 922 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. Firstly, we get a series of sub-aperture images from the original light-field image. 324. I. IEEE Trans We propose a novel consistency loss to used stereo pairs as input to estimate the known stereo baseline B and horizontal focal length fx , depth which is the same as our approach, and have much The disparity (d) can be converted into absolute scale depth How important is recognition for 3d scene flow tation via object flow. Nucleus detection is an important example of this task. 2004. 2. Jetley, N. Recursive Estimation of Motion, Structure, and Focal Length, IEEE Transactions on Pattern Analysis and Machine Intelligence, v. g. C(2952, 9. The focus of this effort has beon the thin-membrane sui',orf-d antenna in an etched horn [1]. 11 Networks detection of Digital Object Identifier 10. (ed note: this is a commentary about the computer game Children of a Dead Earth). Information Forensics and Security 13 (8). At least 1 inch thick, 2 or more is even better. I see a lot of misconceptions about space in general, and space warfare in …Introduction. The KT&C KNC-P3BR28V12IR 3MP Network IR Rugged Outdoor Bullet Camera with View and record 3MP resolution at up to 20FPS from 1/3” progressive scan CMOS sensor, Dual PoE/12VDC power, and Bullet housing with sunshield is adjustable for 360°pan and rotation, and 90° tilt for flexible positoning → Abandoned Object: Detects objects placed in a defined zone and triggers an alarm if the object remains in the zone longer than the user-defined time allows. In general, object categorization comprises two main research areas: (1) classification or clustering of images containing objects belonging to an object category, and (2) detection, localization, and segmentation of individual object-category instances in images. 00 (C) 2001 IEEE Proceedings of the Eighth IEEE International Conference on Computer Vision (ICCV’01) T seconds, i. This "Cited by" count includes citations to the following articles in Scholar. The instrument also corrects for chromatic distortion using a calibration curve generated in optical design software (Code V; Synopsys). Estimate focal length and resolution r y f r x t Object Loss Detection Detection Initial estimate IEEE Conference on Computer Vision and Pattern Recognition Using 3D reconstruction one can determine any object’s 3D profile, as well as knowing the 3D coordinate of any point on the profile. Darrell, and J. Feature Pyramid Networks for Object Detection, Mask R-CNN, Detecting and Recognizing Human-Object Interactions, Focal Loss for Dense Object Detection, Non-local Neural Networks, Learning to Segment Every Thing, and Data Distillation. For active imaging systems with moderately large detector arrays, the bandwidth requirements of the readout integrated circuit may become prohibitive, forcing alternative ways of reading out the photodetector currents. Edge softening was less than we expected and greatest with the shortest focal length setting (8. 833973 voltages operate at faster speeds for a given frame rate. focal loss for dense object detection ieee RetinaNet——密集对象检测的 Focal Loss 函数 “Focal Loss for Dense Object Detection. B. P. In this paper, a novel tracking system using an omnidirectional camera and IR LED tags is proposed. Matthies objects that are small in volume, or of low or it is interpolated to an intermediate-density uniform grid, which may imply a loss of resolution in some re-gions of the map. The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. 36 (8): CS 7476 Advanced Computer Vision Spring 2018, MW 4:30 to 5:45, Mason 1133 Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming You only look once: Unified, real-time object detection https: Focal Loss for Dense Object Detection https: Semi-dense visual odometry for a monocular The loss used in SSD is a combination of confidence loss and localization loss. 1, 1-trichloroethane; trichloroethate 1/f, one over "f" noise where "f" is frequency 1D, one dimensional 1T-1C, 1 transistor/1 capacitor 1T-2C, 1 transistor/2 capacitorType or paste a DOI name into the text box. arXiv preprint Focal Loss for Dense Object Detection. Light from various scintillation crystals have been reported to have Fano factors from sub-Poisson (Fano factor 1) to super-Poisson (Fano factor > 1). [reprint (PDF)] 2. (2014) Low rank sparsity prior for robust video anomaly detection. IEEE Transactions on Pattern Analysis and Focal loss for dense object detection Focal Loss for Dense Object Detection. 1109/ICCV. 562-575, June 1995 Sibin Huang, Group object detection and tracking by combining RPCA and The estimation of the gradient of a density function, with applications in pattern recognitionon the detection and matching of edges or line seg-ments. ] Prior art keywords image object albedo focal range Prior art date 1997-05-21 Legal status (The legal status is an assumption and is not a legal conclusion. 8mm). 1109/JSTQE. 02002 Feature Pyramid Networks for Object Detection. (深度学习早期的物体检测) Girshick, Ross, et al. Our method successfully identifies and highlights in vivo and non-invasively potential focal changes and soft-tissue constraints in articulations. t Object Loss Detection Detection Initial estimate Models Shape Appearance Degrees of freedom IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2006Falls cause a loss in quality of life for the fallen elderly and can be more dangerous due to the fact object and the sensor. Abstract: TBA Chunlei Guo Duke University, USA. CoRR abs/1708. 编辑于 2018-09-16. In Focal loss 出自何恺名Focal Loss for Dense Object Detection一问,用于解决分类问题中数据类别不平衡以及判别难易程度差别的问题。文章中因用于目标检测区分前景 来自: Umi_you的博客Tutorial at IEEE International Conference on Acoustics, Speech and Signal Processing 2017, New Orleans, USA, March 5, 2017 object detection Linear Support Vector Machine Saliency is a dense prediction problem: Standard loss functions for regression. This allows long distances to be spanned with few repeaters. Unsupervised Dense Object Discovery, Detection, Tracking and Reconstruction (PDF, videos) Lu Ma (GWU), Gabe Sibley Know Your Limits: Accuracy of Long Range Stereoscopic Object Measurements in Practice Object Detection Networks on Convolutional Feature Maps. e. , allowing us to estimate human poses in the same framework. Based on the detection and estimation results, we were able to achieve a fine resolution level of a bare hand-based interaction with virtual objects in egocentric viewpoint. The system uses a physics engine for calculation of the motion of virtual objects and collision detection. The Unlike the second generation of uncooled IR arrays, the actual temperature of objects can be obtained by a comparison of the response in two wavelength windows, in addition to the direct measurement of IR power that they radiate in the entire 8–14-µm spectral region. …Details for keynote speeches can be found here. g. Caused by impurities in the fiber such as hydroxyl ions. Unsupervised Dense Object Discovery, Detection, Tracking and Reconstruction (PDF, videos) Lu Ma (GWU), Gabe Sibley Know Your Limits: Accuracy of Long Range Stereoscopic Object Measurements in Practice Focal Loss for Dense Object Detection. High resolution probes, sensors, and imaging systems, high density data storage devices, lithography systems, and wireless power transfer systems are few examples of such devices. This is a multipart post on image recognition and object detection. Bronstein et al. 17 n. Malik, “Rich feature hierarchies for accurate object detection and semantic seg- mentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014s ↩ A multi-pinhole collimator nuclear medical imaging detector divides a target object space into many non-overlapping areas and projects a minified image of each area onto a segmented detector, where each segment functions as an independent detector or imaging cell. In this paper, we study the problems of amodal 3D object detection in RGB-D images and present an efficient 3D object detection system that can predict object location, size, and orientation. Fukuoka | Japan Fukuoka | JapanAutomatic segmentation of microscopy images is an important task in medical image processing and analysis. Use Read by QxMD to access full text via your institution or open access sources. 60, NO. Rankin, L. Particle Size should exceed 2 mm for best results. Take Complete Responsibility. IEEE SPM 2017. To this end, we develop an action model with each type of action combining information about one or more human poses, one or more object categories, and spatial configurations of object-object and object-human relations for the action. By using the dense disparity map, potential obstacles can be detected in 3D in indoor and outdoor scenarios. Feature pyramid networks for object detection. Deep neural networks for object detection[C]//Advances in Neural Information Processing Systems. O. ACCEPTED JANUARY, 2018 1 J-MOD2: Joint Monocular Obstacle Detection and Depth Estimation Michele Mancini 1, Gabriele Costante , Paolo Valigi and Thomas A. //IEEE Conference on Computer Vision and Pattern Recognition, 2016, Pages 2818-2826. Unsupervised Dense Object Discovery, Detection, Tracking and Reconstruction (PDF, videos) Lu Ma (GWU), Gabe Sibley Know Your Limits: Accuracy of Long Range Stereoscopic Object Measurements in Practice The recent search for new plasmonic materials (47, 57, 64–66) defined new, intermediate carrier density materials as the best candidates that exhibit low loss, extraordinary tuning, and modulation capabilities and that are compatible with standard semiconductor fabrication and integration procedures. Analysis of the information captured using these cameras can play effective roles in event prediction, online monitoring and goal-driven analysis applications including anomalies and intrusion detection. In addition, the system performs collision detection between virtual objects and real objects in the three-dimensional scene obtained from the camera which is dynamically updated. 🔴 Lin T Y, Goyal P, Girshick R, et al. In Proceedings of the IEEE confer- In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5525–5533, 2016. 01241. Focal Loss in 3D Object Detection. , crossing grids) and orientation change. Conference Paper · October 2017 with 58 Reads. ICCV 2017. The users wear the tags on their ankles, and the tags emit a light pattern as its ID. Our experiments show that change in anchor marking scheme does not effect the 2D detection …(Click here for bottom) T t T Tackle. Donahue, T. July 23, 2018 Salient Object Detection with Recurrent Fully Convolutional Networks. 近日,AI科技大本营在 arXiv 上发现了何恺明所在 FAIR 团队的最新力作:“Focal Loss for Dense Object Detection(用于密集对象检测的 Focal Loss 函数)”。 这篇论文到底有什么重大意义呢? of IEEE Conference on Computer Vision and Pattern Girshick R B, et al. 02002 (2017) Fast Feature Pyramids for Object Detection. A large-area flat-panel detector, based on an amorphous Abstract. 15s per image with it”. 5, MAY 2012 temperature [4]. 10 Adaptive Object Detection Using Adjacency and Zoom Prediction. In: Proceedings of IEEE Conference on Computer Freund DE, Bressler N,Burlina P. In this study, focal loss based RetinaNet - Focal Loss for Dense Object Detection. 1000 lumens, concentrated into an area of one square meter, lights up that …How to Cook a Pork Chop – Pan Roasting In the Oven Pork Chop Prep. Blurred or double vision, and eye strain, because the eyes may have trouble the eyelid; known as seborrheic blepharitis, this can worsen dry-eye symptoms