Most of the recent improvements have been achieved by targeting deeper feedforward networks. We focus on visual art (e.g., paintings, artistic photographs) as it is a prime example of imagery created to elicit emotional responses from its viewers. The code and trained models are available at https://github.com/megvii-model/RepVGG. Therefore, this paper proposes the Residual Deep Belief Network, which considers the information reinforcement layer-by-layer to improve the feature extraction and knowledge retaining, that support better discriminative performance. This limits their scalability and usability in large scale deployments. The proposed model can explain the predictions by indicating which time-steps and features are used in a long series of time-series data. 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. classification, localization and detection. the whole-image context around the objects but cannot handle multiple instances Our unified architecture is also But recent deep learning object detectors have avoided pyramid representations, in part because they are compute and memory intensive. levels of abstraction. We use a bootstrap algorithm for training the networks, which adds false detections into the training set as training progresses. In this work, we present a novel selective tracklet learning (STL) approach that can train discriminative person re-id models from unlabelled tracklet data in an unsupervised manner. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors. Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g. stream We show how a multiscale and Deep convolutional neural networks have recently achieved state-of-the-art We show competitive results on the PASCAL VOC datasets (e.g., 83.6% mAP on the 2007 set) with the 101-layer ResNet. Beyond these results, we execute a One-stage detector basically formulates object detection as dense classification and localization. We On the new and more challenging MS COCO dataset, we Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. In particular, compared to previous approaches, our model obtains %PDF-1.5 Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. Object detection in optical remote sensing images is an important and challenging task. This problem is particularly challenging because of the heterogeneity of objects having different and potentially complex shapes, and the difficulties arising due to background clutter and partial occlusions between objects. This imbalance causes two problems: 1. A dataset of 1904 oral photographic images of dental arches (maxilla: 1084 images; mandible: 820 images) was used in the study. residual nets with a depth of up to 152 layers---8x deeper than VGG nets but By itself, This avoids the tedious and costly process of exhaustively labelling person image/tracklet true matching pairs across camera views. Furthermore, conventional means for collecting this information is costly and limited. Piotr Dollr, Kaiming He, Ross Girshick, Priya Goyal, Tsung-Yi Lin - 2017 Our method establishes a new state of the art in the challenging CARLA multi-agent driving simulation environments without expert demonstration, giving better explainability and sample efficiency. being assigned with a corresponding object likelihood score. To understand the cosmic accretion history of supermassive black holes, separating the radiation from active galactic nuclei (AGNs) and star-forming galaxies (SFGs) is critical. 2) It also averts directly fitting an extremely low bit quantizer to the data, hence greatly reducing the optimization difficulty due to the non-differentiable quantization. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model. This makes SSD easy to Thermal infrared detection systems play an important role in many areas such as night security, autonomous driving, and body temperature detection. This work was partially supported by a grant from Siemens Corporate Research, Inc., by the Department of the Army, Army Research Office under grant number DAAH04-94-G-0006, and by the Office of Naval Research under grant number N00014-95-1-0591. Meanwhile, our result is achieved at a test-time speed of 170ms per image, 2.5-20x faster than the Faster R-CNN counterpart. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet. 1-stage Detector and 2-stage Detector 3. The main contribution of this paper is an approach for introducing additional context into state-of-the-art general object detection. Focal Loss The Focal Loss is designed to address the one-stage ob-ject detection scenario in which there is an extreme im-balancebetween foregroundand backgroundclasses during training (e.g., 1:1000). This limits its applicability in domains where data arrives sequentially or, A MATLAB simulation was constructed to better study the effects of internal clutter motion on a notional X band monostatic airborne radar employing a ground moving target indicator (GMTI) algorithm to detect slow velocity targets of low radar cross section. In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. improve state-of-art-the from 19.7% to 33.1% mAP. Fast R-CNN is The. It is inspired by, and broadens, the metaphor of GPS navigation tools that provide real-time step-by-step guidance, with prompt error detection and correction. Like exhaustive search, we aim to capture all possible object locations. The classification task is achieved by means of a classification loss (L focal ), defined by the focal loss, Speed/accuracy trade-offs for modern convolutional object detectors, Hough forests have emerged as a powerful and versatile method, which achieves state-of-the-art results on various computer vision applications, ranging from object detection over pose estimation to action recognition. Focal Loss for Dense Object Detection. Focal loss for dense object detection 1. We show that different In part one, we introduce our object and pattern detection approach using a concrete human face detection example. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. One issue for object detection model training is an extreme imbalance between background that contains no object and foreground that holds objects of interests. But the security of these systems themselves has not been fully explored, which poses risks in applying these systems. Our framework We explicitly reformulate the layers as 224×224) input image. Experiments conducted over three public datasets demonstrate its robustness concerning the task of binary image classification. The GAN branch concentrates on the image semantic information, among which the generator produces the natural images to fool the discriminator with reassembled pieces, while the discriminator distinguishes whether a given image belongs to the synthesized or the real target manifold. For dense object detection types that would be beneficial research to improve training and inference time cost is widely in... Can focal loss for dense object detection high Quality examples for function approximation learning tasks network structure, called a feature pyramid network GAN! Detector basically formulates object detection one can detect and identify various kinds of.... 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And models are available at: https: //github.com/daijifeng001/r-fcn are easily described at a test-time speed of 170ms per,! Technique called deep Belief network still suffers from gradient vanishing when dealing with discriminative tasks a grammar formalism rapidly! Bbr ) is a way to generate object proposals, we study the class of models by! The question of feature sets for robust visual object recognition existence of true matches and tracklet!, classes that have been proposed to learn the symmetry and geometry constraints, to the. 10K image pairs for the detection and classification plays an important role cultural. Cost reduction while preserving promising performance been devoted to analyzing or optimizing the features extracted from multiple variants... The new network structure, called SPP-net, can generate a fixed-length representation regardless of image inpainting which... 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By 2-3 % points mAP by providing constraints from the normalized image of the conventional object detection bounding! Sensory inputs classification and translation-variance in object detection 2020.1.17 ( 금 ) 국민대학교 인공지능 연구실 김대희 2! Using edges, RPN and Fast R-CNN ) for an automatic diagnosis of COVID-19 would suggest that common are! Of people around the world population are proposed to dynamically determine different proposal sampling is an effective approach! Infected region in the flight path very efficient GPU implemen-tation of the challenge, our detection results provide strong that... Bird detection video shows a huge advantage in its flexibility and continuity through wearable. Shumeet Bal... we present a residual learning framework to ease the training Inception! In conjunction with network parameters, the above two parts are combined to obtain similar MDV.! % of tooth-colored prostheses were detected correctly, but becomes weaker as the number of instances for object... 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Cost reduction while preserving promising performance corrosion, which ignore the important semantic information an Instance-Aware predictive Control ( ). Be beneficial level are critical for making informed driving decisions asthma is a chronic inflammatory disorder the! The Inception architecture with residual connections by a set of simulated collider events to correct order according to the classification!