∙ 0 ∙ share . The usage of deep learning is varied, from object detection in self-driving cars to disease detection with medical imaging deep learning has proved to achieve human level accuracy & better. With the rapid development in deep learning, it has drawn attention of several researchers with innovations in approaches to join a race. These object detection has been develop to help solve many problem such as autonomous driving, object counting and pose estimation. Deep learning and its applications in computer vision, including image classification, object detection, semantic segmentation, etc. Then, a classi er model is proposed and the results for the pedestrian classi cation tasks are presented. of NIPS Workshop on Bayesian Deep Learning, 2017. This year, I also aim to be more consistent with my blogs and learning. We build a multi-level representation from the high resolution and apply it to the Faster R-CNN, Mask R-CNN and Cascade R-CNN framework. Namely example are masked RCNN and YOLO object detection algorithm. Now it is the Top1 neural network for object detection. In this work, our tiny-model outperforms other small sized detection network (pelee, mobilenet-ssd or tiny-yolo) in the metrics of FLOPs, parameter size and accuracy. Convolutional Neural Networks. Keywords: Tracking, deep learning, neural networks, machine learning 1 Introduction Given some object of interest marked in one frame of a video, the goal of \single-target tracking" is to locate this object in subsequent video frames, despite object The detection models can get better results for big object. A feature extraction network followed by a detection network. This note covers advancement in computer vision/image processing powered by convolutional neural network (CNN) in increasingly more challenging topics from Image Classification to Object Detection to Segmentation.. 27.06.2020 — Deep Learning, Computer Vision, Object Detection, Neural Network, Python — 5 min read Share TL;DR Learn how to build a custom dataset for YOLO v5 (darknet compatible) and use it to fine-tune a large object detection model. Compared with other computer vision tasks, the history of small object detection is relatively short. (official and unofficial) Deep Learning based Approaches Deep Regression Networks (ECCV, 2016) Paper: click here. small object detection github, Object Detection. Update log. Motivation. Index Terms—Baggage screening, Deep Learning, Convolutional Neural Networks, Image filtering, Object Detection Algorithms, X-ray Images . ... , yielding much higher precision in object contour detection than previous methods. DeepScores comes with ground truth for object classification, detection and semantic segmenta- tion. This paper presents an object detector based on deep learning of small samples. 2020/june - update arxiv papers. Earlier architectures for object detection consisted of two distinct stages - a region proposal network that performs object localization and a classifier for detecting the types of objects in the proposed regions. https://github.com/kuanhungchen/awesome-tiny-object-detection We construct a novel training strategy consisting of a combination of optimal set of anchor scales and utilization of SE blocks for detection and learning a deep association network for tracking detected images in the subsequent frames. The arxiv version of the paper can be found here. The feature extraction network is typically a pretrained CNN (for details, see Pretrained Deep Neural Networks (Deep Learning Toolbox)). Object Detection: Locate the presence of objects with a bounding box and types or classes of the located objects in an image. The hello world of object detection would be using HOG features combined with a classifier like SVM and using sliding windows to make predictions at different patches of the image. ... heading angle regression and using FPN to improve detection of small objects. An end-to-end solution for robotic manipulation of unknown objects, including object detection, grasp detection and control. Tiny-DSOD tries to tackle the trade-off between detection accuracy and computation resource consumption. Today, I would like to share an interesting soluti… Scaled YOLO v4 is the best neural network for object detection — the most accurate (55.8% AP Microsoft COCO test-dev) among neural network published. [32] uses a two-level tiling based technique in order to detect small objects. 2018/november - update 9 papers. A drone project that performs object detection and make a search engine out of the drone feed. Single Shot Detectors. The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. You signed in with another tab or window. Update log. News [2020.12] One paper is accepted by AAAI 2021. tracker that learns to track generic objects at 100 fps. Bounding Box Regression with Uncertainty for Accurate Object Detection Yihui He, Chenchen Zhu, Jianren Wang, Marios Savvides, Xiangyu Zhang, CVPR 2019 [presentation]. Learn more. • Requires training a size estimator from a small set 34 Fig: [Shi ECCV 16] Priors: Motion 3. The part highlighted with red characters means papers that i think "must-read". 2018/october - update 5 papers and performance table. Braun, Markus and Krebs, Sebastian and Flohr, Fabian B. and Gavrila, Dariu M. Shifeng Zhang, Yiliang Xie, Jun Wan, Hansheng Xia, Stan Z. Li, Guodong Guo, Gui-Song Xia, Xiang Bai, Jian Ding, Zhen Zhu, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, Liangpei Zhang, Lukáš Neumann, Michelle Karg, Shanshan Zhang, Christian Scharfenberger, Eric Piegert, Sarah Mistr, Olga Prokofyeva, Robert Thiel, Andrea Vedaldi, Andrew Zisserman, and Bernt Schiele, Lukas Tuggener, Ismail Elezi, Jurgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann, Karsten Behrendt, Libor Novak, Rami Botros, Shanshan Zhang, Rodrigo Benenson, Bernt Schiele, Shuo Yang, Ping Luo, Chen Change Loy, Xiaoou Tang, Piotr Dollár, Christian Wojek, Bernt Schiele, Pietro Perona, Liming Wang, Jianbo Shi, Gang Song, I-fan Shen, Kai Han, Yunhe Wang, Qiulin Zhang, Wei Zhang, Chunjing Xu, Tong Zhang, Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang, Lingling Li, Zhixi Feng, Rong Qu, Xiongwei Wu, Doyen Sahoo, Steven C.H. Object detection with deep learning and OpenCV. Its size is only 1.3M and very suitable for deployment in low computing power scenarios such as edge devices. Key ideas. I wrote this page with reference to this survey paper and searching and searching.. Last updated: 2019/10/18. (Need more investigation into this topic) Key ideas. The Table came from this survey paper. # Deep Learning based methods for object detection and tracking. Model Solver. However 0.5:0.5 ratio works better than 0.1:0.9 mixup ratio. In this series of posts on “Object Detection for Dummies”, we will go through several basic concepts, algorithms, and popular deep learning models for image processing and objection detection. The solution is to measure the performance of all models on hardware with equivalent specifications, but it is very difficult and time consuming. 03/17/2020 ∙ by Al-Akhir Nayan, et al. Image Classification 2019/february - update 3 papers. Modern drones are be equipped with cameras and are very prospective for a variety of commercial uses such as aerial photography, surveillance, etc.n. 相关资料 Understanding SSD MultiBox — Real-Time Object Detection In Deep Learning 没有涉及数学原理解释SSD目标检测。 caffe SSD 原论文使用的代码。 SSD-Tensorflow 使用Tensorflow实现的SSD算法。 ssd_eccv2016_slide.pdf 解释SSD工作的演示PPT。 Synthetic samples generator is designed by switching the object regions in different scenes. 2018/9/18 - update all of recent papers and make some diagram about history of object detection using deep learning. Work fast with our official CLI. Deep learning based approaches for object detection is revolutionizing the capabilities of autonomous navigation vehicles robustly. Work fast with our official CLI. Re-localization and Re-training 35 ... Divide object detection into two sub-tasks with a two stream architecture ... ☺End-to-end learning + No custom deep learning layers ☺State-of … If nothing happens, download the GitHub extension for Visual Studio and try again. Obj e ct detection before Deep Learning was a several step process, starting with edge detection and feature extraction using techniques like SIFT, HOG etc. First, a state of the art is made on object and pedestrian detection. What is deep learning? We first use state-of-the-art object detection method Robotic Manipulation of Unknown Objects. Research Interest My primary research interests are generic object detection, object detection in remote sensing images, few-shot learning, and deep learning … Therefore, the YOLO model family is known for its speed. In this section, we will present current target tracking algorithms based on Deep Learning. Mixup helps in object detection. A paper list of object detection using deep learning. 2020/may - update CVPR 2020 papers and other papers. | [CVPR' 19] |[pdf] | [official code - torch], [ScratchDet] ScratchDet: Training Single-Shot Object Detectors from Scratch | [CVPR' 19] |[pdf], Bounding Box Regression with Uncertainty for Accurate Object Detection | [CVPR' 19] |[pdf] | [official code - caffe2], Activity Driven Weakly Supervised Object Detection | [CVPR' 19] |[pdf], Towards Accurate One-Stage Object Detection with AP-Loss | [CVPR' 19] |[pdf], Strong-Weak Distribution Alignment for Adaptive Object Detection | [CVPR' 19] |[pdf] | [official code - pytorch], [NAS-FPN] NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | [CVPR' 19] |[pdf], [Adaptive NMS] Adaptive NMS: Refining Pedestrian Detection in a Crowd | [CVPR' 19] |[pdf], Point in, Box out: Beyond Counting Persons in Crowds | [CVPR' 19] |[pdf], Locating Objects Without Bounding Boxes | [CVPR' 19] |[pdf], Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects | [CVPR' 19] |[pdf], Towards Universal Object Detection by Domain Attention | [CVPR' 19] |[pdf], Exploring the Bounds of the Utility of Context for Object Detection | [CVPR' 19] |[pdf], What Object Should I Use? - Task Driven Object Detection | [CVPR' 19] |[pdf], Dissimilarity Coefficient based Weakly Supervised Object Detection | [CVPR' 19] |[pdf], Adapting Object Detectors via Selective Cross-Domain Alignment | [CVPR' 19] |[pdf], Fully Quantized Network for Object Detection | [CVPR' 19] |[pdf], Distilling Object Detectors with Fine-grained Feature Imitation | [CVPR' 19] |[pdf], Multi-task Self-Supervised Object Detection via Recycling of Bounding Box Annotations | [CVPR' 19] |[pdf], [Reasoning-RCNN] Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection | [CVPR' 19] |[pdf], Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation | [CVPR' 19] |[pdf], Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors | [CVPR' 19] |[pdf], Spatial-aware Graph Relation Network for Large-scale Object Detection | [CVPR' 19] |[pdf], [MaxpoolNMS] MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors | [CVPR' 19] |[pdf], You reap what you sow: Generating High Precision Object Proposals for Weakly-supervised Object Detection | [CVPR' 19] |[pdf], Object detection with location-aware deformable convolution and backward attention filtering | [CVPR' 19] |[pdf], Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection | [CVPR' 19] |[pdf], Hybrid Task Cascade for Instance Segmentation | [CVPR' 19] |[pdf], [GFR] Improving Object Detection from Scratch via Gated Feature Reuse | [BMVC' 19] |[pdf] | [official code - pytorch], [Cascade RetinaNet] Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection | [BMVC' 19] |[pdf], Soft Sampling for Robust Object Detection | [BMVC' 19] |[pdf], Multi-adversarial Faster-RCNN for Unrestricted Object Detection | [ICCV' 19] |[pdf], Towards Adversarially Robust Object Detection | [ICCV' 19] |[pdf], A Robust Learning Approach to Domain Adaptive Object Detection | [ICCV' 19] |[pdf], A Delay Metric for Video Object Detection: What Average Precision Fails to Tell | [ICCV' 19] |[pdf], Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach | [ICCV' 19] |[pdf], Employing Deep Part-Object Relationships for Salient Object Detection | [ICCV' 19] |[pdf], Learning Rich Features at High-Speed for Single-Shot Object Detection | [ICCV' 19] |[pdf], Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection | [ICCV' 19] |[pdf], Selectivity or Invariance: Boundary-Aware Salient Object Detection | [ICCV' 19] |[pdf], Progressive Sparse Local Attention for Video Object Detection | [ICCV' 19] |[pdf], Minimum Delay Object Detection From Video | [ICCV' 19] |[pdf], Towards Interpretable Object Detection by Unfolding Latent Structures | [ICCV' 19] |[pdf], Scaling Object Detection by Transferring Classification Weights | [ICCV' 19] |[pdf], [TridentNet] Scale-Aware Trident Networks for Object Detection | [ICCV' 19] |[pdf], Generative Modeling for Small-Data Object Detection | [ICCV' 19] |[pdf], Transductive Learning for Zero-Shot Object Detection | [ICCV' 19] |[pdf], Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection | [ICCV' 19] |[pdf], [CenterNet] CenterNet: Keypoint Triplets for Object Detection | [ICCV' 19] |[pdf], [DAFS] Dynamic Anchor Feature Selection for Single-Shot Object Detection | [ICCV' 19] |[pdf], [Auto-FPN] Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification | [ICCV' 19] |[pdf], Multi-Adversarial Faster-RCNN for Unrestricted Object Detection | [ICCV' 19] |[pdf], Object Guided External Memory Network for Video Object Detection | [ICCV' 19] |[pdf], [ThunderNet] ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices | [ICCV' 19] |[pdf], [RDN] Relation Distillation Networks for Video Object Detection | [ICCV' 19] |[pdf], [MMNet] Fast Object Detection in Compressed Video | [ICCV' 19] |[pdf], Towards High-Resolution Salient Object Detection | [ICCV' 19] |[pdf], [SCAN] Stacked Cross Refinement Network for Edge-Aware Salient Object Detection | [ICCV' 19] |[official code] |[pdf], Motion Guided Attention for Video Salient Object Detection | [ICCV' 19] |[pdf], Semi-Supervised Video Salient Object Detection Using Pseudo-Labels | [ICCV' 19] |[pdf], Learning to Rank Proposals for Object Detection | [ICCV' 19] |[pdf], [WSOD2] WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for Weakly-Supervised Object Detection | [ICCV' 19] |[pdf], [ClusDet] Clustered Object Detection in Aerial Images | [ICCV' 19] |[pdf], Towards Precise End-to-End Weakly Supervised Object Detection Network | [ICCV' 19] |[pdf], Few-Shot Object Detection via Feature Reweighting | [ICCV' 19] |[pdf], [Objects365] Objects365: A Large-Scale, High-Quality Dataset for Object Detection | [ICCV' 19] |[pdf], [EGNet] EGNet: Edge Guidance Network for Salient Object Detection | [ICCV' 19] |[pdf], Optimizing the F-Measure for Threshold-Free Salient Object Detection | [ICCV' 19] |[pdf], Sequence Level Semantics Aggregation for Video Object Detection | [ICCV' 19] |[pdf], [NOTE-RCNN] NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection | [ICCV' 19] |[pdf], Enriched Feature Guided Refinement Network for Object Detection | [ICCV' 19] |[pdf], [POD] POD: Practical Object Detection With Scale-Sensitive Network | [ICCV' 19] |[pdf], [FCOS] FCOS: Fully Convolutional One-Stage Object Detection | [ICCV' 19] |[pdf], [RepPoints] RepPoints: Point Set Representation for Object Detection | [ICCV' 19] |[pdf], Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection | [ICCV' 19] |[pdf], Weakly Supervised Object Detection With Segmentation Collaboration | [ICCV' 19] |[pdf], Leveraging Long-Range Temporal Relationships Between Proposals for Video Object Detection | [ICCV' 19] |[pdf], Detecting 11K Classes: Large Scale Object Detection Without Fine-Grained Bounding Boxes | [ICCV' 19] |[pdf], [C-MIDN] C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection | [ICCV' 19] |[pdf], Meta-Learning to Detect Rare Objects | [ICCV' 19] |[pdf], [Cap2Det] Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection | [ICCV' 19] |[pdf], [Gaussian YOLOv3] Gaussian YOLOv3: An Accurate and Fast Object Detector using Localization Uncertainty for Autonomous Driving | [ICCV' 19] |[pdf] [official code - c], [FreeAnchor] FreeAnchor: Learning to Match Anchors for Visual Object Detection | [NeurIPS' 19] |[pdf], Memory-oriented Decoder for Light Field Salient Object Detection | [NeurIPS' 19] |[pdf], One-Shot Object Detection with Co-Attention and Co-Excitation | [NeurIPS' 19] |[pdf], [DetNAS] DetNAS: Backbone Search for Object Detection | [NeurIPS' 19] |[pdf], Consistency-based Semi-supervised Learning for Object detection | [NeurIPS' 19] |[pdf], [NATS] Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection | [NeurIPS' 19] |[pdf], [AA] Learning Data Augmentation Strategies for Object Detection | [arXiv' 19] |[pdf], [Spinenet] Spinenet: Learning scale-permuted backbone for recognition and localization | [arXiv' 19] |[pdf], Object Detection in 20 Years: A Survey | [arXiv' 19] |[pdf], [Spiking-YOLO] Spiking-YOLO: Spiking Neural Network for Real-time Object Detection | [AAAI' 20] |[pdf], Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction | [AAAI' 20] |[pdf], [CBnet] Cbnet: A novel composite backbone network architecture for object detection | [AAAI' 20] |[pdf], [Distance-IoU Loss] Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression | [AAAI' 20] |[pdf], Computation Reallocation for Object Detection | [ICLR' 20] |[pdf], [YOLOv4] YOLOv4: Optimal Speed and Accuracy of Object Detection | [arXiv' 20] |[pdf], Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector | [CVPR' 20] |[pdf], Large-Scale Object Detection in the Wild From Imbalanced Multi-Labels | [CVPR' 20] |[pdf], Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection | [CVPR' 20] |[pdf], Rethinking Classification and Localization for Object Detection | [CVPR' 20] |[pdf], Multiple Anchor Learning for Visual Object Detection | [CVPR' 20] |[pdf], [CentripetalNet] CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection | [CVPR' 20] |[pdf], Learning From Noisy Anchors for One-Stage Object Detection | [CVPR' 20] |[pdf], [EfficientDet] EfficientDet: Scalable and Efficient Object Detection | [CVPR' 20] |[pdf], Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax | [CVPR' 20], Dynamic Refinement Network for Oriented and Densely Packed Object Detection | [CVPR' 20] |[pdf], Noise-Aware Fully Webly Supervised Object Detection | [CVPR' 20], [Hit-Detector] Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection | [CVPR' 20] |[pdf], [D2Det] D2Det: Towards High Quality Object Detection and Instance Segmentation | [CVPR' 20], Prime Sample Attention in Object Detection | [CVPR' 20] |[pdf], Don’t Even Look Once: Synthesizing Features for Zero-Shot Detection | [CVPR' 20] |[pdf], Exploring Categorical Regularization for Domain Adaptive Object Detection | [CVPR' 20] |[pdf], [SP-NAS] SP-NAS: Serial-to-Parallel Backbone Search for Object Detection | [CVPR' 20], [NAS-FCOS] NAS-FCOS: Fast Neural Architecture Search for Object Detection | [CVPR' 20] |[pdf], [DR Loss] DR Loss: Improving Object Detection by Distributional Ranking | [CVPR' 20] |[pdf], Detection in Crowded Scenes: One Proposal, Multiple Predictions | [CVPR' 20] |[pdf], [AugFPN] AugFPN: Improving Multi-Scale Feature Learning for Object Detection | [CVPR' 20] |[pdf], Robust Object Detection Under Occlusion With Context-Aware CompositionalNets | [CVPR' 20], Cross-Domain Document Object Detection: Benchmark Suite and Method | [CVPR' 20] |[pdf], Exploring Bottom-Up and Top-Down Cues With Attentive Learning for Webly Supervised Object Detection | [CVPR' 20] |[pdf], [SLV] SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection | [CVPR' 20], [HAMBox] HAMBox: Delving Into Mining High-Quality Anchors on Face Detection | [CVPR' 20] |[pdf], [Context R-CNN] Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection | [CVPR' 20] |[pdf], Mixture Dense Regression for Object Detection and Human Pose Estimation | [CVPR' 20] |[pdf], Offset Bin Classification Network for Accurate Object Detection | [CVPR' 20], [NETNet] NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection | [CVPR' 20] |[pdf], Scale-Equalizing Pyramid Convolution for Object Detection | [CVPR' 20] |[pdf], Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians | [CVPR' 20] |[pdf], [MnasFPN] MnasFPN: Learning Latency-Aware Pyramid Architecture for Object Detection on Mobile Devices | [CVPR' 20] |[pdf], Physically Realizable Adversarial Examples for LiDAR Object Detection | [CVPR' 20] |[pdf], Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation | [CVPR' 20] |[pdf], Incremental Few-Shot Object Detection | [CVPR' 20] |[pdf], Where, What, Whether: Multi-Modal Learning Meets Pedestrian Detection | [CVPR' 20], Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation | [CVPR' 20] |[pdf], Learning a Unified Sample Weighting Network for Object Detection | [CVPR' 20], Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization | [CVPR' 20] |[pdf], DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution | [arXiv' 20] |[pdf], [DETR] End-to-End Object Detection with Transformers | [ECCV' 20] |[pdf], Suppress and Balance: A Simple Gated Network for Salient Object Detection | [ECCV' 20] |[code], [BorderDet] BorderDet: Border Feature for Dense Object Detection | [ECCV' 20], Corner Proposal Network for Anchor-free, Two-stage Object Detection | [ECCV' 20], A General Toolbox for Understanding Errors in Object Detection | [ECCV' 20], [Chained-Tracker] Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking | [ECCV' 20], Side-Aware Boundary Localization for More Precise Object Detection | [ECCV' 20], [PIoU] PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments | [ECCV' 20], [AABO] AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling | [ECCV' 20], Highly Efficient Salient Object Detection with 100K Parameters | [ECCV' 20], [GeoGraph] GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end | [ECCV' 20], Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection | [ECCV' 20], Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection | [ECCV' 20], Arbitrary-Oriented Object Detection with Circular Smooth Label | [ECCV' 20], Soft Anchor-Point Object Detection | [ECCV' 20], Object Detection with a Unified Label Space from Multiple Datasets | [ECCV' 20], [MimicDet] MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection | [ECCV' 20], Prior-based Domain Adaptive Object Detection for Hazy and Rainy Conditions | [ECCV' 20], [Dynamic R-CNN] Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training | [ECCV' 20], [OS2D] OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features | [ECCV' 20], Multi-Scale Positive Sample Refinement for Few-Shot Object Detection | [ECCV' 20], Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild | [ECCV' 20], Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection | [ECCV' 20], Two-Stream Active Query Suggestion for Large-Scale Object Detection in Connectomics | [ECCV' 20], [FDTS] FDTS: Fast Diverse-Transformation Search for Object Detection and Beyond | [ECCV' 20], Dual refinement underwater object detection network | [ECCV' 20], [APRICOT] APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection | [ECCV' 20], Large Batch Optimization for Object Detection: Training COCO in 12 Minutes | [ECCV' 20], Hierarchical Context Embedding for Region-based Object Detection | [ECCV' 20], Pillar-based Object Detection for Autonomous Driving | [ECCV' 20], Dive Deeper Into Box for Object Detection | [ECCV' 20], Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN | [ECCV' 20], Probabilistic Anchor Assignment with IoU Prediction for Object Detection | [ECCV' 20], [HoughNet] HoughNet: Integrating near and long-range evidence for bottom-up object detection | [ECCV' 20], [LabelEnc] LabelEnc: A New Intermediate Supervision Method for Object Detection | [ECCV' 20], Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer | [ECCV' 20], On the Importance of Data Augmentation for Object Detection | [ECCV' 20], Adaptive Object Detection with Dual Multi-Label Prediction | [ECCV' 20], Quantum-soft QUBO Suppression for Accurate Object Detection | [ECCV' 20], Improving Object Detection with Selective Self-supervised Self-training | [ECCV' 20]. Convolution Layer. Lipschitz continuous autoencoders in application to anomaly detection presented at AISTATS 2020 Contextual multi-armed bandit algorithm for semiparametric reward model presented at ICML 2019 Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric published in the Machine Learning, 2020 [R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentation | [CVPR' 14] |[pdf] [official code - caffe], [OverFeat] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | [ICLR' 14] |[pdf] [official code - torch], [MultiBox] Scalable Object Detection using Deep Neural Networks | [CVPR' 14] |[pdf], [SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | [ECCV' 14] |[pdf] [official code - caffe] [unofficial code - keras] [unofficial code - tensorflow], Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction | [CVPR' 15] |[pdf] [official code - matlab], [MR-CNN] Object detection via a multi-region & semantic segmentation-aware CNN model | [ICCV' 15] |[pdf] [official code - caffe], [DeepBox] DeepBox: Learning Objectness with Convolutional Networks | [ICCV' 15] |[pdf] [official code - caffe], [AttentionNet] AttentionNet: Aggregating Weak Directions for Accurate Object Detection | [ICCV' 15] |[pdf], [Fast R-CNN] Fast R-CNN | [ICCV' 15] |[pdf] [official code - caffe], [DeepProposal] DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | [ICCV' 15] |[pdf] [official code - matconvnet], [Faster R-CNN, RPN] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | [NIPS' 15] |[pdf] [official code - caffe] [unofficial code - tensorflow] [unofficial code - pytorch], [YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | [CVPR' 16] |[pdf] [official code - c], [G-CNN] G-CNN: an Iterative Grid Based Object Detector | [CVPR' 16] |[pdf], [AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | [CVPR' 16] |[pdf], [ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | [CVPR' 16] |[pdf], [HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | [CVPR' 16] |[pdf], [OHEM] Training Region-based Object Detectors with Online Hard Example Mining | [CVPR' 16] |[pdf] [official code - caffe], [CRAPF] CRAFT Objects from Images | [CVPR' 16] |[pdf] [official code - caffe], [MPN] A MultiPath Network for Object Detection | [BMVC' 16] |[pdf] [official code - torch], [SSD] SSD: Single Shot MultiBox Detector | [ECCV' 16] |[pdf] [official code - caffe] [unofficial code - tensorflow] [unofficial code - pytorch], [GBDNet] Crafting GBD-Net for Object Detection | [ECCV' 16] |[pdf] [official code - caffe], [CPF] Contextual Priming and Feedback for Faster R-CNN | [ECCV' 16] |[pdf], [MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | [ECCV' 16] |[pdf] [official code - caffe], [R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | [NIPS' 16] |[pdf] [official code - caffe] [unofficial code - caffe], [PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | [NIPSW' 16] |[pdf] [official code - caffe], [DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | [PAMI' 16] |[pdf], [NoC] Object Detection Networks on Convolutional Feature Maps | [TPAMI' 16] |[pdf], [DSSD] DSSD : Deconvolutional Single Shot Detector | [arXiv' 17] |[pdf] [official code - caffe], [TDM] Beyond Skip Connections: Top-Down Modulation for Object Detection | [CVPR' 17] |[pdf], [FPN] Feature Pyramid Networks for Object Detection | [CVPR' 17] |[pdf] [unofficial code - caffe], [YOLO v2] YOLO9000: Better, Faster, Stronger | [CVPR' 17] |[pdf] [official code - c] [unofficial code - caffe] [unofficial code - tensorflow] [unofficial code - tensorflow] [unofficial code - pytorch], [RON] RON: Reverse Connection with Objectness Prior Networks for Object Detection | [CVPR' 17] |[pdf] [official code - caffe] [unofficial code - tensorflow], [RSA] Recurrent Scale Approximation for Object Detection in CNN | | [ICCV' 17] |[pdf] [official code - caffe], [DCN] Deformable Convolutional Networks | [ICCV' 17] |[pdf] [official code - mxnet] [unofficial code - tensorflow] [unofficial code - pytorch], [DeNet] DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | [ICCV' 17] |[pdf] [official code - theano], [CoupleNet] CoupleNet: Coupling Global Structure with Local Parts for Object Detection | [ICCV' 17] |[pdf] [official code - caffe], [RetinaNet] Focal Loss for Dense Object Detection | [ICCV' 17] |[pdf] [official code - keras] [unofficial code - pytorch] [unofficial code - mxnet] [unofficial code - tensorflow], [Mask R-CNN] Mask R-CNN | [ICCV' 17] |[pdf] [official code - caffe2] [unofficial code - tensorflow] [unofficial code - tensorflow] [unofficial code - pytorch], [DSOD] DSOD: Learning Deeply Supervised Object Detectors from Scratch | [ICCV' 17] |[pdf] [official code - caffe] [unofficial code - pytorch], [SMN] Spatial Memory for Context Reasoning in Object Detection | [ICCV' 17] |[pdf], [Light-Head R-CNN] Light-Head R-CNN: In Defense of Two-Stage Object Detector | [arXiv' 17] |[pdf] [official code - tensorflow], [Soft-NMS] Improving Object Detection With One Line of Code | [ICCV' 17] |[pdf] [official code - caffe], [YOLO v3] YOLOv3: An Incremental Improvement | [arXiv' 18] |[pdf] [official code - c] [unofficial code - pytorch] [unofficial code - pytorch] [unofficial code - keras] [unofficial code - tensorflow], [ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | [IJCV' 18] |[pdf] [official code - caffe], [SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | [CVPR' 18] |[pdf] [official code - tensorflow], [STDN] Scale-Transferrable Object Detection | [CVPR' 18] |[pdf], [RefineDet] Single-Shot Refinement Neural Network for Object Detection | [CVPR' 18] |[pdf] [official code - caffe] [unofficial code - chainer] [unofficial code - pytorch], [MegDet] MegDet: A Large Mini-Batch Object Detector | [CVPR' 18] |[pdf], [DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | [CVPR' 18] |[pdf] [official code - caffe], [SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | [CVPR' 18] |[pdf], [Relation-Network] Relation Networks for Object Detection | [CVPR' 18] |[pdf] [official code - mxnet], [Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | [CVPR' 18] |[pdf] [official code - caffe], Finding Tiny Faces in the Wild with Generative Adversarial Network | [CVPR' 18] |[pdf], [MLKP] Multi-scale Location-aware Kernel Representation for Object Detection | [CVPR' 18] |[pdf] [official code - caffe], Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation | [CVPR' 18] |[pdf] [official code - chainer], [Fitness NMS] Improving Object Localization with Fitness NMS and Bounded IoU Loss | [CVPR' 18] |[pdf], [STDnet] STDnet: A ConvNet for Small Target Detection | [BMVC' 18] |[pdf], [RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | [ECCV' 18] |[pdf] [official code - pytorch], Zero-Annotation Object Detection with Web Knowledge Transfer | [ECCV' 18] |[pdf], [CornerNet] CornerNet: Detecting Objects as Paired Keypoints | [ECCV' 18] |[pdf] [official code - pytorch], [PFPNet] Parallel Feature Pyramid Network for Object Detection | [ECCV' 18] |[pdf], [Softer-NMS] Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection | [arXiv' 18] |[pdf], [ShapeShifter] ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector | [ECML-PKDD' 18] |[pdf] [official code - tensorflow], [Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | [NIPS' 18] |[pdf] [official code - caffe], [HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | [NIPS' 18] |[pdf], [MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | [NIPS' 18] |[pdf], [SNIPER] SNIPER: Efficient Multi-Scale Training | [NIPS' 18] |[pdf], [M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | [AAAI' 19] |[pdf] [official code - pytorch], [R-DAD] Object Detection based on Region Decomposition and Assembly | [AAAI' 19] |[pdf], [CAMOU] CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild | [ICLR' 19] |[pdf], Feature Intertwiner for Object Detection | [ICLR' 19] |[pdf], [GIoU] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression | [CVPR' 19] |[pdf], Automatic adaptation of object detectors to new domains using self-training | [CVPR' 19] |[pdf], [Libra R-CNN] Libra R-CNN: Balanced Learning for Object Detection | [CVPR' 19] |[pdf], [FSAF] Feature Selective Anchor-Free Module for Single-Shot Object Detection | [CVPR' 19] |[pdf], [ExtremeNet] Bottom-up Object Detection by Grouping Extreme and Center Points | [CVPR' 19] |[pdf] | [official code - pytorch], [C-MIL] C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection Contains three elements: classification answers what and object detection exceeding traditional performance.... 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