박사

Improving object detection in hard conditions of scale, occlusion and label

노준혁 2020년
논문상세정보
' Improving object detection in hard conditions of scale, occlusion and label' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 응용 물리
  • Computer Vision
  • Deep Learning
  • Object Detection
  • Pedestrian Detection
  • Weakly Supervised Object Localization
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
2,616 0

0.0%

' Improving object detection in hard conditions of scale, occlusion and label' 의 참고문헌

  • ¡°Learning Deep Features for Discriminative Localization
    [2016]
  • eature Selective Anchor-Free Module for SingleShot Object Detection
    [2019]
  • [90] F. Yu and V. Koltun. Multi-Scale Context Aggregation by Dilated Convolutions. arXiv:1511.07122, 2015.
  • [74] K. Simonyan and A. Zisserman. Very Deep Convolutional Networks for LargeScale Image Recognition. arXiv:1409.1556, 2014.
  • [70] O. Russakovsky, J. Deng, H. SU, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, and L. Fei-Fei. Imagenet Large Scale Visual RecognitionChallenge. IJCV, 115(3):211–252, 2015.
    115 ( 3 ) :211–252 [2015]
  • [68] J. Redmon and A. Farhadi. YOLO9000: Better, Faster, Stronger. In CVPR, 2017.
  • [66] P. O. Pinheiro and R. Collobert. From Image-Level to Pixel-Level Labeling With Convolutional Networks. In CVPR, 2015.
  • [57] D. G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. IJCV, 60(2):91–110, 2004.
  • [45] H. Law and J. Deng. CornerNet: Detecting Objects as Paired Keypoints. In ECCV, 2018.
  • [33] G. E. Hinton and R. R. Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks. science, 313(5786):504–507, 2006.
  • [31] K. He, G. Gkioxari, P. Dollar, and R. Girshick. Mask R-CNN. In ´ ICCV, 2017.
    [2017]
  • [28] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A.Courville, and Y. Bengio. Generative Adversarial Nets. In NIPS, 2014.
    [2014]
  • [21] M. Everingham, L. Van Gool,C. K. Williams, J. Winn, and A. Zisserman. The Pascal Visual ObjectClasses (VOC)Challenge. IJCV, 88(2):303–338, 2010.
    88 ( 2 ) :303–338 [2010]
  • [1] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane,´ R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, ´ O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015. Software available from tensorflow.org.
  • [15] N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR, pages 886–893, 2005.
  • You only look once : Unified , real-time object detection
    [2016]
  • Two-Phase Learning for Weakly Supervised Object Localization
    [2017]
  • The cityscapes dataset for semantic urban scene understanding
    [2016]
  • The Caltech-UCSD Birds-200-2011 Dataset .
    [2011]
  • Tensorpack
    https : //github.com/tensorpack/ [2016]
  • Tell me where to look : Guided attention inference network
    [2018]
  • Task-Driven Super Resolution : Object Detection in Low-resolution Images
    [2018]
  • TS2C : Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection
    [2018]
  • Ssd : Single shot multibox detector
    [2016]
  • SqueezeDet : Unified , Small , Low Power FullyConvolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
    [2016]
  • Squeeze-and-excitation networks
    [2018]
  • Speed/accuracy trade-offs for mod ernConvolutional object detectors
    [2017]
  • Small Object Detection Using Deep Feature Pyramid Networks
    [2018]
  • Single-pedestrian Detection Aided by Multi-pedestrian Detection
    [2013]
  • Semantic image segmentation with deepConvolutional nets and fullyConnectedCrfs
    arXiv:1412.7062 [2014]
  • Self-Taught Object Localization With Deep Networks
    [2016]
  • Self-Produced Guidance for Weakly-Supervised Object Localization
    [2018]
  • Self-Erasing Network for Integral Object Attention
    [2018]
  • SOD-MTGAN : Small Object Detection via Multi-Task Generative Adversarial Network
    [2018]
  • SINet : A ScaleinsensitiveConvolutional Neural Network for Fast Vehicle Detection
    [2019]
  • Rich feature hierarchies for accurate object detection and semantic segmentation
    [2014]
  • RethinkingClass Activation Maps for Weakly Supervised Object Localization
    [2020]
  • Relation Networks for Object Detection
    [2018]
  • R-fcn : Object detection via region-based fullyConvolutional networks
    [2016]
  • Pyramid scene parsing network
    [2017]
  • Pushing the Limits of DeepCNNs for Pedestrian Detection
    [2017]
  • Perceptual Generative Adversarial Networks for Small Object Detection
    [2017]
  • Pedestrian Detection : An Evaluation of the State of the Art
    34 ( 4 ) :743–761 [2012]
  • P. Perona , and S. Belongie . Integral Channel Features
    [2009]
  • P. Dollar , and ´ C. L. Zitnick . Microsoft COCO : Common Objects in Context
    [2014]
  • Object Region Mining With Adversarial Erasing : A Simple Classification to Semantic Segmentation Approach
    [2017]
  • Object Detection with Discriminatively Trained Part-Based Models
    32 ( 9 ) :1627–1645 , [2010]
  • Object Detection Meets Knowledge Graphs
    [2017]
  • Network in network
    [2014]
  • Multi-cue Pedestrian Classification with Partial Occlusion Handling
    [2010]
  • MobileNets : Efficient Convolutional Neural Networks for Mobile Vision Applications
    [2017]
  • Is Object Localization for Free ? – Weakly-Supervised Learning With Convolutional Neural Networks
    [2015]
  • Is Faster R-CNN Doing Well for Pedestrian Detection ?
    [2016]
  • Inside-Outside Net : Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
    [2016]
  • Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors
    [2018]
  • Improved Techniques for Weakly-Supervised Object Localization
    [2018]
  • How Far Are We from Solving Pedestrian Detection ?
    [2016]
  • Hide-And-Seek : Forcing a Network to Be Meticulous for Weakly-Supervised Object and Action Localization
    [2017]
  • Handling Occlusions with Franken-Classifiers
    [2013]
  • GradCam++ : Improved Visual Explanations for Deep Convolutional Networks
    [2018]
  • Grad-cam : Visual explanations from deep networks via gradient-based localization .
    [2017]
  • Going Deeper with Convolutions
    [2015]
  • Fused DNN : A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection
    [2017]
  • FoveaBox : Beyond Anchor-based Object Detector
    [2019]
  • Focal loss for dense object detection
    [2017]
  • Finding Tiny Faces
    [2017]
  • Feature Super-Resolution : Make Machine See More Clearly
    [2018]
  • Faster r-cnn : Towards real-time object detection with region proposal networks
    [2015]
  • Fast R-CNN
    [2015]
  • FCOS : Fully Convolutional One-Stage Object Detection
    [2019]
  • Evaluation of Image Resolution and Super-Resolution on Face Recognition Performance
    23 ( 1 ) :75–93 [2012]
  • EnhanceNet : Single Image Super¨ Resolution through Automated Texture Synthesis
    [2017]
  • Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
    [2018]
  • Detection and Tracking of Occluded People
    110 ( 1 ) :58–69 [2014]
  • Detecting Small Signs from Large Images
    [2017]
  • Deep Residual Learning for Image Recognition
    [2016]
  • Deep Learning Strong Parts for Pedestrian Detection
    [2015]
  • Deep Learning
    [2015]
  • DANet : Divergent Activation for Weakly supervised Object Localization
    [2019]
  • CornerNet-Lite : Efficient Keypoint Based Object Detection
    [2019]
  • Context-aware Single-Shot Detector
    [2018]
  • CityPersons : A Diverse Dataset for Pedestrian Detection
    [2017]
  • CenterNet : Keypoint Triplets for Object Detection
    [2019]
  • Cascade r-cnn : Delving into high quality object detection
    [2018]
  • Cascade R-CNN : High Quality Object Detection and Instance Segmentation
    [2019]
  • Beyond Skip Connections : Top-Down Modulation for Object Detection
    [2016]
  • Better to Follow , Follow to Be Better : Towards Precise Supervision of Feature Super-Resolution for Small Object Detection
    [2019]
  • Batch Normalization : Accelerating Deep Network Training by Reducing Internal Covariate Shift
    [2015]
  • B. Hariharan , and S. Belongie . Feature Pyramid Networks for Object Detection
    [2017]
  • Auto-WEKA : Combined selection and hyperparameter optimization of classification algorithms .
    [2013]
  • Attention-Based Dropout Layer for Weakly Supervised Object Localization
    [2019]
  • An Analysis of Scale Invariance in Object Detection - SNIP
    [2018]
  • Adversarial Complementary Learning for Weakly Supervised Object Localization
    [2018]
  • Acquisition of Localization Confidence for Accurate Object Detection
    [2018]
  • A. Ranga , A. Tyagi , and A. C. Berg
    [2017]
  • A unified multi-scale deep convolutional neural network for fast object detection
    [2016]
  • A Tree-based Context Model for Object Recognition
    34 ( 2 ) :240–252 [2011]
  • A Discriminative Deep Model for Pedestrian Detection with Occlusion Handling
    [2012]