xingyizhou/ExtremeNet: Bottom-up Object Detection by Grouping Extreme and Center Points

Bottom-up Object Detection by Grouping Extreme and Center Points

ExtremeNet: Training and Evaluation Code Code for bottom-up object detection by grouping extreme and center points: Bottom-up Object Detection by Grouping Extreme and Center Points, Xingyi Zhou, Jiacheng Zhuo, Philipp Krähenbühl, CVPR 2019 (arXiv 1901.08043) This project is developed upon the CornerNet code and contains the code from Deep Extreme Cut(DEXTR). Thanks to the original authors! Contact: zhouxy2017@gmail.com. Any questions or discussions are welcomed! Abstract With the advent of deep learning, object detection drifted from a bottom-up to a top-down recognition problem.…

saved by

• Alexander Davies

related reading

• [1711.08189] An Analysis of Scale Invariance in Object Detection - SNIParxiv.org
• An Analysis of Scale Invariance in Object Detection SNIP - Singh_An_Analysis_of_CVPR_2018_paper.pdfopenaccess.thecvf.com
• GitHub - david8862/keras-YOLOv3-model-set: end-to-end YOLOv4/v3/v2 object detection pipeline, implemented on tf.keras with different technologiesgithub.com
• [1810.13128] The Effect of Learning Strategy versus Inherent Architecture Properties on the Ability of Convolutional Neural Networks to Develop Transformation Invariancearxiv.org
• GitHub - Masao-Taketani/FOTS_OCR: TensorFlow Implementation of FOTS, Fast Oriented Text Spotting with a Unified Network.github.com
• 2006.02474.pdfarxiv.org
• 2001.01629.pdfarxiv.org
• Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Imagescis.temple.edu
• Reference for ultralytics/trackers/bot_sort.py | Ultralytics Docsdocs.ultralytics.com
• Convex hull, Image processing, Image Classification, Image retrieval, Shape detectiondiva-portal.org
• Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxesvirtualhumans.mpi-inf.mpg.de
• [2201.03545] A ConvNet for the 2020sarxiv.org