xingyizhou/ExtremeNet: Bottom-up Object Detection by Grouping Extreme and Center Points
github.com · 853 words · saved by 1 readers
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
related reading
- GitHub - david8862/keras-YOLOv3-model-set: end-to-end YOLOv4/v3/v2 object detection pipeline, implemented on tf.keras with different technologiesgithub.com
- [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 - Masao-Taketani/FOTS_OCR: TensorFlow Implementation of FOTS, Fast Oriented Text Spotting with a Unified Network.github.com
- Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Imagescis.temple.edu
- Enhancing 3D Object Detection with 2D Detection-Guided Query Anchorsarxiv.org
- 2001.01629.pdfarxiv.org
- ImageNet Classification with Deep Convolutional Neural Networksproceedings.neurips.cc
- [2201.03545] A ConvNet for the 2020sarxiv.org
- Learning 3D object-centric representation through predictionarxiv.org
- Deep Anomaly Detection with Outlier Exposurearxiv.org
- 2006.02474.pdfarxiv.org