近日,Swin Transformer拿到2021 ICCV Best Paper了!MSRA再一次拿到Best Paper,上一次可以追溯到ResNet,巧合的是,这一次也是通用骨干网络模型。 放一张图感受一下SwinT的威力 语义分割在ADE20K上刷到53.5 mIoU,超过之前SOTA大概4.5 mIoU! 来源: https://paperswithcode.com/sota/semantic-segmentation-on-ade20k-val ...
Bookstores are drawing in customers with something new For Subscribers Valerie Plesch/The Washington Post/Getty Images ‘A great friend’: Audio undercuts Trump US attorney nominee’s disavowal of alleged Nazi sympathizer China’s electric vehicle industry is preparing to take on the world. Is Ame...
Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS, 2012. 物体检测(Object Detection) - PVANET [paper](arxiv.org/pdf/1608.0802) [Code](sanghoon/pva-faster-rcnn) - Kye-Hyeon Kim, Sanghoon Hong, Byungseok Roh, Yeongjae Cheon, Minje Park, PVANET: Deep but Light...
code for the paper "In Conclusion Not Repetition:Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point Processes" - thinkwee/DPP_CNN_Summarization
Status:Archive (code is provided as-is, no updates expected) pixel-cnn++ This is a Python3 /Tensorflowimplementation ofPixelCNN++, as described in the following paper: PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications, by Tim Salimans, Andrej...
paper: http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf github(Caffe): https://github.com/xiaolonw/adversarial-frcnn Faster R-CNN Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arxiv: http://arxiv.org/abs/1506.01497 gitxiv: http://www.git...
一、Transformer带来的技术进步程度还不够高 引用 @花花有句话很形象：“我认为目前transformer在CV领域...
In this paper, we discuss the possibility of learning deep network structures automatically. Note that the number of possible network structures increases exponentially with the number of layers in the network, which inspires us to adopt the genetic algorithm to efficiently traverse this large search ...
R-CNN, or Regions with CNN Features, is an object detection model that uses high-capacity CNNs to bottom-up region proposals in order to localize and segment objects. It uses selective search to identify a number of bounding-box object region candidates
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