cvpr

Weakly Supervised Semantic Segmentation list 文章转自Github：https://github.com/JackieZhangdx/WeakSupervisedSegmentationList ======================================================== This repository contains lists of state-or-art weakly supervised semantic segmentation works. Papers and resources are listed below according to supervision types. There are some personal views and notes, just ignore if not interested. Last update 2019/4 Paper list instance box one-shot others Resources some unsupervised segment proposal methods and datasets here . CVPR 2018 Tutorial : WSL web&ppt , Part1 , Part2

Image-to-Image papers A collection of image-to-image papers. Papers are ordered in arXiv first version submitting time (if applicable). Feel free to send a PR or issue. TOC Supervised Unsupervised Unsupervised - General Unsupervised - Attention/Instance guided Unsupervised - Many-to-many (Attributes) Unsupervised - Disentangled (and/or Exemplar guided) To be classified Supervised Note Model Paper Conference paper link code link pix2pix Image-to-Image Translation with Conditional Adversarial Networks CVPR 2017 1611.07004 junyanz/pytorch-CycleGAN-and-pix2pix texture guided TextureGAN TextureGAN:

CVPR 2016 MC-ZSL: Zeynep Akata, Mateusz Malinowski, Mario Fritz, Bernt Schiele. “Multi-Cue Zero-Shot Learning With Strong Supervision.” CVPR (2016). [ pdf ] [code] LATEM: Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, Bernt Schiele. “Latent Embeddings for Zero-Shot Classification.” CVPR (2016). [ pdf ][ code ] LIM: Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, Anton van den Hengel. “Less Is More: Zero-Shot Learning From Online Textual Documents With Noise Suppression.” CVPR (2016). [ pdf ] SYNC: Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei Sha. “Synthesized

awesome-point-cloud-analysis for anyone who wants to do research about 3D point cloud. If you find the awesome paper/code/dataset or have some suggestions, please contact linhua2017@ia.ac.cn. Thanks for your valuable contribution to the research community 😃 - Recent papers (from 2017) Keywords dat. : dataset   |   cls. : classification   |   rel. : retrieval   |   seg. : segmentation det. : detection   |   tra. : tracking   |   pos. : pose   |   dep. : depth reg. : registration   |   rec. : reconstruction   |   aut. : autonomous driving oth. : other, including normal-related, correspondence,

CVPR 2019 CADA-VAE: Edgar Schönfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, Zeynep Akata. “Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders.” CVPR (2019). [pdf] [code] GDAN: He Huang, Changhu Wang, Philip S. Yu, Chang-Dong Wang. “Generative Dual Adversarial Network for Generalized Zero-shot Learning.” CVPR (2019). [pdf] [code] DeML: Binghui Chen, Weihong Deng. “Hybrid-Attention based Decoupled Metric Learning for Zero-Shot Image Retrieval.” CVPR (2019). [pdf] [code] Gzsl-VSE: Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama. “Generalized Zero-Shot Recognition

视频研究入门经典 Labor-Free Video Concept Learningby Jointly Exploiting Web Videos and Images ​ intro: CVPR 2016 ​ intro: Lead–Exceed Neural Network (LENN), LSTM ​ paper: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/CVPR16_webly_final.pdf Video Fill in the Blank with Merging LSTMs ​ intro: for Large Scale Movie Description and Understanding Challenge (LSMDC) 2016, "Movie fill-in-the-blank" Challenge, UCF_CRCV ​ intro: Video-Fill-in-the-Blank (ViFitB) ​ arxiv: https://arxiv.org/abs/1610.04062 Video Pixel Networks ​ intro: Google DeepMind ​ arxiv: https://arxiv.org/abs/1610.00527

在目标检测的研究过程中，深度学习一直占居着主要的位置。通过搭建不同的网络模型，对当前两大主流开源数据集PASCALVOC和IMAGENET进行测试，已然成了一种新风向。 作为计算机视觉三大顶会：CVPR，ICCV，ECCV，每年都会有该方向的最新成果。 接下来汇总一下，以便需要时查看： 2014 ----------------------------------------------------------------- RCNN 58.5 CVPR(2014) SPPNET 59.2 ECCV(2014) ----------------------------------------------------------------- 2015 ----------------------------------------------------------------- Fast-RCNN 70 ICCV(2015) Faster-RCNN 73.2 NIPS(2015) ----------------------------------------------------------------- 2016 ----------------------------------------------------------------- YOLOv1 66.4

1. Verification and Certification of Neural Networks 神经网络的验证与认证 2. Automated Analysis of Marine Video for Environmental Monitoring 海洋环境监测视频的自动分析 3. Understanding Subjective Attributes of Data: Focus on Fashion and Subjective Search 理解数据的主观属性：关注时尚和主观搜索 4. Deep-Vision: New Frontiers and Advances in theory in Deep Learning for Computer Vision (6th edition) 深度视觉：计算机视觉深度学习理论的新前沿与进展（第6版） 5. Women in Computer Vision 计算机视觉领域的女性 6. Image Matching: Local Features and Beyond 图像匹配：局部特征及其应用 7. Long-Term Visual Localization under Changing Conditions 变化条件下的长期视觉定位 8. Language and Vision 语言与视觉 9. Computational

Table of contents Introduction Tutorials Survey papers Benchmark datasets Fine-grained image recognition Fine-grained recognition by localization-classification subnetworks Fine-grained recognition by end-to-end feature encoding Fine-grained recognition with external information Fine-grained recognition with web data / auxiliary data Fine-grained recognition with multi-modality data Fine-grained recognition with humans in the loop Fine-grained image retrieval Unsupervised with pre-trained models Supervised with metric learning Fine-grained image generation Generating from fine-grained image

1.深度学习文本检测识别精选资源汇总github网址 https://github.com/hwalsuklee/awesome-deep-text-detection-recognition 2. DBLP （DataBase systems and Logic Programming）是计算机领域内对研究的成果以作者为核心的一个计算机类英文文献的集成数据库系统，可在这里搜索相应期刊及其论文，搜索关键此有 AAAI, ICCV, TPAMI（顶会）,CVPR,arXiv,IJCV,PR,PRL,NC(neural computing),ICFHR(每年3\4月份的会议，多为手写识别方向)，ICDAR（多为场景文本）。 待更新～～ 来源： https://www.cnblogs.com/KAKAFEIcoffee/p/11804236.html