目錄[1] Context Prior for Scene Segmentation[2] Deep Stereo using Adaptive Thin Volume Representation with Uncertaint
目錄[1] Bi-directional Relationship Inferring Network for Referring Image Segmentation[2] A Real-Time Cross-modality
目錄CC JJY S[1] Normalized and Geometry-Aware Self-Attention Network for Image Captioning[2] Cops-Ref: A new Dataset
目錄[1] CVPR2020_A Unified Optimization Framework for Low-Rank Inducing Penaltie[2] CVPR2020_Automatic Neural Network
普通的機器學習分類迴歸問題都是點估計，即模型給出的輸出是一個real value，或者是各個類別的probability。 但是模型對於不同點的估計，確信度應該是不同的。對於和以往出現過樣本非常相似的點，給出的預測確信度比較高，對於和以往
Paper Reading Note URL: Towards Principled Methods for Training Generative Adversarial Networks Wasserstein GAN T
論文 資料1 基於度量的元學習（metric-based meta-learning）如今已成爲少樣本學習研究過程中被廣泛應用的一個範式。這篇文章提出利用交叉模態信息（cross-modal information）來進一步加強現有的度量
作者：一顆檸檬味的橙子 鏈接：https://zhuanlan.zhihu.com/p/105717426 來源：知乎 著作權歸作者所有。商業轉載請聯繫作者獲得授權，非商業轉載請註明出處。 來源：NeurIPS 2019 文章題目：C
(NIPS 2014) Generative Adversarial Nets Paper: https://papers.nips.cc/paper/5423-generative-adversarial-nets Code
(CVPR 2017) Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing P
Towards Real World Human Parsing: Multiple-Human Parsing in the Wild Paper: https://arxiv.org/pdf/1705.07206.pdf
(ICLR 2016) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Paper: ht
(ICLR 2017) Learning to Remember Rare Events Paper: https://openreview.net/pdf?id=SJTQLdqlg Code: https://github.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017.04) 1、使用 depthwise conv + p
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