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Unsupervised Visual Representation Learning By

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Exploring Simple Siamese Representation Learning

openaccess.thecvf.com

Exploring Simple Siamese Representation Learning Xinlei Chen Kaiming He Facebook AI Research (FAIR) Abstract Siamese networks have become a common structure in various recent models for unsupervised visual representa-tion learning. These models maximize the similarity be-tween two augmentations of one image, subject to certain

  Into, Learning, Visual, Representation, Unsupervised, Representation learning, Unsupervised visual representa tion learning, Representa

DetCo: Unsupervised Contrastive Learning for Object Detection

openaccess.thecvf.com

Self-supervised learning of visual representation is an es-sential problem in computer vision, facilitating many down-stream tasks such as image classification, object detection, and semantic segmentation [23,35,43]. It aims to provide models pre-trained on large-scale unlabeled data for down-stream tasks. Previous methods focus on designing ...

  Learning, Visual, Representation, Unsupervised, Visual representation

AAAI-22 Accepted Papers — Main Technical Track

aaai.org

243: Unsupervised Representation for Semantic Segmentation by Implicit Cycle-Attention Contrastive Learning Bo Pang, Yizhuo Li, Yifan Zhang, Gao Peng, Jiajun Tang, Kaiwen Zha, Jiefeng Li, Cewu Lu 246: OneRel: Joint Entity and Relation Extraction with One Module in One Step Yu-Ming Shang, Heyan Huang, Xian-Ling Mao

  Learning, Representation, Unsupervised, Unsupervised representation

DeepFace: Closing the Gap to Human-Level Performance in …

www.cs.toronto.edu

compact face representation, in sheer contrast to the shift toward tens of thousands of appearance features in other re-cent systems [5,7,2]. The proposed system differs from the majority of con-tributions in the field in that it uses the deep learning (DL) framework [3,21] in lieu of well engineered features. DL is

  Learning, Representation

Learning Deep Architectures for AI - Université de Montréal

www.iro.umontreal.ca

rally provide such sharing and re-use of components: the low-level visual features (like edge detectors) and intermediate-level visual features (like object parts) that are useful to detect MAN are also useful for a large group of other visual tasks. In addition, learning about a large set of interrelated concepts might provide a

  Learning, Visual

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