AAAI-20 Accepted Paper List.3.23.20docx

Yu.Ming!Shang!(Bit)*;!Heyan!Huang!(Beijing!Engineering!Research!Center!of!High!Volume!Language!Information Processing!and!Cloud!Computing!Applications,!Department!of ...

AAAI-21 Accepted Paper List.1.29

! 4!! 228:!Rain!Streak!Removal!via!Dual!Graph!Convolutional!Network! Xueyang!Fu,!Qi!Qi,!Yurui!Zhu,!Xinghao!Ding,!Zheng*Jun!Zha!! 233:!RevMan:!Revenue*Aware!Multi*Task ...

AAAI-19 Accepted Papers List for Web

AAAI#19Accepted&Papers&–&Main&Technical&Track& & (The following list includes all accepted papers for the main track of AAAI-19. The list is sorted

AAAI-22 Paper Awards

Operator-Potential Heuristics for Symbolic Search Daniel Fišer, Alvaro Torralba, Joerg Hoffmann AAAI DISTINGUISHED PAPERS In 2021, an additional category of Distinguished Papers was added for special recognition. AAAI-22 Distinguished Papers AlphaHoldem: High-Performance Artificial Intelligence for Heads-Up No-Limit Poker via End-

  Heuristic

Exploring Simple Siamese Representation Learning

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

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

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

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

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