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AutoScaler: Scale-attention Networks for Visual Correspondence
Shenlong Wang
, Linjie Luo
, Ning Zhang
, Jia Li
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Chapter in Book/Report/Conference proceeding
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Conference contribution
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Dive into the research topics of 'AutoScaler: Scale-attention Networks for Visual Correspondence'. Together they form a unique fingerprint.
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Keyphrases
Attention Network
100%
Visual Correspondence
100%
Multi-scale Attention
100%
Autoscaler
100%
Feature Network
40%
State-of-the-art Techniques
20%
Defining Characteristic
20%
Spatial Accuracy
20%
Optical Flow
20%
Receptive Field
20%
Local Features
20%
Shared Features
20%
Computer Vision Problems
20%
Scale Space
20%
Competitive Results
20%
Flow Matching
20%
Daisy
20%
Feature Descriptor
20%
Handcrafted Features
20%
Semantic Matching
20%
Attention Map
20%
Weight Sharing
20%
KITTI
20%
Adaptive Size
20%
Multi-scale Feature Maps
20%
Siamese Framework
20%
Computer Science
Attention (Machine Learning)
100%
local feature
20%
Sharing Feature
20%
feature descriptor
20%
Defining Feature
20%
Feature Map
20%
Computer Vision
20%
Receptive Field
20%