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Learning Representations for Automatic Colorization

Published on Oct 24, 20161496 Views

We develop a fully automatic image colorization system. Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations. As many scene elements naturall

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Chapter list

Learning Representations for Automatic Colorization00:00
Colorization/100:07
Colorization/200:11
Colorization/300:15
Colorization/400:25
Colorization/500:29
Colorization/600:37
Colorization/700:46
Colorization/800:52
Colorization/900:59
Manual colorization/101:11
Manual colorization/201:28
Manual colorization/301:31
Manual colorization/401:33
Manual colorization/501:37
Manual colorization/601:45
Manual colorization/701:48
Manual colorization/801:52
Manual colorization/802:05
Manual colorization/902:20
Motivation/102:24
Motivation/202:32
Related work/102:46
Related work/203:01
Related work/303:09
Related work/403:12
Design principles/103:21
Design principles/203:29
Design principles/303:40
Design principles/403:47
Design principles/503:56
Design principles/604:13
Design principles/704:24
Design principles/804:40
Design principles/905:01
Histogram prediction/105:18
Histogram prediction/205:30
Histogram prediction/305:36
Histogram prediction/405:48
Histogram prediction/505:49
Histogram prediction/605:50
Training/105:55
Training/206:03
Training/306:07
Sparse Training/106:17
Sparse Training/206:38
Sparse Training/307:00
Comparison: Previous work/107:08
Comparison: Concurrent work07:29
Example/108:21
Example/208:31
Example/308:42
Example/409:11
Example/509:19
Self-supervision (ongoing work)/109:40
Self-supervision (ongoing work)/209:53
Self-supervision (ongoing work)/310:02
Self-supervision (ongoing work)/410:24
Self-supervision (ongoing work)/510:49
Summary11:13