Adversarial Complementary Learning for Weakly Supervised Object Localization

CVPR 2018 Xiaolin ZhangYunchao WeiJiashi FengYi YangThomas Huang

In this work, we propose Adversarial Complementary Learning (ACoL) to automatically localize integral objects of semantic interest with weak supervision. We first mathematically prove that class localization maps can be obtained by directly selecting the class-specific feature maps of the last convolutional layer, which paves a simple way to identify object regions... (read more)

PDF Abstract CVPR 2018 PDF CVPR 2018 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Weakly-Supervised Object Localization ILSVRC 2016 GoogLeNet-ACoL Top-5 Error 42.58 # 2

Methods used in the Paper


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