Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up

CVPR 2019 Weifeng GeXiangru LinYizhou Yu

Given a training dataset composed of images and corresponding category labels, deep convolutional neural networks show a strong ability in mining discriminative parts for image classification. However, deep convolutional neural networks trained with image level labels only tend to focus on the most discriminative parts while missing other object parts, which could provide complementary information... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Fine-Grained Image Classification CUB-200-2011 Stacked LSTM Accuracy 90.4% # 1

Methods used in the Paper