TY  - CONF
AU  - Vacit Oguz Yazici
AU  - Abel Gonzalez-Garcia
AU  - Arnau Ramisa
AU  - Bartlomiej Twardowski
AU  - Joost Van de Weijer
A2  - CVPR
PY  - 2020//
TI  - Orderless Recurrent Models for Multi-label Classification
BT  - 33rd IEEE Conference on Computer Vision and Pattern Recognition
N2  - Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task. Current approaches sort labels according to their frequency, typically ordering them in either rare-first or frequent-first. These imposed orderings do not take into account that the natural order to generate the labels can change for each image, e.g.\ first the dominant object before summing up the smaller objects in the image. Therefore, in this paper, we propose ways to dynamically order the ground truth labels with the predicted label sequence. This allows for the faster training of more optimal LSTM models for multi-label classification. Analysis evidences that our method does not suffer from duplicate generation, something which is common for other models. Furthermore, it outperforms other CNN-RNN models, and we show that a standard architecture of an image encoder and language decoder trained with our proposed loss obtains the state-of-the-art results on the challenging MS-COCO, WIDER Attribute and PA-100K and competitive results on NUS-WIDE.
UR  - https://arxiv.org/abs/1911.09996
L1  - http://refbase.cvc.uab.es/files/YGR2020.pdf
N1  - LAMP; 600.109; 601.309; 600.141; 600.120
ID  - Vacit Oguz Yazici2020
ER  -