[1] WU J X, YANG H. Linear regression-based efficient SVM learning for large-scale classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 26, 2357-2369(2015).

[2] [2] 2林春焕. 弱监督学习下的多标签图像分类［D］. 西安： 西安电子科技大学， 2019.LINC H. Multi-label Image Classification Under Weakly Supervised Learning［D］. Xi'an： Xidian University， 2019. （in Chinese）

[3] SERMANET P, EIGEN D, ZHANG X et al. Overfeat： Integrated recognition， localization and detection using convolutional networks[J]. arXiv preprint(2013).

[4] WEI Y C, XIA W, LIN M et al. HCP： a flexible CNN framework for multi-label image classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 1901-1907(2016).

[5] WANG J, YANG Y, MAO J H et al. CNN-RNN： a unified framework for multi-label image classification[C], 2285-2294(2016).

[6] CHEN T S, XU M X, HUI X L et al. Learning semantic-specific graph representation for multi-label image recognition[C], 522-531(2019).

[7] LANCHANTIN J, WANG T L, ORDONEZ V et al. General multi-label image classification with transformers[C], 16473-16483(2021).

[8] CHENG X, LIN H Z, WU X Y et al. MLTR： multi-label classification with transformer[C], 1-6(2022).

[9] LIU S, ZHANG L, YANG X et al. Query2Label： A Simple Transformer Way to Multi-Label Classification[J]. arXiv preprint arXiv：, 2021.

[10] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[11] ZHU K, WU J X. Residual attention： a simple but effective method for multi-label recognition[C], 184-193(2021).

[12] CUI Y, JIA M L, LIN T Y et al. Class-balanced loss based on effective number of samples[C], 9260-9269(2019).

[13] WU T, HUANG Q Q, LIU Z W et al. Distribution-balanced Loss for Multi-label Classification in Long-tailed Datasets[M]. Computer Vision – ECCV 2020, 162-178(2020).

[14] RIDNIK T, BEN-BARUCH E, ZAMIR N et al. Asymmetric loss for multi-label classification[C], 82-91(2021).

[15] YE J, HE J J, PENG X J et al. Attention-driven Dynamic Graph Convolutional Network for Multi-label Image Recognition[M]. Computer Vision – ECCV 2020, 649-665(2020).

[16] YOU R C, GUO Z Y, CUI L et al. Cross-modality attention with semantic graph embedding for multi-label classification[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12709-12716(2020).

[17] LIN T Y, GOYAL P, GIRSHICK R et al. Focal loss for dense object detection[C], 2999-3007(2017).