MIML-FCN+: Multi-Instance Multi-Label Learning via Fully Convolutional Networks With Privileged Information

Hao Yang, Joey Tianyi Zhou, Jianfei Cai, Yew Soon Ong; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1577-1585

Abstract


Multi-instance multi-label (MIML) learning has many interesting applications in computer visions, including multi-object recognition and automatic image tagging. In these applications, additional information such as bounding-boxes, image captions and descriptions is often available during training phrase, which is referred as privileged information (PI). However, as existing works on learning using PI only consider instance-level PI (privileged instances), they fail to make use of bag-level PI (privileged bags) available in MIML learning. Therefore, in this paper, we propose a two-stream fully convolutional network, named MIML-FCN+, unified by a novel PI loss to solve the problem of MIML learning with privileged bags. Compared to the previous works on PI, the proposed MIML-FCN+ utilizes the readily available privileged bags, instead of hard-to-obtain privileged instances, making the system more general and practical in real world applications. As the proposed PI loss is convex and SGD-compatible and the framework itself is a fully convolutional network, MIML FCN+ can be easily integrated with state-of-the-art deep learning networks. Moreover, the flexibility of convolutional layers allows us to exploit structured correlations among instances to facilitate more effective training and testing. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed MIML-FCN+, outperforming state-of-the-art methods in the application of multi-object recognition.

Related Material


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[bibtex]
@InProceedings{Yang_2017_CVPR,
author = {Yang, Hao and Tianyi Zhou, Joey and Cai, Jianfei and Soon Ong, Yew},
title = {MIML-FCN+: Multi-Instance Multi-Label Learning via Fully Convolutional Networks With Privileged Information},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}