  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.
