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.
Relevant and irrelevant web images collected by tag-based image retrieval have been employed as loosely labeled training data for learning SVM classifiers for image categorization by only using the visual features. In this work, we propose a new image categorization method by incorporating the textual features extracted from the surrounding textual descriptions (tags, captions, categories, etc.) as privileged information and simultaneously coping with noise in the loose labels of training web images. When the training and test samples come from different datasets, our proposed method can be further extended to reduce the data distribution mismatch by adding a regularizer based on the Maximum Mean Discrepancy (MMD) criterion. Our comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed methods for image categorization and image retrieval by exploiting privileged information from web data. In the Afterword to the second edition of the book "Estimation of Dependences Based on Empirical Data" by V. Vapnik, an advanced learning paradigm called Learning Using Hidden Information (LUHI) was introduced. This Afterword also suggested an extension of the SVM method (the so called SVM γ + method) to implement algorithms which address the LUHI paradigm (Vapnik, 1982-2006, Sections 2.4.2 and 2.5.3 of the Afterword). See also (Vapnik, Vashist, & Pavlovitch, 2008, 2009) for further development of the algorithms. In contrast to the existing machine learning paradigm where a teacher does not play an important role, the advanced learning paradigm considers some elements of human teaching. In the new paradigm along with examples, a teacher can provide students with hidden information that exists in explanations, comments, comparisons, and so on. This paper discusses details of the new paradigm 1 and corresponding algorithms, introduces some new algorithms, considers several specific forms of privileged information, demonstrates superiority of the new learning paradigm over the classical learning paradigm when solving practical problems, and discusses general questions related to the new ideas. Many computer vision problems have an asymmetric distribution of information between training and test time. In this work, we study the case where we are given additional information about the training data, which however will not be available at test time. This situation is called learning using privileged information (LUPI). We introduce two maximum-margin techniques that are able to make use of this additional source of information, and we show that the framework is applicable to several scenarios that have been studied in computer vision before. Experiments with attributes, bounding boxes, image tags and rationales as additional information in object classification show promising results. In some pattern analysis problems, there exists expert knowledge, in addition to the original data involved in the classification process. The vast majority of existing approaches simply ignore such auxiliary (privileged) knowledge. Recently a new paradigm-learning using privileged information-was introduced in the framework of SVM+. This approach is formulated for binary classification and, as typical for many kernel-based methods, can scale unfavorably with the number of training examples. While speeding up training methods and extensions of SVM+ to multiclass problems are possible, in this paper we present a more direct novel methodology for incorporating valuable privileged knowledge in the model construction phase, primarily formulated in the framework of generalized matrix learning vector quantization. This is done by changing the global metric in the input space, based on distance relations revealed by the privileged information. Hence, unlike in SVM+, any convenient classifier can be used after such metric modification, bringing more flexibility to the problem of incorporating privileged information during the training. Experiments demonstrate that the manipulation of an input space metric based on privileged data improves classification accuracy. Moreover, our methods can achieve competitive performance against the SVM+ formulations. This paper describes a new paradigm of machine learning, in which Intelligent Teacher is involved. During training stage, Intelligent Teacher provides Student with information that contains, along with classification of each example, additional privileged information (for example, explanation) of this example. The paper describes two mechanisms that can be used for significantly accelerating the speed of Student's learning using privileged information: (1) correction of Student's concepts of similarity between examples, and (2) direct Teacher-Student knowledge transfer.