Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Exploiting Cross Domain Relationships for Target Recognition

Cross domain recognition extracts knowledge from one domain to recognize samples from another domain of interest. The key to solving problems under this umbrella is to find out the latent connections between different domains. In this dissertation, three different cross domain recognition problems are studied by exploiting the relationships between different domains explicitly according to the specific real problems. First, the problem of cross view action recognition is studied. The same action might seem quite different when observed from different viewpoints. Thus, how to use the training samples from a given camera view and perform recognition in another new view is the key point. In this work, reconstructable paths between different views are built to mirror labeled actions from one source view into one another target view for learning an adaptable classifier. The path learning takes advantage of the joint dictionary learning techniques with exploiting hidden information in the seemingly useless samples, making the recognition performance robust and effective. Second, the problem of person re-identification is studied, which tries to match pedestrian images in non-overlapping camera views based on appearance features. In this work, we propose to learn a random kernel forest to discriminatively assign a specific distance metric to each pair of local patches from the two images in matching. The forest is composed by multiple decision trees, which are designed to partition the overall space of local patch-pairs into substantial subspaces, where a simple but effective local metric kernel can be defined to minimize the distance of true matches. Third, the problem of multi-event detection and recognition in smart grid is studied. The signal of multi-event might not be a straightforward combination of some single-event signals because of the correlation among devices. In this work, a concept of ``root-pattern\u27\u27 is proposed that can be extracted from a collection of single-event signals, but also transferable to analyse the constituent components of multi-cascading-event signals based on an over-complete dictionary, which is designed according to the ``root-patterns\u27\u27 with temporal information subtly embedded. The correctness and effectiveness of the proposed approaches have been evaluated by extensive experiments

Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks

Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, Long Short-Term Memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition. This network is capable of selectively focusing on the informative joints in each frame of each skeleton sequence by using a global context memory cell. To further improve the attention capability of our network, we also introduce a recurrent attention mechanism, with which the attention performance of the network can be enhanced progressively. Moreover, we propose a stepwise training scheme in order to train our network effectively. Our approach achieves state-of-the-art performance on five challenging benchmark datasets for skeleton based action recognition