Domain adaptation, which transfers the knowledge from label-rich source domain to unlabeled target domains, is a challenging task in machine learning. The prior domain adaptation methods focus on pairwise adaptation assumption with a single source and a single target domain, while little work concerns the scenario of one source domain and multiple target domains. Applying pairwise adaptation methods to this setting may be suboptimal, as they fail to consider the semantic association among multiple target domains. In this work we propose a deep semantic information propagation approach in the novel context of multiple unlabeled target domains and one labeled source domain. Our model aims to learn a unified subspace common for all domains with a heterogeneous graph attention network, where the transductive ability of the graph attention network can conduct semantic propagation of the related samples among multiple domains. In particular, the attention mechanism is applied to optimize the relationships of multiple domain samples for better semantic transfer. Then, the pseudo labels of the target domains predicted by the graph attention network are utilized to learn domain-invariant representations by aligning labeled source centroid and pseudo-labeled target centroid. We test our approach on four challenging public datasets, and it outperforms several popular domain adaptation methods.

The clustering methods have absorbed even-increasing attention in machine learning and computer vision communities in recent years. In this article, we focus on the real-world applications where a sample can be represented by multiple views. Traditional methods learn a common latent space for multiview samples without considering the diversity of multiview representations and use K-means to obtain the final results, which are time and space consuming. On the contrary, we propose a novel end-to-end deep multiview clustering model with collaborative learning to predict the clustering results directly. Specifically, multiple autoencoder networks are utilized to embed multi-view data into various latent spaces and a heterogeneous graph learning module is employed to fuse the latent representations adaptively, which can learn specific weights for different views of each sample. In addition, intraview collaborative learning is framed to optimize each single-view clustering task and provide more discriminative latent representations. Simultaneously, interview collaborative learning is employed to obtain complementary information and promote consistent cluster structure for a better clustering solution. Experimental results on several datasets show that our method significantly outperforms several state-of-the-art clustering approaches.

In recent years, person re-identification (re-ID) has achieved relatively good performance, benefiting from the revival of deep neural networks. However, due to the existence of domain bias which refers to the different data distributions between two domains, it remains challenging to directly deploy a model trained on a labeled source domain to a target domain only with unlabeled data available. In this paper, a Self-Training with Progressive Representation Enhancement (PREST) framework, which comprises a multi-scale self-training method and a view-invariant representation learning module, is proposed to promote re-ID performance on the target domain in an unsupervised manner. More specifically, multi-scale representations, including the global body and local parts of pedestrian images, are utilized to obtain pseudo-labels. Then, some images are selected according to the pseudo-labels to create a new dataset for supervising the fine-tuning process, which is operated iteratively to progressively promote the performance. Furthermore, to mitigate the influence of different styles among sub-domains, in cases where a single sub-domain is captured by one camera, a classifier with a gradient reverse layer is first employed to learn view-invariant representation for pedestrian images with the same identity taken by different cameras; this can further enhance the reliability of the predicted labels and improve the cross-domain re-ID performance. Extensive experiments on three large-scale re-ID datasets demonstrate that our framework achieves significantly better performance than existing approaches.

As an important task in the process of image understanding and analysis, saliency detection has recently received increasing attention. In this paper, we propose an efficient multiple self-weighted graph-based manifold ranking method to construct salient maps. First, we extract several different views of featuresfromsuperpixels,andgenerateoriginalsalientregionsas foregroundand backgroundcuesusing boundary informationvia multiplegraph-basedmanifoldranking.Furthermore,asetofhyperparameters is learned to distinguish the importance between different graphs, which can be viewed as an adaptive weighting of each graph, and then a centroid graph is generated by using these self-weighted multiple graphs. An iterative algorithm is proposed to simultaneously optimize the hyperparameters as well as the centroid graph connection. Thus, an ideal centroid graph can be obtained, offering a more clear profile of the separated structure. Finally, the saliency maps can be produced with an approximate binary image from the manifold ranking. Extensive experiments have demonstrated our method consistently achieves superior detection performance than several state-of-the-arts.

Class-Incremental Unsupervised Domain Adaptation (CI-UDA) requires the model can continually learn several steps containing unlabeled target domain samples, while the source-labeled dataset is available all the time. The key to tackling CI-UDA problem is to transfer domain-invariant knowledge from the source domain to the target domain, and preserve the knowledge of the previous steps in the continual adaptation process. However, existing methods introduce much biased source knowledge for the current step, causing negative transfer and unsatisfying performance. To tackle these problems, we propose a novel CI-UDA method named Pseudo-Label Distillation Continual Adaptation (PLDCA). We design Pseudo-Label Distillation module to leverage the discriminative information of the target domain to filter the biased knowledge at the class- and instance-level. In addition, Contrastive Alignment is proposed to reduce domain discrepancy by aligning the class-level feature representation of the confident target samples and the source domain, and exploit the robust feature representation of the unconfident target samples at the instance-level. Extensive experiments demonstrate the effectiveness and superiority of PLDCA.

The goal of zero-shot learning (ZSL) is to recognize objects from unseen classes correctly without corresponding training samples. The existing ZSL methods are trained on a set of predefined classes and do not have the ability to learn from a stream of training data. However, in many real-world applications, training data are collected incrementally; this is one of the main reasons why ZSL methods cannot be applied to certain real-world situations. Accordingly, in order to handle practical learning tasks of this kind, we introduce a novel ZSL setting, referred to as incremental ZSL (IZSL), the goal of which is to accumulate historical knowledge and alleviate Catastrophic Forgetting to facilitate better recognition when incrementally trained on new classes. We further propose a novel method to realize IZSL, which employs a generative replay strategy to produce virtual samples of previously seen classes. The historical knowledge is then transferred from the former learning step to the current step through joint training on both real new and virtual old data. Subsequently, a knowledge distillation strategy is leveraged to distill the knowledge from the former model to the current model, which regularizes the training process of the current model. In addition, our method can be flexibly equipped with the most generative-ZSL methods to tackle IZSL. Extensive experiments on three challenging benchmarks indicate that the proposed method can effectively tackle the IZSL problem effectively, while the existing ZSL methods fail.