Open-world specific emitter identification (SEI) is a practical but challenging task, because it requires accurate identification of both known and unknown emitters in open environments with channel variations. However, traditional closed-set SEI methods suffer from severe performance degradation in open-world environments since 1) closed-set SEI models would misclassify unknown specific emitters (SEs) as known ones, and 2) channel variations would lead to distribution shifts in the radio frequency (RF) fingerprint of known SEs. It is challenging to address both issues, as they coexist and interact with each other. In this paper, we propose a novel prototype matching with domain alignment framework for open-world SEI, which designed to simultaneously address the challenges of unknown SE identification and channel variation. The proposed framework first pretrains a feature extractor and then trains the pretrained feature extractor and an extended open-set classifier via the self-training paradigm. Towards the former, a partial domain alignment strategy is introduced to mitigate the impact of channel variations. Towards the latter, a prototype matching-based pseudo-labeling strategy is proposed to generate reliable pseudo-labels, facilitating subsequent fine-grained identification of unknown SEs. An open-set classification loss is introduced later to help the classifier avoid classifying unknown SEs as known ones. Experiments conducted on a public WiFi dataset demonstrate the superiority and robustness of the proposed method, illustrating its potential application prospects in open-world environments.

Unsupervised domain adaptation (UDA) transfers knowledge from a labeled source domain to an unlabeled target domain on cross-domain object recognition by reducing a distribution discrepancy between the source and target domains (interdomain discrepancy). Prevailing methods on UDA were presented based on the premise that target data are collected in advance. However, in online scenarios, the target data often arrive in a streamed manner, such as visual image recognition in daily monitoring, which means that there is a distribution discrepancy between incoming target data and collected target data (intradomain discrepancy). Consequently, most existing methods need to re-adapt the incoming data and retrain a new model on online data. This paradigm is difficult to meet the real-time requirements of online tasks. In this study, we propose an online UDA framework via jointly reducing interdomain and intradomain discrepancies on cross-domain object recognition where target data arrive in a streamed manner. Specifically, the proposed framework comprises two phases: classifier training and online recognition phases. In the former, we propose training a classifier on a shared subspace where there is a lower interdomain discrepancy between the two domains. In the latter, a low-rank subspace alignment method is introduced to adapt incoming data to the shared subspace by reducing the intradomain discrepancy. Finally, online recognition results can be obtained by the trained classifier. Extensive experiments on DA benchmarks and real-world datasets are employed to evaluate the performance of the proposed framework in online scenarios. The experimental results show the superiority of the proposed framework in online recognition tasks.

Multimodal physiological-based emotion recognition is one of the most available but challenging studies due to complexity of emotions and individual differences in physiological signals. However, existing studies mainly combine multimodal data to fuse multimodal information in offline scenarios, ignoring data/modalities correlation among multimodal data and individual differences of non-stationary physiological signals in online scenarios. In this paper, we propose a novel Online Multimodal HyperGraph Learning (OMHGL) method to fuse multimodal information for emotion recognition based on time-series physiological signals. Our method consists of multimodal hypergraph fusion and online hypergraph learning. Specifically, the multimodal hypergraph fusion can fuse multimodal physiological signals to effectively obtain emotionally dependent information via leveraging multimodal information and higher-order correlations among multimodal data/modalities. The online hypergraph learning is designed to learn new information from online data by updating hypergraph projection. As a result, the proposed online emotion recognition model can be more effective for emotion recognition of target subjects when target data arrive in an online manner. Experimental results have demonstrated that the proposed method significantly outperforms the baselines and compared state-of-the-art methods in online emotion recognition tasks.

In generative adversarial network (GAN) based zero-shot learning (ZSL) approaches, the synthesized unseen visual features are inevitably prone to seen classes since the feature generator is merely trained on seen references, which causes the inconsistency between visual features and their corresponding semantic attributes. This visual-semantic inconsistency is primarily induced by the non-preserved semantic-relevant components and the non-rectified semantic-irrelevant low-level visual details. Existing generative models generally tackle the issue by aligning the distribution of the two modalities with an additional visual-to-semantic embedding, which tends to cause the hubness problem and ruin the diversity of visual modality. In this paper, we propose a novel generative model named learning modality-consistent latent representations GAN (LCR-GAN) to address the problem via embedding the visual features and their semantic attributes into a shared latent space. Specifically, to preserve the semantic-relevant components, the distributions of the two modalities are aligned by maximizing the mutual information between them. And to rectify the semantic-irrelevant visual details, the mutual information between original visual features and their latent representations is confined within an appropriate range. Meanwhile, the latent representations are decoded back to both modalities to further preserve the semantic-relevant components. Extensive evaluations on four public ZSL benchmarks validate the superiority of our method over other state-of-the-art methods.

Electrocardiogram (ECG) signal based cross-subject emotion recognition methods reduce the influence of individual differences using domain adaptation (DA) techniques. These methods generally assume that the entire unlabeled data of unknown target subjects are available in training phase. However, this assumption does not hold in some practical scenarios where the data of target subjects arrive one by one in an online manner instead of being acquired at a time. Thus, existing DA methods cannot be directly applied in this case since the unknown target data is inaccessible in training phase. To tackle the problem, we propose a novel online cross-subject ECG emotion recognition method leveraging hypergraph-based online transfer learning (HOTL). Specifically, the proposed hypergraph structure is capable of learning the high-order correlation among data, such that the recognition model trained on source subjects can be more effectively generalized to target subjects. Meanwhile, the structure can be easily updated by adding a hyperedge which connects a newly coming sample with the current hypergraph, resulting in further reduce the individual differences in online manner without re-training the model. Consequently, HOTL can effectively deal with the online cross-subject scenario where unknown target ECG data arrive one by one and varying overtime. Extensive experiments conducted on the Amigos dataset validate the superiority of the proposed method.

In zero-shot learning (ZSL) tasks, especially in generalized zero-shot learning (GZSL), the model tends to classify unseen test samples into seen categories, which is well known as the domain shift problem, because the model is trained from seen samples without unseen samples. Recently, generative adversarial network (GAN) based methods have achieved good performance in GZSL, which replace real unseen features by synthesizing fake ones to mitigate the domain shift. However, the domain shift problem is still not well solved, due to the lacking of unseen samples in the training progress of the GAN generator. In this paper, we propose a generative model named discriminative learning GAN (DL-GAN) to alleviate the domain shift in GZSL. Specifically, the DL-GAN is designed with three novel components: a dual-stream embedding model that aligns features to the ground-truth attributes to extract discriminative latent attributes from features, an attribute-based generative model that generates high-quality unseen features from semantic attributes to guarantee inter-class discriminability and semantic consistency, and a seen/unseen classifier that leverages validation samples to distinguish seen samples from unseen ones. Experimental results on four widely used datasets verify that our proposed approach significantly outperforms the state-of-the-art methods under the GZSL protocol.