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In recent years, the rapid proliferation of multi-modal fake news has posed potential harm across various sectors of society, making the detection of multi-modal fake news crucial. Most existing methods can not effectively reduce the redundant information and preserve both semantic and structural information. To address these problems, this paper proposes a semantic distillation and structural alignment (SDSA) network. We design an semantic distillation module for modality-specific features to preserve task-relevant semantic information and eliminate redundant information.

Most works in class incremental learning (CIL) assume disjoint sets of classes as tasks. Although a few works deal with overlapped sets of classes, they either assume a balanced data distribution or assume a mild imbalanced distribution. Instead, in this paper, we explore one of the understudied real-world CIL settings where (1) different tasks can share some classes but with new data samples, and (2) the training data of each task follows a long-tail distribution. We call this setting CIL-LT.

Efficient and accurate bird sound classification is of importance for ecology, habitat protection and scientific research, as it plays a central role in monitoring the distribution and abundance of species. However, prevailing methods typically demand extensively labeled audio datasets and have highly customized frameworks, imposing substantial computational and annotation loads. In this study, we present an efficient and general framework called SSL-Net, which combines spectral and learned features to identify different bird sounds.

Efficient and accurate bird sound classification is of importance for ecology, habitat protection and scientific research, as it plays a central role in monitoring the distribution and abundance of species. However, prevailing methods typically demand extensively labeled audio datasets and have highly customized frameworks, imposing substantial computational and annotation loads. In this study, we present an efficient and general framework called SSL-Net, which combines spectral and learned features to identify different bird sounds.

Unsupervised anomaly detection (UAD) is a widely adopted approach in industry due to rare anomaly occurrences and data imbalance. A desirable characteristic of an UAD model is contained generalization ability which excels in the reconstruction of seen normal patterns but struggles with unseen anomalies. Recent studies have pursued to contain the generalization capability of their UAD models in reconstruction from different perspectives, such as design of neural network (NN) structure and training strategy.

We have seen recent advances in the fields of Machine Learning (ML), Deep Learning (DL), and Artificial intelligence (AI) that the models are becoming increasingly complex and large in terms of architecture and parameter size. These complex ML/DL models have beaten the state of the art in most fields of computer science like computer vision, NLP, tabular data prediction and time series forecasting, etc. With the increase in models’ performance, model explainability and interpretability has become essential to explain/justify model outcome, especially for business use cases.

Diversifying training data techniques have achieved tremendous success in Domain Generalization (DG) tasks. The key to diversifying domain data is by increasing the types of domain styles. After investigating this issue from the perspective of the Fourier transform, the domain cue is found to be implicitly encoded in the amplitude component of Fourier features, which is more indicative of domain-specific information than statistics (means and standard deviations).

Explainable AI (XAI) is the study on how humans can be able to understand the cause of a model’s prediction. In this work, the problem of interest is Scene Text Recognition (STR) Explainability, using XAI to understand the cause of an STR model’s prediction. Recent XAI literatures on STR only provide a simple analysis and do not fully explore other XAI

Real-world image recognition systems often face corrupted input images, which cause distribution shifts and degrade the performance of models. These systems often use a single prediction model in a central server and process images sent from various environments, such as cameras distributed in cities or cars. Such single models face images corrupted in heterogeneous ways in test time. Thus, they require to instantly adapt to the multiple corruptions during testing rather than being re-trained at a high cost.

Since mask occlusion causes plentiful loss of facial feature, Masked Face Recognition (MFR) is a challenging image processing task, and the recognition results are susceptible to noise. However, existing MFR methods are mostly deterministic point embedding models, which are limited in representing noise images. Moreover, Data Uncertainty Learning (DUL) fails to achieve reasonable performance in MFR.