Some multimedia data from real life can be collected as multivariate time series data, such as community-contributed social data or sensor data. Many methods have been proposed for multivariate time series forecasting. In light of its importance in wide ...

The increasing amount of visual data shared online highlights the importance of organizing and finding related content. However, current efforts to improve visual search and image classification lack support for exploratory image search. Sorting images ...

With recent advancements in synthetic media manipulation and generation, verifying multimedia content posted online has become increasingly difficult. Additionally, the malicious exploitation of AI technologies by actors to disseminate disinformation on ...

Recently, there has been an increased interest in cross-data research problems, such as predicting air quality using life logging images, predicting congestion using weather and tweets data, and predicting sleep quality using daily exercises and meals. ...

Action recognition, which consists in automatically recognizing the action being performed in a video sequence, is a fundamental task in computer vision and multimedia. Supervised action recognition has been widely studied because of the growing need for ...

In realistic application scenarios, existing methods for image-text modeling have limitations in dealing with data stream: training on all data needs too much computation/storage resources, and even the full access to previous data is invalid. In this ...

Simultaneous Localization and Mapping (SLAM) has developed as a fundamental method for intelligent robot perception over the past decades. Most of the existing feature-based SLAM systems relied on traditional hand-crafted visual features and a strong ...

State-of-the-art deep neural unsupervised video summarization methods mostly fall under the adversarial reconstruction framework. This employs a Generative Adversarial Network (GAN) structure and Long Short-Term Memory (LSTM) autoencoders during its ...

Zero-shot sketch-based image retrieval (ZS-SBIR) is a task that learns semantic knowledge and embedding extraction to retrieve similar images using a sketch without any training examples of unseen classes. Existing methods have attempted to address the ...

It is essential for sports teams to review their offensive and defensive tactical execution performance as well as understand their opponents’ tactics in order to identify effective counterattack strategies. This study focuses on basketball offensive ...

The emergence of photo-realistic deepfakes on a large scale has become a significant societal concern, which has garnered considerable attention from the research community. Several recent studies have identified the critical issue of “temporal ...

The current challenge of Deepfake detection is the cross-domain performance on unseen Deepfake data. Instead of extracting forgery artifacts that are robust to the cross-domain scenarios as most previous works, we propose a novel method named Single-...

Identifying the frames of news is important to understand the articles’ vision, intention, message to be conveyed, and which aspects of the news are emphasized. Framing is a widely studied concept in journalism, and has emerged as a new topic in ...

This paper proposes a multi-channel convolutional neural network (MC-CNN) for classifying memes and non-memes. Our architecture is trained and validated on a challenging dataset that includes non-meme formats with textual attributes, which are also ...

The effective knowledge and interaction within multi-modalities are key to Visual Dialog. Classic graph-based framework with the direct connection between history dialog and answer fails to give the right answer for the spurious guidance and strong bias ...

The easy sharing of multimedia content on social media has caused a rapid dissemination of fake news, which threatens society’s stability and security. Therefore, fake news detection has garnered extensive research interest in the field of social ...

Image retrieval aims to find similar images given the query. Most of existing retrieval works are based on the pre-trained model of single-label image classification. In practice, the query usually contains more than one instance, and the single label ...

Image clustering is a crucial but challenging task in multimedia machine learning. Recently the combination of clustering with deep learning has achieved promising performance against conventional methods on high-dimensional image data. Unfortunately, ...

This paper investigates the problem of scene graph generation in videos with the aim of capturing semantic relations between subjects and objects in the form of ⟨ subject, predicate, object⟩ triplets. Recognizing the predicate between subject and object ...

Recent advances in computer vision research led to large vision foundation models that generalize to a broad range of image domains and perform exceptionally well in various image based tasks. However, content-based image-to-image retrieval is often ...