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Micro-action involves low-amplitude movement of human body, which brings challenges to common action recognition. This paper focuses on the extremely small region of human body as well as the severe long-tail distribution in micro-action recognition. An ...

Classifying videos differs from that of images in the need to capture the information on what has happened, instead of what is in the frames. Conventional methods typically follow the data-driven approach, which uses transformer-based attention models to ...

The use of video advertisements is a common marketing strategy in today's digital age. Extensive research is conducted by companies to comprehend the emotions conveyed in video advertisements, as they play a crucial role in crafting memorable ...

Concerns over the physical health of middle school students have prompted the inclusion of physical fitness tests into high school entrance examinations. Table tennis skills assessments, in particular, are popular among students due to their engaging ...

The proliferation of news sources on the web amplifies the problem of disinformation and misinformation, impacting public perception and societal stability. These issues necessitate the identification of bias in news broadcasts, whereby the analysis and ...

In the application areas of streaming, social networks, and video-sharing platforms such as YouTube and Facebook, along with traditional television systems, programs’ classification stands as a pivotal effort in multimedia content management. Despite ...

Test-time adaptation (TTA) aims at boosting the generalization capability of a trained model by conducting self-/un-supervised learning during testing in real-world applications. Though TTA on image-based tasks has seen significant progress, TTA ...

Video classification is a complex task that involves analyzing audio and video signals using deep neural models. To reliably classify these signals, researchers have developed multimodal fusion techniques that combine audio and video data into ...

Video understanding remains a challenging task because video understanding models have many parameters to be trained and should capture detailed spatiotemporal contexts in video effectively. Recent methods have typically employed 3D convolution modules ...

Video processing and analysis have become an urgent task, as a huge amount of videos (e.g., YouTube, Hulu) are uploaded online every day. The extraction of representative key frames from videos is important in video processing and analysis since it ...

This paper proposes a method to classify systematic surface defects on virtual car body parts through a CRNN. Systematic surface defects occur during the manufacturing process of outer surface parts. Those defects get classified by auditors according ...

Video transformer naturally incurs a heavier computation burden than a static vision transformer, as the former processes T times longer sequence than the latter under the current attention of quadratic complexity (T2N2). The existing works treat the ...

Videos naturally contain dynamic variation over the temporal axis, which will result in the same visual clues (e.g., semantics, objects) changing their scale, position, and perspective patterns between adjacent frames. A primary trend in video CNN is ...

The expression of video content presents the combination of audio-visual aspects. How to effectively combine audio and video features and generate robust content representation is still a problem to be explored. In this paper, we propose a multi-modal ...

Recently, GAN-based deepfake videos frequently appeared on video websites, which has a bad impact on the credibility of the videos. Due to the variety of forgery algorithms, the classification methods trained on large-scale datasets often have poor ...

While Transformers have yielded impressive results for video classification on large datasets recently, simpler models without the transformer architecture can be promising for small datasets. In this paper, we propose three major techniques to improve ...

This paper presents the method that underlies our submission to the Pre-training for Video Understanding Challenge Track II. We follow the basic pipeline of temporal segment networks [20] and further improve its performance in several aspects. ...

Transformer achieves remarkable successes in understanding 1 and 2-dimensional signals (e.g., NLP and Image Content Understanding). As a potential alternative to convolutional neural networks, it shares merits of strong interpretability, high ...

With the rapid development of social media, tremendous videos with new classes are generated daily, which raise an urgent demand for video classification methods that can continuously update new classes while maintaining the knowledge of old videos with ...

Learning comprehensive spatiotemporal features is crucial for human action recognition. Existing methods tend to model the spatiotemporal feature blocks in an integrate-separate-integrate form, such as appearance-and-relation network (ARTNet) and ...