7 accepted papers
Obtaining labeled data in the field of industrial anomaly detection is challenging, which necessitates the development of label-free frameworks. However, current methods mainly focus on the unsupervised paradigm, which uses a large number of normal samples of the same category to train the model, an…
Adversarial attacks pose a significant challenge in deep learning, as carefully crafted perturbations can severely degrade even the most advanced models. In real-world scenarios, where the target models are often unknown, previous works often focus on creating adversarial patterns for specific known…
Understanding student behavior in educational settings is critical in improving both the quality of pedagogy and the level of student engagement. While various AI-based models exist for classroom analysis, they tend to specialize in limited tasks and lack generalizability across diverse educational…
With the success of pre-trained visual-language (VL) models such as CLIP in visual representation tasks, transferring pre-trained models to downstream tasks has become a crucial paradigm. Recently, the prompt tuning paradigm, which draws inspiration from natural language processing (NLP), has made s…
As an important and challenging task in video understanding, Temporal Action Detection (TAD) has been deeply studied in recent years. However, current works mainly tackle this task with visual information, while neglecting to explore the potential of the audio modality. To address this challenge, in…
Human pose estimation has long been motivated for its application in human behavior understanding and activity recognition. Despite recent advances in multi-person pose estimation, existing solutions remain challenging in crowded scenes, especially in classroom scenarios where students are extremely…
Deep learning-based methods for human pose estimation require large volumes of training data to achieve superior performance. However, data acquisition in classroom environments raises privacy concerns, which will undoubtedly hinder the development of the latest deep learning techniques in education…