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pro vyhledávání: '"Wang, ZhiFeng"'
With the continuous deepening and development of the concept of smart education, learners' comprehensive development and individual needs have received increasing attention. However, traditional educational evaluation systems tend to assess learners'
Externí odkaz:
http://arxiv.org/abs/2411.05325
This paper introduces a modeling approach that employs multi-level global processing, encompassing both short-term frame-level and long-term sample-level feature scales. In the initial stage of shallow feature extraction, various scales are employed
Externí odkaz:
http://arxiv.org/abs/2411.03668
Reconstructing from multi-view images is a longstanding problem in 3D vision, where neural radiance fields (NeRFs) have shown great potential and get realistic rendered images of novel views. Currently, most NeRF methods either require accurate camer
Externí odkaz:
http://arxiv.org/abs/2411.02979
Accurate annotation of educational resources is critical in the rapidly advancing field of online education due to the complexity and volume of content. Existing classification methods face challenges with semantic overlap and distribution imbalance
Externí odkaz:
http://arxiv.org/abs/2411.01841
Bokeh rendering is one of the most popular techniques in photography. It can make photographs visually appealing, forcing users to focus their attentions on particular area of image. However, achieving satisfactory bokeh effect usually presents signi
Externí odkaz:
http://arxiv.org/abs/2410.14400
Most affective computing tasks still rely heavily on traditional methods, with few deep learning models applied, particularly in multimodal signal processing. Given the importance of stress monitoring for mental health, developing a highly reliable a
Externí odkaz:
http://arxiv.org/abs/2410.11376
The integration of Artificial Intelligence into the modern educational system is rapidly evolving, particularly in monitoring student behavior in classrooms, a task traditionally dependent on manual observation. This conventional method is notably in
Externí odkaz:
http://arxiv.org/abs/2410.07834
Autor:
Zhang, Qixuan, Wang, Zhifeng, Zhang, Dylan, Niu, Wenjia, Caldwell, Sabrina, Gedeon, Tom, Liu, Yang, Qin, Zhenyue
Vision Large Language Models (VLLMs) are transforming the intersection of computer vision and natural language processing. Nonetheless, the potential of using visual prompts for emotion recognition in these models remains largely unexplored and untap
Externí odkaz:
http://arxiv.org/abs/2410.02244
This paper introduces LLDif, a novel diffusion-based facial expression recognition (FER) framework tailored for extremely low-light (LL) environments. Images captured under such conditions often suffer from low brightness and significantly reduced co
Externí odkaz:
http://arxiv.org/abs/2408.04235
Autor:
Zhang, Qixuan, Wang, Zhifeng, Liu, Yang, Qin, Zhenyue, Zhang, Kaihao, Caldwell, Sabrina, Gedeon, Tom
In this paper, we present a novel benchmark for Emotion Recognition using facial landmarks extracted from realistic news videos. Traditional methods relying on RGB images are resource-intensive, whereas our approach with Facial Landmark Emotion Recog
Externí odkaz:
http://arxiv.org/abs/2404.13493