11 accepted papers
Existing video-based automatic depression assessment (ADA) approaches frequently achieve video-level depression assessment by aggregating features or predictions of individual frames or equal-length segments within the given video. While their performances have been largely enhanced by recent advanc
Depression represents a global mental health challenge requiring efficient and reliable automated detection methods. Current Transformer- or Graph Neural Networks (GNNs)-based multimodal depression detection methods face significant challenges in modeling individual differences and cross-modal tempo
Depression can be reflected by long-term human spatio-temporal facial behaviours. While human face videos recorded in real-world usually have long and variable lengths, existing video-based depression assessment approaches frequently re-sample/down-sample such videos to short and equal-length videos…
The conversion of monocular RGB images to depth maps is crucial in robotic applications. Current supervised learning approaches, dependent on high-quality RGB-Depth pairs, struggle with indoor environments characterized by multiple objects and fluctuating lighting, leading to inaccurate and unstable…
As artificial intelligence and robotics advance, social robots are increasingly integrated into service domains, necessitating strategies to enhance user acceptance of their recommendations. Prior work has explored how gendered appearance influences user acceptance, yet the role of linguistic featur
Tongue diagnosis is integral to Traditional Chinese Medicine (TCM) for evaluating a patient’s body constitution. Yet, this field faces challenges such as indirect constitution diagnosis, a dearth of labeled datasets, and the complexities of few-shot learning. Existing studies focus mainly on analyzi…
The prevalence of depression is escalating, especially among youth, which has become a critical mental health concern. Current assessment methods, relying heavily on questionnaires, clinical observations, and AI-driven analyses, are limited by their focus on single-event data, failing to encapsulate…
Social robots are increasingly utilized across domains like education, healthcare, and elderly care. Research has examined how the robot's postures impact social perceptions, but few studies have investigated the effects of robots' bodily movements with dynamics gait variations on attributed authori
The prediction of dialogue acts (DA) labels on utterance-level in conversations can be treated as a sequence labeling problem, which requires context- and speaker-aware semantic comprehension, especially for Japanese. In this study, we pro-posed a hierarchical attention with the graph neural network…
In this paper, we propose an adversarial auto-encoder-based classifier, which can regularize the distribution of latent representation to smooth the boundaries among categories. Moreover, we adopt multi-instance learning by dividing speech into a bag of segments to capture the most salient moments f…
Social connectedness is vital for developing group cohesion and strengthening belongingness. However, with the accelerating pace of modern life, people have fewer opportunities to participate in group-building activities. Furthermore, owing to the teleworking and quarantine requirements necessitated