Estimating the momentary level of participant's engagement is an important prerequisite for assistive systems that support human interactions. Previous work has addressed this task in within-domain evaluation scenarios, i.e. training and testing on the ...

Existing image inpainting methods have achieved remarkable accomplishments in generating visually appealing results, often accompanied by a trend toward creating more intricate structural textures. However, while these models excel at creating more ...

Many domain adaptive object detection (DAOD) methods employ domain adversarial training to align features and mitigate the domain gap. In this approach, a feature extractor is trained to deceive a domain classifier, thereby aligning feature ...

Domain Adaptive Object Detection (DAOD) aims to improve the adaptation of the detector for the unlabeled target domain by the labeled source domain. Recent advances leverage a self-training framework to enable a student model to learn the target domain ...

Semi-supervised graph domain adaptation, as a subfield of graph transfer learning, seeks to precisely annotate unlabeled target graph nodes by leveraging transferable features acquired from the limited labeled source nodes. However, most existing studies ...

As e-commerce stores broaden their reach into new regions and introduce new products within established markets, the development of effective machine learning models becomes increasingly challenging due to the scarcity of labeled data. Traditional ...

Medical image analysis, empowered by artificial intelligence (AI), plays a crucial role in modern healthcare diagnostics. However, the effectiveness of machine learning models hinges on their ability to generalize to diverse patient populations, ...

We investigate domain adaptation for Human Activity Recognition (HAR), where a model trained on one dataset (source) is applied to another dataset (target) with different characteristics. Specifically, we focus on evaluating the performance of SelfHAR, a ...

Recently, WiFi-based gait recognition technologies have been widely studied. However, most of them work on a strong assumption that users need to walk continuously and periodically under a constant body posture. Thus, a significant challenge arises when ...

Predicting depth maps from monocular images has made an impressive performance in the past years. However, most depth estimation methods are trained with paired image-depth map data or multi-view images (e.g., stereo pair and monocular sequence), which ...

This work introduced a novel analysis method to estimate interaction intensity, i.e., the level of positivity/negativity of interaction, for intimate couples (married and heterosexual) under the impact of alcohol, which has great influences on behavioral ...

Current state-of-the-art dialogue systems heavily rely on extensive training datasets. However, challenges arise in domains where domain-specific training datasets are insufficient or entirely absent. To tackle this challenge, we propose a novel ...${}^{\mathrm{}}$${}^{\mathrm{}}$${}^{\mathrm{}}$${}^{\mathrm{}}$

Image enhancement methods leveraging learning-based approaches have demonstrated impressive results when trained on synthetic degraded-clear image pairs. However, when deployed in real-world scenarios, such models often suffer significant performance ...

Graph neural networks (GNNs) have demonstrated remarkable success in numerous graph analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to distribution shifts, limiting their capacity for knowledge transfer across ...

Heterogeneous data widely exists in various high-impact applications. Domain adaptation and out-of-distribution generalization paradigms have been formulated to handle the data heterogeneity across domains. However, most existing domain adaptation and ...

In this paper, we tackle a new problem ofmulti-source unsupervised domain adaptation (MSUDA) for graphs, where models trained on annotated source domains need to be transferred to the unsupervised target graph for node classification. Due to the ...

Time series domain adaptation stands as a pivotal and intricate challenge with diverse applications, including but not limited to human activity recognition, sleep stage classification, and machine fault diagnosis. Despite the numerous domain adaptation ...

Trip purpose is a meaningful aspect of travel behaviour for the understanding of urban mobility. However, it is non-trivial to automatically obtain trip purposes. On one hand, trip purposes are naturally diverse and complicated, but the available ...

Multivariate Time Series Anomaly Detection (MTS-AD) is crucial for the effective management and maintenance of devices in complex systems, such as server clusters, spacecrafts, and financial systems, and so on. However, upgrade or cross-platform ...

We propose a method to decompose a single eye region image in the wild into albedo, shading, specular, normal and illumination. This inverse rendering problem is particularly challenging due to inherent ambiguities and complex properties of the natural ...