19 accepted papers
Cross-subject EEG decoding remains a fundamental challenge due to substantial inter-subject variability in brain activity, which hinders the development of subject-independent EEG models. Despite progress in extracting cross-subject invariant features, existing studies neglect the shared neural resp
Real-world multivariate time series unsupervised anomaly detection is a challenging problem due to intricate temporal correlations. Recently, impressive progress have been made in tackling this issue through the design of large-scale models, facilitated by the growing model parameters. However, in r…
Detecting anomalies in unsupervised multivariate time series is challenging due to the intricate temporal patterns present in both local short-term and global long-term dependencies. Long short-term memory has achieved impressive results in this domain, yet it is gradually being supplemented by Tran…
The performance of multimodal models often deteriorates when modality absence occurs. The absence disrupts the learned inter-modal correlations, resulting in biased multimodal representations. This challenge is especially pronounced when the absence is pervasive, affecting both the training and infe…
Interactive segmentation aims to extract objects of interest from an image based on user-provided clicks. In real-world applications, there is often a need to segment a series of images featuring the same target object. However, existing methods typically process one image at a time, failing to cons…
Class-incremental learning (CIL) enables models to continuously learn new classes while addressing catastrophic forgetting. With the introduction of pre-trained models, new tuning paradigms have emerged for CIL. This paper revisits parameter-efficient fine-tuning (PEFT) methods in the context of inc…
Sleep staging is critical for assessing sleep quality and tracking health. Polysomnography (PSG) provides comprehensive multimodal sleep-related information, but its complexity and impracticality limit its practical use in daily and ubiquitous monitoring. Conversely, unimodal devices offer more conv…
Segmentation of ultra-high resolution (UHR) images is a critical task with numerous applications, yet it poses significant challenges due to high spatial resolution and rich fine details. Recent approaches adopt a dual-branch architecture, where a global branch learns long-range contextual informati…
In real-world applications, deep neural networks may encounter constantly changing environments, where the test data originates from continually shifting unlabeled target domains. This problem, known as Unsupervised Continual Domain Shift Learning (UCDSL), poses practical difficulties. Existing meth…
Text-to-image (T2I) diffusion models have achieved remarkable progress in generating realistic images from textual descriptions. However, ensuring consistent high-quality image generation with complete backgrounds, object appearance, and optimal texture rendering remains challenging. This paper pres…
Brain computer interface with time-series physiological signal analysis (e.g., EEG and fNIRS) is commonly-used technology for the auxiliary diagnosis of dementia. However, due to the non-stationary, non-linear and low signal-to-noise ratio of time-series signal, as well as the lack of relevant demen…
Federated learning (FL) facilitates collaborative model training across distributed clients while maintaining privacy. Federated noisy label learning (FNLL) is more of a challenge for data inaccessibility and noise heterogeneity. Existing works primarily assume clients are either noisy or clean, whi…
Human activity recognition (HAR) is crucial for health monitoring and disease diagnosis in Internet-of-Things environments. However, existing HAR approaches either suffer from poor accuracy or achieve high accuracy at the expense of costly manual annotations. To overcome the challenge above, we prop…
Unsupervised anomaly detection is a binary classification that detects anomalies in unseen samples given only unlabeled normal data. Reconstruction-based approaches are widely used, which perform reconstruction error minimization on training data to learn normal patterns and quantify the degree of a…
Although most of existing works for sensor-based Human Activity Recognition rely on the temporal view, we argue that the spectral view also provides complementary prior and accordingly benchmark a standard multi-view framework with extensive experiments to demonstrate its consistent superiority over…
Time series classification is an important problem in the real world. Due to its non-stationary property that the distribution changes over time, it remains challenging to build models for generalization to unseen distributions. In this paper, we propose to view time series classification from the d…
Sensor-based human activity recognition (HAR) plays an important role in our daily life. Most work on HAR often assumes that training and test samples follow the same data distribution, which is not realistic in practice. For example, activity patterns usually vary from person to person, which will…
Video surveillance applications such as vehicle speed measurement and traffic condition monitoring are prevailing nowadays. Monocular 3D object detection by traffic surveillance cameras is one of the key means to achieve these functions. The methods in literature depend on camera calibration and req…
Discriminating pathologic cognitive decline from the expected decline of normal aging is an important research topic for elderly care and health monitoring. However, most cognitive assessment methods only work when data distributions of the training set and testing set are consistent. Enabling exist…