13 accepted papers
Contactless stress monitoring, with its non-intrusive nature, is invaluable for maintaining mental and physical health. Recent studies have demonstrated encouraging results in contactless stress monitoring during daytime using radio frequency (RF) signals. However, the weak correlation between stres…
The mutual interference mitigation between identical or similar radar systems in autonomous driving has gained wide spread attention from both academia and industry. The resulted ghost target interference will reduce the sensitivity of the radar sensor and increase the false alarm rate. To tackle th…
Diffusion models have recently brought a powerful revolution in image generation. Despite showing impressive generative capabilities most of these models rely on the current sample to denoise the next one possibly resulting in denoising instability. In this paper we reinterpret the iterative denoisi…
Radar sensing has been a promising solution for contactless monitoring of Heart Rate Variability (HRV), an essential indicator of the cardiovascular and autonomic nervous systems. However, existing works neglect heartbeat-driven body surface motions spreading across the entire body with spatial vari…
Recent advancements have showcased the potential of hand-held millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imagin…
Existing efforts on human tracking using wireless signal are primarily focused on constrained scenarios with only a few individuals in empty spaces. However, in practical unconstrained scenarios with severe interference and attenuation, accurate multi-person tracking has been intractable. In this pa…
This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions examining it from two angles: the collection pipeline of real reflection pairs and the perception of real reflection locations. We devise an advanced reflection collection pipeline that is highly ad…
Fall detection using Radio Frequency (RF) signals with deep learning has exhibited significant promise in recent years. However, the costly collection of RF data with falls has hampered the performance of existing methods. While there has been approaches which can generate RF signals using various s…
Shadow removal, which aims to restore the background in the shadow regions, is challenging due to the highly ill-posed nature. Most existing deep learning-based methods individually remove the shadow by only considering the content of the matched paired images, barely taking into account the auxilia…
Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous works have extensively studied explicitly encoding Structural Causal Models (SCMs) into architectures of generator networks…
Fall is a severe health threat for elders’ health care. While existing systems could achieve promising performance under specific scenarios, the required computing resources are usually not affordable, which is not applicable for real-time detection. In this paper, we propose mmFall, a real time fal…
Image-to-video person re-identification aims to retrieve the same pedestrian as the image-based query from a video-based gallery set. Existing methods treat it as a cross-modality retrieval task and learn the common latent embeddings from image and video modalities, which are both less effective and…
Video-based person re-identification aims to match pedestrians from video sequences across non-overlapping camera views. The key factor for video person re-identification is to effectively exploit both spatial and temporal clues from video sequences. In this work, we propose a novel Spatial-Temporal…