51 accepted papers
Robust decision-making is crucial in numerous risk-sensitive applications where outcomes are uncertain and the cost of failure is high. Conditional Robust Optimization (CRO) offers a framework for such tasks by constructing prediction sets for the outcome that satisfy predefined coverage requirement…
Sleep staging is pivotal for assessing sleep quality and clinical diagnostics. While contactless radio-frequency (RF) sensing offers an unobtrusive alternative to Polysomnography (PSG), existing methods often struggle with generalization across diverse devices and environments due to the scarcity of
Reconstructing 3D scenes from multi-view image sequences remains a significant challenge in practical applications. While recent advances in 3D Gaussian Splatting have enabled high-quality rendering, existing methods rely heavily on pixel-level L1 loss, which misaligns with human perception, leading
It is essential for understanding neural network decisions to interpret the functionality (also known as concepts) of neurons. Existing approaches describe neuron concepts by generating natural language descriptions, thereby advancing the understanding of the neural network's decision-making mechani
Image-to-image translation models have achieved notable success in converting images across visual domains and are increasingly used for medical tasks such as predicting post-operative outcomes and modeling disease progression. However, most existing methods primarily aim to match the target distrib
Thruster failures in unmanned surface vehicles (USVs) can critically compromise mission completion, particularly when severe degradation eliminates controllability in essential degrees of freedom. While traditional fault-tolerant control treats environmental disturbances as impediments to be rejecte…
Multimodal person Re-IDentification (ReID) aims to reliably associate specific individuals by utilizing complementary information from heterogeneous modalities. In contrast, low-frequency radio frequency (RF) signals, with their superior penetration capability and illumination invariance, provide id
Unsupervised remote photoplethysmography (rPPG) promises to leverage unlabeled video data, but its potential is hindered by a critical challenge: training on low-quality "in-the-wild" videos severely degrades model performance. An essential step missing here is to assess the suitability of the video
Underwater localization is a crucial capability for ensuring robust and accurate vehicle navigation. Although various well-developed localization systems exist, their primary focus is on ground and aerial applications. The challenges posed by underwater environments, such as sparse textures and dyna
With the rapid growth of social media and digital photography, visually appealing images have become essential for effective communication and emotional engagement. Among the factors influencing aesthetic appeal, composition--the arrangement of visual elements within a frame--plays a crucial role. I…
We propose ComDrive: the first comfort-oriented end-to-end autonomous driving system to generate temporally consistent and comfortable trajectories. Recent studies have demonstrated that imitation learning-based planners and learning-based trajectory scorers can effectively generate and select safet
Large-scale Vision-Language Pre-training (VLP) has demonstrated remarkable success in the general domain. However, in the fashion domain, items are distinguished by fine-grained attributes such as texture and material, which are crucial for tasks such as retrieval. Existing models often fail to take…
We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories. In autonomous driving scenarios, there is rarely a single suitable trajectory. Recent methods have increasingly focused on modeling multimodal trajectory distributions. However, they suf…
Text-guided image editing model has achieved great success in general domain. However, directly applying these models to the fashion domain may encounter two issues: (1) Inaccurate localization of editing region; (2) Weak editing magnitude. To address these issues, the MADiff model is proposed. Spec…
Passive non-line-of-sight (NLOS) imaging has developed rapidly in recent years. However, existing models generally suffer from low-quality reconstruction due to the severe loss of information during the projection process. In this paper, we introduce ParaEncodeNet, an NLOS imaging method for reconst…
Scaling Deep Neural Networks (DNNs) requires significant computational resources in terms of GPU quantity and compute capacity. In practice, there usually exists a large number of heterogeneous GPU devices due to the rapid release cycle of GPU products. It is highly needed to efficiently and economi…
Off-policy evaluation (OPE) is one of the most fundamental problems in reinforcement learning (RL) to estimate the expected long-term payoff of a given target policy with *only* experiences from another behavior policy that is potentially unknown. The distribution correction estimation (DICE) family…
Human-centric perception with radio frequency (RF) signals has recently entered a new era of end-to-end processing with Transformers. Considering the long-sequence nature of RF signals, the State Space Model (SSM) has emerged as a superior alternative due to its effective long-sequence modeling and…
Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, we propose the Co-Supervised Spotlight Shifting Network (CS<sup xmlns:mml="http://w…
Remote physiological measurement (RPM) based on video and radar has made significant progress in recent years. However, unimodal methods based solely on video or radar sensor have notable limitations due to their measurement principles, and multimodal RPM that combines these modalities has emerged a…
Recent advancements in WiFi indoor localization have demonstrated the potential for achieving decimeter-level accuracy based on Angle of Arrival (AoA). However, existing commercial WiFi Access Points (APs) suffer from phase offset across different antennas, which significantly degrade the performanc…
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…
Millimeter-wave (mmWave) radar has gained increasing attention in environmental perception due to its robustness under low-light conditions. However, existing methods fail to address the challenges of multipath interference and low angle resolution. In this paper, we introduce DiffRadar which levera…
In high-stakes sectors such as network security, IoT security, accurately distinguishing between normal and anomalous data is critical due to the significant implications for operational success and safety in decision-making. The complexity is exacerbated by the presence of unlabeled data and the op…
Contactless vital signs estimation using mmWave radar has gained significant attention. However, existing studies are built upon the radar being directed facing the thorax to capture fine-grained vital signs, ignoring the angle variation between the radar and thorax in practical deployment. In this…
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…
Propositional satisfiability (SAT) is an NP-complete problem that impacts many research fields, such as planning, verification, and security. Mainstream modern SAT solvers are based on the Conflict-Driven Clause Learning (CDCL) algorithm. Recent work aimed to enhance CDCL SAT solvers using Graph Neu…
As generative large language models (LLMs) such as ChatGPT gain widespread adoption in various domains, their potential to propagate and amplify social biases, particularly in high-stakes areas such as the labor market, has become a pressing concern. AI algorithms are not only widely used in the sel…
Intrusion detection acts as a key to in-home security, where WiFi-based systems have gained wide attention due to the ubiquitous nature of WiFi signals. While existing methods achieve impressive performance in specific environments, they are susceptible to environmental changes, especially for compl…
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…
Robust Markov Decision Processes (MDPs) and risk-sensitive MDPs are both powerful tools for making decisions in the presence of uncertainties. Previous efforts have aimed to establish their connections, revealing equivalences in specific formulations. This paper introduces a new formulation for risk…
For learning-based binary descriptors, the binarization process has not been well addressed. The reason is that the binarization blocks gradient back-propagation. Existing learning-based binary descriptors learn real-valued output, and then it is converted to binary descriptors by their proposed bin…
Existing deep learning-based wireless sensing models usually require intensive computation. In this paper, we introduce a lightweight RF-based 3D human pose estimation model, i.e., Fast RFPose, to enable real-time human pose estimation. Specifically, Fast RFPose first estimates the human locations i…
We study Model Predictive Control (MPC) and propose a general analysis pipeline to bound its dynamic regret. The pipeline first requires deriving a perturbation bound for a finite-time optimal control problem. Then, the perturbation bound is used to bound the per-step error of MPC, which leads to a…
In image retrieval, deep local features learned in a data-driven manner have been demonstrated effective to improve retrieval performance. To realize efficient retrieval on large image database, some approaches quantize deep local features with a large codebook and match images with aggregated match…
Stabilizing an unknown dynamical system is one of the central problems in control theory. In this paper, we study the sample complexity of the learn-to-stabilize problem in Linear Time-Invariant (LTI) systems on a single trajectory. Current state-of-the-art approaches require a sample complexity lin…
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…
The multimedia community has recently seen a tremendous surge of interest in the fashion recommendation problem. A lot of efforts have been made to model the compatibility between fashion items. Some have also studied users' personal preferences for the outfits. There is, however, another difficulty…
We study predictive control in a setting where the dynamics are time-varying and linear, and the costs are time-varying and well-conditioned. At each time step, the controller receives the exact predictions of costs, dynamics, and disturbances for the future $k$ time steps. We show that when the pre…
Snake-like robots have numerous applications in minimally invasive surgery. One important research topic of snake-like robots is the flexible joint mechanism and its actuation. This letter describes the design and fabrication of a new type of flexible joint mechanism that is enabled by metal powder
Suturing and knot tying in a confined space is a technically challenging yet clinically demanding task in minimally invasive surgery, which requires the use of highly articulated instruments passing through small incisions on the patient's body. Manually operating such instruments is usually very di
With the rapid growth of fashion-focused social networks and online shopping, intelligent fashion recommendation is now in great needs. Recommending fashion outfits, each of which is composed of multiple interacted clothing and accessories, is relatively new to the field. The problem becomes even mo…
Fashion recommendation is the task of suggesting a fashion item that fits well with a given item. In this work, we propose to automatically synthesis new items for recommendation. We jointly consider the two key issues for the task, i.e., compatibility and personalization. We propose a personalized…
Task autonomy is an important consideration for the development of future surgical robots. For robot-assisted anastomosis, suture thread detection is a prerequisite for subsequent robot manipulation. Previous works on automatic thread detection are focused on the learning of the models with specific…
The technique of robust suture detection is vital in many applications including trainee suturing skill evaluation, suture augmentation in robotic-assisted surgery and suture recognition for automatic suturing. Due to the complicated environment of surgery, the detection of a suture is challenged by…
This paper presents a versatile robotic system for sewing a 3D structured object. Leveraging on using a customized robotic sewing device and closed-loop visual servoing control, an all-in-one solution for sewing personalized stent graft is demonstrated. Stitch size planning and automated knot tying…
In this paper, a new perturbation analysis algorithm for the MUltiple SIgnal Classification (MUSIC) estimator applied to a Hermitian Toeplitz covariance matrix is presented. Inspired by the perspective that the MUSIC algorithm can be recognized as a structured matrix approximation, the perturbation…
This paper presents an intelligent sewing system for personalized stent graft manufacturing, a challenging sewing task that is currently performed manually. Inspired by medical suturing robots, we have adopted a single-sided sewing technique using a curved needle to perform the task of sewing stents…
Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumina…
A fundamental problem with dual-arm robotic control is to find the coordinated motion resolution under high kinematic redundancy and intrinsic constraints of each robot. To solve this problem, this paper presents a multi-tasking, co-operative control framework, in which potential task conflicts and…