19 accepted papers
Subject-Driven Text-to-Image (T2I) Generation aims to preserve a subject's identity while editing its context based on a text prompt. A core challenge in this task is the "similarity-controllability paradox", where enhancing textual control often degrades the subject's fidelity, and vice-versa. We a
When unmanned aerial vehicles (UAVs) conduct exploration tasks in weakly textured environments, such as planetary surfaces or outdoor scenes with sparse features, the absence of GPS typically necessitates the use of visual SLAM for localization. However, feature sparsity, motion blur caused by rapid
Federated Learning (FL) enables collaborative model training across distributed clients without data sharing, but its high computational and communication demands strain resource-constrained devices. While existing methods use dynamic pruning to improve efficiency by periodically adjusting sparse mo…
Micro-expression (ME) recognition holds great potential for revealing true human emotions. A significant barrier to effective ME recognition is the lack of sufficient annotated ME video data because MEs are subtle and involuntary facial expressions that are very hard to capture. To address this issu…
Existing implicit sentiment learning methods mainly focus on capturing implicit sentiment knowledge individually, without paying more attention to the potential connection between implicit and explicit sentiment. From a linguistic perspective, implicit and explicit sentiment expressions are essentia…
"Despite real-time planners exhibiting remarkable performance in autonomous driving, the growing exploration of Large Language Models (LLMs) has opened avenues for enhancing the interpretability and controllability of motion planning. Nevertheless, LLM-based planners continue to encounter significan…
Current cross-prompt automatic essay scoring (AES) systems are primarily concerned with obtaining shared knowledge specific to the target prompt by using the source and target prompt essays. However, it may not be feasible in practical situations because the target prompt essays may not be available…
With the rapid development of 3D movie and light-field displays, there is a growing demand for stereo videos. However, generating high-quality stereo videos from planar videos remains a challenging task. Traditional depth-image-based rendering techniques struggle to effectively handle the problem of…
Pelvic fractures are one of the most serious traumas in orthopedics, and the technical proficiency and expertise of the surgical team strongly influence the quality of reduction results. With the advancement of information technology and robotics, robot-assisted pelvic fracture reduction surgery is…
Current cross-prompt automated essay scoring (AES) is a challenging task due to the large discrepancies between different prompts, such as different genres and expressions. The main goal of current cross-prompt AES systems is to learn enough shared features between the source and target prompts to g…
COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and…
Deep learning models for automatic readability assessment generally discard linguistic features traditionally used in machine learning models for the task. We propose to incorporate linguistic features into neural network models by learning syntactic dense embeddings based on linguistic features. To…
This letter studies the problem of controlling the sliding motion of an object held by a robot manipulator. We show how a parallel-jaw gripper can reliably control the motion of a rigid, prism-like object by 1) estimating the object's sliding velocity using measurements from tactile sensors at the g
Collision detection is critical for safe robot operation in the presence of humans. Acoustic information originating from collisions between robots and objects provides opportunities for fast collision detection and localization; however, audio information from microphones on robot manipulators need…
For mental disorders, patients' underlying mental states are non-observed latent constructs which have to be inferred from observed multi-domain measurements such as diagnostic symptoms and patient functioning scores. Additionally, substantial heterogeneity in the disease diagnosis between patients…
This paper studies the resilient distributed recovery of large fields under measurement attacks, by a team of agents, where each measures a small subset of the components of a large spatially distributed field. An adversary corrupts some of the measurements. The agents collaborate to process their m…
Smart grid has attracted increasing attention in the past decade, and one of its common problems is the variation of the nominal frequency (50 or 60 Hz) introduced by harmonics. In this paper, a batch-mode frequency estimator that can accurately obtain the deviation from the nominal frequency is pro…
Internet of Things (IoT) applications for Smart Cities, such as systems for traffic control and pollution monitoring, increasingly rely on trustworthy and secure data analytics. Proper countermeasures are needed to ensure that IoT applications function reliably under security threats. This paper stu…
We study cyber-physical systems subject to dynamic sensor attacks, relating them to the system's strong observability. First, we find necessary and sufficient conditions for an attacker to create a dynamically undetectable sensor attack and relate these conditions to properties of the system dynamic…