Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Object detection is a cornerstone of environmental perception in advanced driver assistance systems(ADAS). However, most existing methods rely on RGB cameras, which suffer from significant performance degradation under low-light conditions due to poor image quality. To address this challenge, we proposes WTEFNet, a real-time object detection framework specifically designed for low-light scenario...
Object detection is a cornerstone of environmental perception in advanced driver assistance systems(ADAS). However, most existing methods rely on RGB cameras, which suffer from significant performance degradation under low-light conditions due to poor image quality. To address this challenge, we proposes WTEFNet, a real-time object detection framework specifically designed for low-light scenario...
Stylized abstraction synthesizes visually exaggerated yet semantically faithful representations of subjects, balancing recognizability with perceptu...
Machine unlearning aims to remove the influence of specific training samples from a trained model without full retraining. While prior work has larg...
Traffic safety science has long been hindered by a fundamental data paradox: the crashes we most wish to prevent are precisely those events we rarel...
Modern on-device neural network applications must operate under resource constraints while adapting to unpredictable domain shifts. However, this co...
Diffusion models have shown strong capabilities in high-fidelity image generation but often falter when synthesizing rare concepts, i.e., prompts th...
Dataset distillation (DD) has emerged as a powerful paradigm for dataset compression, enabling the synthesis of compact surrogate datasets that appr...
Generative image editing using diffusion models has become a prevalent application in today's AI cloud services. In production environments, image e...
The integration of large language models (LLMs) with function calling has emerged as a crucial capability for enhancing their practical utility in r...
We present OvSGTR, a novel transformer-based framework for fully open-vocabulary scene graph generation that overcomes the limitations of traditiona...
Long-term Action Quality Assessment (AQA) aims to evaluate the quantitative performance of actions in long videos. However, existing methods face ch...
Spatio-temporal prediction is a pivotal task with broad applications in traffic management, climate monitoring, energy scheduling, etc. However, exi...
Large Language Models (LLMs) falter in multi-step interactions -- often hallucinating, repeating actions, or misinterpreting user corrections -- due...
In recent years, the fusion of the medical and computer science domains has gained significant traction in the scientific research landscape. Progress...
Guidance is a cornerstone of modern diffusion models, playing a pivotal role in conditional generation and enhancing the quality of unconditional sa...
The rapid advancement of large language models (LLMs) and multi-modal LLMs (MLLMs) has historically relied on model-centric scaling through increasi...
Face swapping, recognized as a privacy and security concern, has prompted considerable defensive research. With the advancements in AI-generated con...
This paper calls on the research community not only to investigate how human biases are inherited by large language models (LLMs) but also to explor...
Efficiently serving large language models (LLMs) under dynamic and bursty workloads remains a key challenge for real-world deployment. Existing serv...