8 accepted papers
Diverse and realistic data are essential for developing reliable autonomous driving (AD) systems, yet collecting and annotating large-scale real-world driving datasets is costly and time-consuming. Recent advances in synthetic scene generation and editing have enabled the creation of diverse driving…
With the widespread application of machine learning-based graph classification models in fields such as biology and economics, there has been a growing number of attacks aimed at perturbing classification results. Although current defense methods, such as randomized smoothing, have achieved some suc…
We present OpenRoboCare, a multimodal dataset for robot caregiving, capturing expert occupational therapist demonstrations of Activities of Daily Living (ADLs). Caregiving tasks involve complex physical human-robot interactions, requiring precise perception under occlusions, safe physical contact, a
Many robot caregiving tasks, such as bathing, dressing, and transferring, require a robot arm to make contact with a human body at multiple points rather than solely at the end effector. However, varied human touch preferences can lead to unsafe or uncomfortable multi-contact interactions. To addres…
Text-driven video editing has recently experienced rapid development. Despite this, evaluating edited videos remains a considerable challenge. Current metrics tend to fail to align with human perceptions, and effective quantitative metrics for video editing are still notably absent. To address this,…
Whole-arm tactile feedback is crucial for robots to ensure safe physical interaction with their surroundings. This paper introduces CushSense, a fabric-based soft and stretchable tactile-sensing skin designed for physical human-robot interaction (pHRI) tasks such as robotic caregiving. Using stretch…
"CLIP has achieved impressive zero-shot performance after pretraining on a large-scale dataset consisting of paired image-text data. Previous works have utilized CLIP by incorporating manually designed visual prompts like colored circles and blur masks into the images to guide the model’s attention,…
The success of large language models (LLMs) facilitate many parties to fine-tune LLMs on their own private data. However, this practice raises privacy concerns due to the memorization of LLMs. Existing solutions, such as utilizing synthetic data for substitution, struggle to simultaneously improve p…