18 citations · 48 across the 11 of their papers we have counts for
11 papers
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
Lingdong Kong, Shaoyuan Xie, Hanjiang Hu +3
Depth estimation from monocular images is pivotal for real-world visual perception systems. While current learning-based depth estimation models train and test on meticulously cura…
The use of deception in dementia-care robots: Should robots tell "white lies" to limit emotional distress?
Samuel Rhys Cox, Grace Cheong, Wei Tsang Ooi
With projections of ageing populations and increasing rates of dementia, there is need for professional caregivers. Assistive robots have been proposed as a solution to this, as th…
Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages
Samuel Rhys Cox, Ashraf Abdul, Wei Tsang Ooi
Large language models (LLMs) are increasingly capable and prevalent, and can be used to produce creative content. The quality of content is influenced by the prompt used, with more…
Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptions
Samuel Rhys Cox, Yi-Chieh Lee, Wei Tsang Ooi
Chatbots are capable of remembering and referencing previous conversations, but does this enhance user engagement or infringe on privacy? To explore this trade-off, we investigated…
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
Keyu Duan, Qian Liu, Tat-Seng Chua +4
Textual graphs (TGs) are graphs whose nodes correspond to text (sentences or documents), which are widely prevalent. The representation learning of TGs involves two stages: (i) uns…
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
Lingdong Kong, Yaru Niu, Shaoyuan Xie +39
Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical a…