Guanqun Cao — accepted AI-conference papers

2025

BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages

Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif +42

ACL 2025long

People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition–an umbrella term for several NLP tasks–impacts various applications within NLP and beyond, most work in this area has focused on high-resource languages. This has led to significant disparities…

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2023

Learn from Incomplete Tactile Data: Tactile Representation Learning with Masked Autoencoders

Guanqun Cao, Jiaqi Jiang, Danushka Bollegala, Shan Luo

IROS 2023poster

The missing signal caused by the objects being occluded or an unstable sensor is a common challenge during data collection. Such missing signals will adversely affect the results obtained from the data, and this issue is observed more frequently in robotic tactile perception. In tactile perception,…

Cited by 8 Source ↗Scholar ↗

2023

Vis2Hap: Vision-based Haptic Rendering by Cross-modal Generation

Guanqun Cao, Jiaqi Jiang, Ningtao Mao, Danushka Bollegala, Min Li, Shan Luo

ICRA 2023poster

To assist robots in teleoperation tasks, haptic rendering which allows human operators access a virtual touch feeling has been developed in recent years. Most previous haptic rendering methods strongly rely on data collected by tactile sensors. However, tactile data is not widely available for robot…

Cited by 20 Source ↗Scholar ↗

2022

A4T: Hierarchical Affordance Detection for Transparent Objects Depth Reconstruction and Manipulation

Jiaqi Jiang, Guanqun Cao, Thanh-Toan Do, Shan Luo

RA-L 2022

Transparent objects are widely used in our daily lives and therefore robots need to be able to handle them. However, transparent objects suffer from light reflection and refraction, which makes it challenging to obtain the accurate depth maps required to perform handling tasks. In this letter, we pr

Cited by 40 Source ↗Scholar ↗

2020

Spatio-temporal Attention Model for Tactile Texture Recognition

Guanqun Cao, Yi Zhou, Danushka Bollegala, Shan Luo

IROS 2020poster

Recently, tactile sensing has attracted great interest in robotics, especially for facilitating exploration of unstructured environments and effective manipulation. A detailed understanding of the surface textures via tactile sensing is essential for many of these tasks. Previous works on texture re…

Cited by 48 Source ↗Scholar ↗