most citedImproving Intention Detection in Single-Trial Classification through Fusion of EEG and Eye-tracker Data

2 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

eess.IV20241 cited

GASA-UNet: Global Axial Self-Attention U-Net for 3D Medical Image Segmentation

Chengkun Sun, Russell Stevens Terry, Jiang Bian +1

Accurate segmentation of multiple organs and the differentiation of pathological tissues in medical imaging are crucial but challenging, especially for nuanced classifications and…

cs.CV2024

BGDB: Bernoulli-Gaussian Decision Block with Improved Denoising Diffusion Probabilistic Models

Chengkun Sun, Jinqian Pan, Russell Stevens Terry +2

Generative models can enhance discriminative classifiers by constructing complex feature spaces, thereby improving performance on intricate datasets. Conventional methods typically…

cs.RO2024

Towards Unified Interactive Visual Grounding in The Wild

Jie Xu, Hanbo Zhang, Qingyi Si +3

Interactive visual grounding in Human-Robot Interaction (HRI) is challenging yet practical due to the inevitable ambiguity in natural languages. It requires robots to disambiguate…

cs.RO20231 cited

InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot Interactions

Hanbo Zhang, Jie Xu, Yuchen Mo +1

Ambiguity is ubiquitous in human communication. Previous approaches in Human-Robot Interaction (HRI) have often relied on predefined interaction templates, leading to reduced perfo…

cs.HC20212 cited

Improving Intention Detection in Single-Trial Classification through Fusion of EEG and Eye-tracker Data

Xianliang Ge, Yunxian Pan, Sujie Wang +5

Intention decoding is an indispensable procedure in hands-free human-computer interaction (HCI). Conventional eye-tracking system using single-model fixation duration possibly issu…