2 citations · 2 across the 2 of their papers we have counts for
7 papers
Incorporating simulated spatial context information improves the effectiveness of contrastive learning models
Lizhen Zhu, James Z. Wang, Wonseuk Lee +1
Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spat…
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion
James Z. Wang, Sicheng Zhao, Chenyan Wu +4
The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding o…
Learning Emotion Representations from Verbal and Nonverbal Communication
Sitao Zhang, Yimu Pan, James Z. Wang
Emotion understanding is an essential but highly challenging component of artificial general intelligence. The absence of extensively annotated datasets has significantly impeded a…
Bodily expressed emotion understanding through integrating Laban movement analysis
Chenyan Wu, Dolzodmaa Davaasuren, Tal Shafir +2
Body movements carry important information about a person's emotions or mental state and are essential in daily communication. Enhancing the ability of machines to understand emoti…
Learning to Adapt to Online Streams with Distribution Shifts
Chenyan Wu, Yimu Pan, Yandong Li +1
Test-time adaptation (TTA) is a technique used to reduce distribution gaps between the training and testing sets by leveraging unlabeled test data during inference. In this work, w…
Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans
Haomiao Ni, Yuan Xue, Kelvin Wong +4
Accurate infarct segmentation in non-contrast CT (NCCT) images is a crucial step toward computer-aided acute ischemic stroke (AIS) assessment. In clinical practice, bilateral symme…