2 citations · 5 across the 8 of their papers we have counts for
8 papers
RoMo: A Large-Scale, Richly Organized Dataset and Semantic Taxonomy for Human Motion Generation
Jiahao Zhang, Joseph Liu, Young-Yoon Lee +9
Success in generative modeling across language, image, and video demonstrates that large, well-curated datasets are the key driver for building capable models. 3D Human motion, how…
On the Equivalency, Substitutability, and Flexibility of Synthetic Data
Che-Jui Chang, Danrui Li, Seonghyeon Moon +1
We study, from an empirical standpoint, the efficacy of synthetic data in real-world scenarios. Leveraging synthetic data for training perception models has become a key strategy e…
M3Act: Learning from Synthetic Human Group Activities
Che-Jui Chang, Danrui Li, Deep Patel +7
The study of complex human interactions and group activities has become a focal point in human-centric computer vision. However, progress in related tasks is often hindered by the…
MSI: Maximize Support-Set Information for Few-Shot Segmentation
Seonghyeon Moon, Samuel S. Sohn, Honglu Zhou +4
FSS(Few-shot segmentation) aims to segment a target class using a small number of labeled images(support set). To extract information relevant to the target class, a dominant appro…
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction
Gang Qiao, Kaidong Hu, Seonghyeon Moon +4
Learning-based approaches to modeling crowd motion have become increasingly successful but require training and evaluation on large datasets, coupled with complex model selection a…
HM: Hybrid Masking for Few-Shot Segmentation
Seonghyeon Moon, Samuel S. Sohn, Honglu Zhou +4
We study few-shot semantic segmentation that aims to segment a target object from a query image when provided with a few annotated support images of the target class. Several recen…