3 citations · 3 across the 6 of their papers we have counts for
6 papers
DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation
Mu Chen, Liulei Li, Wenguan Wang +1
Top-leading solutions for Video Scene Graph Generation (VSGG) typically adopt an offline pipeline. Though demonstrating promising performance, they remain unable to handle real-tim…
UAHOI: Uncertainty-aware Robust Interaction Learning for HOI Detection
Mu Chen, Minghan Chen, Yi Yang
This paper focuses on Human-Object Interaction (HOI) detection, addressing the challenge of identifying and understanding the interactions between humans and objects within a given…
PiPa++: Towards Unification of Domain Adaptive Semantic Segmentation via Self-supervised Learning
Mu Chen, Zhedong Zheng, Yi Yang
Unsupervised domain adaptive segmentation aims to improve the segmentation accuracy of models on target domains without relying on labeled data from those domains. This approach is…
General and Task-Oriented Video Segmentation
Mu Chen, Liulei Li, Wenguan Wang +2
We present GvSeg, a general video segmentation framework for addressing four different video segmentation tasks (i.e., instance, semantic, panoptic, and exemplar-guided) while main…
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
Mu Chen, Zhedong Zheng, Yi Yang
Scene segmentation via unsupervised domain adaptation (UDA) enables the transfer of knowledge acquired from source synthetic data to real-world target data, which largely reduces t…
PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
Mu Chen, Zhedong Zheng, Yi Yang +1
Unsupervised Domain Adaptation (UDA) aims to enhance the generalization of the learned model to other domains. The domain-invariant knowledge is transferred from the model trained…