5 papers
Group-DINOmics: Incorporating People Dynamics into DINO for Self-supervised Group Activity Feature Learning
Ryuki Tezuka, Chihiro Nakatani, Norimichi Ukita
This paper proposes Group Activity Feature (GAF) learning without group activity annotations. Unlike prior work, which uses low-level static local features to learn GAFs, we propos…
End-to-End Shared Attention Estimation via Group Detection with Feedback Refinement
Chihiro Nakatani, Norimichi Ukita, Jean-Marc Odobez
This paper proposes an end-to-end shared attention estimation method via group detection. Most previous methods estimate shared attention (SA) without detecting the actual group of…
Human-in-the-loop Adaptation in Group Activity Feature Learning for Team Sports Video Retrieval
Chihiro Nakatani, Hiroaki Kawashima, Norimichi Ukita
This paper proposes human-in-the-loop adaptation for Group Activity Feature Learning (GAFL) without group activity annotations. This human-in-the-loop adaptation is employed in a g…
Dynamic Group Detection using VLM-augmented Temporal Groupness Graph
Kaname Yokoyama, Chihiro Nakatani, Norimichi Ukita
This paper proposes dynamic human group detection in videos. For detecting complex groups, not only the local appearance features of in-group members but also the global context of…
Size-Variable Virtual Try-On with Physical Clothes Size
Yohei Yamashita, Chihiro Nakatani, Norimichi Ukita
This paper addresses a new virtual try-on problem of fitting any size of clothes to a reference person in the image domain. While previous image-based virtual try-on methods can pr…