activity
20202026
most citedPredicate correlation learning for scene graph generation

31 citations · 51 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

AnyCrowd: Instance-Isolated Identity-Pose Binding for Arbitrary Multi-Character Animation

Zhenyu Xie, Ji Xia, Michael Kampffmeyer +9

Controllable character animation has advanced rapidly in recent years, yet multi-character animation remains underexplored. As the number of characters grows, multi-character refer…

cs.CV2026

Steering Video Diffusion Transformers with Massive Activations

Xianhang Cheng, Yujian Zheng, Zhenyu Xie +2

In this work, we study the role of Massive Activations (MAs), which are rare, high-magnitude spikes confined to a few fixed hidden dimensions in video diffusion transformers (DiTs)…

cs.CV2025

LUIVITON: Learned Universal Interoperable VIrtual Try-ON

Cong Cao, Xianhang Cheng, Jingyuan Liu +5

To enable large-scale reuse of real-world 3D assets, where garments and characters rarely share skeletons, templates, or dense correspondences, we present a fully automated virtual…

cs.LG2023★ 2 cited

oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation

Jianhui Li, Zhennan Qin, Yijie Mei +11

With the rapid development of deep learning models and hardware support for dense computing, the deep learning workload characteristics changed significantly from a few hot spots o…

cs.CV2021★ 31 cited

Predicate correlation learning for scene graph generation

Leitian Tao, Li Mi, Nannan Li +3

For a typical Scene Graph Generation (SGG) method, there is often a large gap in the performance of the predicates' head classes and tail classes. This phenomenon is mainly caused…

cs.CV2020★ 18 cited

Multiple Video Frame Interpolation via Enhanced Deformable Separable Convolution

Xianhang Cheng, Zhenzhong Chen

Generating non-existing frames from a consecutive video sequence has been an interesting and challenging problem in the video processing field. Typical kernel-based interpolation m…