activity
20182023
most citedFocal and Global Knowledge Distillation for Detectors

20 citations · 45 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2023

ToonTalker: Cross-Domain Face Reenactment

Yuan Gong, Yong Zhang, Xiaodong Cun +5

We target cross-domain face reenactment in this paper, i.e., driving a cartoon image with the video of a real person and vice versa. Recently, many works have focused on one-shot t…

cs.CV2023★ 5 cited

Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation

Yingqing He, Menghan Xia, Haoxin Chen +8

Generating videos for visual storytelling can be a tedious and complex process that typically requires either live-action filming or graphics animation rendering. To bypass these c…

cs.CV2022

3D GAN Inversion with Facial Symmetry Prior

Fei Yin, Yong Zhang, Xuan Wang +8

Recently, a surge of high-quality 3D-aware GANs have been proposed, which leverage the generative power of neural rendering. It is natural to associate 3D GANs with GAN inversion m…

cs.CV2022★ 4 cited

MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model

Yatai Ji, Junjie Wang, Yuan Gong +6

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our i…

cs.CV2022★ 13 cited

Rethinking Knowledge Distillation via Cross-Entropy

Zhendong Yang, Zhe Li, Yuan Gong +4

Knowledge Distillation (KD) has developed extensively and boosted various tasks. The classical KD method adds the KD loss to the original cross-entropy (CE) loss. We try to decompo…

cs.CV2021★ 20 cited

Focal and Global Knowledge Distillation for Detectors

Zhendong Yang, Zhe Li, Xiaohu Jiang +4

Knowledge distillation has been applied to image classification successfully. However, object detection is much more sophisticated and most knowledge distillation methods have fail…

cs.SD2018★ 3 cited

Towards Learning Fine-Grained Disentangled Representations from Speech

Yuan Gong, Christian Poellabauer

Learning disentangled representations of high-dimensional data is currently an active research area. However, compared to the field of computer vision, less work has been done for…