67 citations · 164 across the 15 of their papers we have counts for
15 papers
Affinity Contrastive Learning for Skeleton-based Human Activity Understanding
Hongda Liu, Yunfan Liu, Min Ren +3
In skeleton-based human activity understanding, existing methods often adopt the contrastive learning paradigm to construct a discriminative feature space. However, many of these a…
SkeletonAgent: An Agentic Interaction Framework for Skeleton-based Action Recognition
Hongda Liu, Yunfan Liu, Changlu Wang +2
Recent advances in skeleton-based action recognition increasingly leverage semantic priors from Large Language Models (LLMs) to enrich skeletal representations. However, the LLM is…
Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition
Hongda Liu, Yunfan Liu, Min Ren +3
In skeleton-based action recognition, a key challenge is distinguishing between actions with similar trajectories of joints due to the lack of image-level details in skeletal repre…
Spatial Transform Decoupling for Oriented Object Detection
Hongtian Yu, Yunjie Tian, Qixiang Ye +1
Vision Transformers (ViTs) have achieved remarkable success in computer vision tasks. However, their potential in rotation-sensitive scenarios has not been fully explored, and this…
3D-Aware Adversarial Makeup Generation for Facial Privacy Protection
Yueming Lyu, Yue Jiang, Ziwen He +3
The privacy and security of face data on social media are facing unprecedented challenges as it is vulnerable to unauthorized access and identification. A common practice for solvi…
Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation
Yunfan Liu, Qi Li, Zhenan Sun +1
One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces. Specifically, the suboptimally disentangled identity information of…
GAN-based Facial Attribute Manipulation
Yunfan Liu, Qi Li, Qiyao Deng +2
Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face image to render desired attributes, which has received significant attention due to its broad practica…
Style Intervention: How to Achieve Spatial Disentanglement with Style-based Generators?
Yunfan Liu, Qi Li, Zhenan Sun +1
Generative Adversarial Networks (GANs) with style-based generators (e.g. StyleGAN) successfully enable semantic control over image synthesis, and recent studies have also revealed…
Reference-guided Face Component Editing
Qiyao Deng, Jie Cao, Yunfan Liu +3
Face portrait editing has achieved great progress in recent years. However, previous methods either 1) operate on pre-defined face attributes, lacking the flexibility of controllin…
A3GAN: An Attribute-aware Attentive Generative Adversarial Network for Face Aging
Yunfan Liu, Qi Li, Zhenan Sun +1
Face aging, which aims at aesthetically rendering a given face to predict its future appearance, has received significant research attention in recent years. Although great progres…
Age Progression and Regression with Spatial Attention Modules
Qi Li, Yunfan Liu, Zhenan Sun
Age progression and regression refers to aesthetically render-ing a given face image to present effects of face aging and rejuvenation, respectively. Although numerous studies have…
Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework
Caiyong Wang, Yuhao Zhu, Yunfan Liu +2
Iris segmentation and localization in non-cooperative environment is challenging due to illumination variations, long distances, moving subjects and limited user cooperation, etc.…
Attribute-aware Face Aging with Wavelet-based Generative Adversarial Networks
Yunfan Liu, Qi Li, Zhenan Sun
Since it is difficult to collect face images of the same subject over a long range of age span, most existing face aging methods resort to unpaired datasets to learn age mappings.…
Learning to Detect Human-Object Interactions
Yu-Wei Chao, Yunfan Liu, Xieyang Liu +2
We study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that co…
Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection
Hongwen Zhang, Qi Li, Zhenan Sun +1
Facial landmark detection is an important yet challenging task for real-world computer vision applications. This paper proposes an effective and robust approach for facial landmark…