77 citations · 157 across the 12 of their papers we have counts for
12 papers
Dual-Stream Collaborative Transformer for Image Captioning
Jun Wan, Jun Liu, Zhihui lai +1
Current region feature-based image captioning methods have progressed rapidly and achieved remarkable performance. However, they are still prone to generating irrelevant descriptio…
Supervision-by-Hallucination-and-Transfer: A Weakly-Supervised Approach for Robust and Precise Facial Landmark Detection
Jun Wan, Yuanzhi Yao, Zhihui Lai +3
High-precision facial landmark detection (FLD) relies on high-resolution deep feature representations. However, low-resolution face images or the compression (via pooling or stride…
FGTBT: Frequency-Guided Task-Balancing Transformer for Unified Facial Landmark Detection
Jun Wan, Xinyu Xiong, Ning Chen +3
Recently, deep learning based facial landmark detection (FLD) methods have achieved considerable success. However, in challenging scenarios such as large pose variations, illuminat…
Precise Facial Landmark Detection by Dynamic Semantic Aggregation Transformer
Jun Wan, He Liu, Yujia Wu +3
At present, deep neural network methods have played a dominant role in face alignment field. However, they generally use predefined network structures to predict landmarks, which t…
Precise Facial Landmark Detection by Reference Heatmap Transformer
Jun Wan, Jun Liu, Jie Zhou +5
Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results. However, when the…
Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network
Jun Wan, Hui Xi, Jie Zhou +4
Current fully-supervised facial landmark detection methods have progressed rapidly and achieved remarkable performance. However, they still suffer when coping with faces under larg…