164 citations
- Tsinghua UniversityCN5 papers
- University of Chinese Academy of SciencesCN4 papers
- Chinese Academy of SciencesCN3 papers
- Beijing Institute of TechnologyCN2 papers
- Beijing University of Posts and TelecommunicationsCN2 papers
- Institute of AutomationCN2 papers
- Alibaba Group (China)CN1 paper
- Beijing Institute of Big Data ResearchCN1 paper
- Beijing Jiaotong UniversityCN1 paper
- Beijing Radiation CenterCN1 paper
- Beijing University of Civil Engineering and ArchitectureCN1 paper
- Chinese Academy of Social SciencesCN1 paper
19 papers
Hybrid supervised instance segmentation by learning label noise suppression
Linwei Chen, Ying Fu, Shaodi You +1
To reach top accuracy, current fully supervised instance segmentation methods severely rely on large-scale pixel-wise labeled datasets. They are usually expensive and time-consumin…
OCFER-Net: Recognizing Facial Expression in Online Learning System
Yi Huo, Lei Zhang
Recently, online learning is very popular, especially under the global epidemic of COVID-19. Besides knowledge distribution, emotion interaction is also very important. It can be o…
VAD-Net: Multidimensional Facial Expression Recognition in Intelligent Education System
Yi Huo, Yun Ge
Current FER (Facial Expression Recognition) dataset is mostly labeled by emotion categories, such as happy, angry, sad, fear, disgust, surprise, and neutral which are limited in ex…
Multi-modal Reference Learning for Fine-grained Text-to-Image Retrieval
Zehong Ma, Hao Chen, Wei Zeng +2
Fine-grained text-to-image retrieval aims to retrieve a fine-grained target image with a given text query. Existing methods typically assume that each training image is accurately…
Soften the Mask: Adaptive Temporal Soft Mask for Efficient Dynamic Facial Expression Recognition
Meng-zhu Li, Quanxing Zha, Hongjun Wu
Dynamic Facial Expression Recognition (DFER) facilitates the understanding of psychological intentions through non-verbal communication. Existing methods struggle to manage irrelev…
Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification
Zitai Wang, Qianqian Xu, Zhiyong Yang +4
Multi-label ranking, which returns multiple top-ranked labels for each instance, has a wide range of applications for visual tasks. Due to its complicated setting, prior arts have…