[1]   Asynchronous Active Learning with Distributed Label Querying (Oral)

• Sheng-Jun Huang, Chen-Chen Zong (学生一作), Kun-Peng Ning, Hai-Bo Ye
• International Joint Conference on Artificial Intelligence, IJCAI-21 for short

[2]   Multi-Label Knowledge Distillation

• Peng-Hui Yang*, Ming-Kun Xie*, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
• International Conference on Computer Vision, ICCV-23 for short

[3]    Dirichlet-Based Prediction Calibration for Learning with Noisy Labels (Oral)

• Chen-Chen Zong, Ye-Wen Wang, Ming-Kun Xie, Sheng-Jun Huang
• AAAI Conference on Artificial Intelligence, AAAI-24 for short

[4]   Dirichlet-Based Coarse-to-Fine Example Selection for Open-Set Annotation

• Ye-Wen Wang*, Chen-Chen Zong*, Ming-Kun Xie, Sheng-Jun Huang
• IEEE International Conference on Multimedia and Expo, ICME-24 for short

[5]    CACL: Community-Aware Heterogeneous Graph Contrastive Learning for Social Media Bot Detection (Findings)

• Si-Rry Chen*, Shuo Feng*, Song-Song Liang, Chen-Chen Zong, Jing Li, Pi-Ji Li
• The Association for Computational Linguistics, ACL-24 for short

[6]    Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning

• Hao-Zhe Liu*, Ming-Kun Xie*, Chen-Chen Zong, Sheng-Jun Huang
• SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-24 for short

[7]    Bidirectional Uncertainty-Based Active Learning for Open-Set Annotation

• Chen-Chen Zong, Ye-Wen Wang, Kun-Peng Ning, Hai-Bo Ye, Sheng-Jun Huang
• European Conference on Computer Vision, ECCV-24 for short

[1]    LMS-Based Background Calibration of Bit Weights in SAR ADC Using Reinforcement Learning Optimization

• Xin-Yan Song, Qiao Meng, Yu-Jia Huang, Chen-Chen Zong, Yi-Shuo Meng
• Circuits, Systems & Signal Processing, CSSP-23 for short

[2]    Combating Noisy and Long-Tailed Data by Reweighting Examples with Negative Learning

• Bo-Shi Zou, Ming Yang, Chen-Chen Zong, Ming-Kun Xie, Sheng-Jun Huang
• Journal of Computer Applications, JOCA-23 for short

[1]   Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting         LMNL        2022

• Chen-Chen Zong, Zheng-Tao Cao, Hong-Tao Guo, Yun Du, Ming-Kun Xie, Shao-Yuan Li , Sheng-Jun Huang
• 2rd place @ Task 2-1 (Detection), 3rd place @ Task 1-1 (Learning) [Certification]
• Part of the 31th International Joint Conference on Artificial Intelligence, IJCAI-22 for short

[1]   GAN-based magic pen Ma Liang cartoon picture production

• Project description: Artificial intelligence is more and more widely used in life, and the scenes in many movies continue to become a reality. In particular, the great progress of the generation confrontation network (GAN), whether writing poetry, music or painting, has confused the false with the true, and even exceeded the level of human beings. Can AI also turn our childhood fairy tales into reality and turn children's or adults' graffiti into exquisite cartoons, paintings of other styles, or even 3D animation? Based on the idea of generation against network in deep learning, this project can turn the user's graffiti into a number of exquisite cartoons with rich colors and different styles.
• Duration: 2019.7-2019.9
• Place: Deecamp2019, Held at the University of the Chinese Academy of Sciences
• Role: Team member

[2]   Data validity verification in video HDR task

• Project description: Live HDR is mainly used to solve the inconsistency between preview and live shooting. Preview is the image seen in the lens, and real shooting is the image taken. Because the paths of preview stream and photo stream are different, there are great differences between them, mainly reflected in the brightening effect of dark area, the restoration of details in high exposure area, the brightness of night scene (preview is much worse than real shooting results), etc. The project aims to improve the consistency between preview and real shooting from the perspective of data effectiveness.
• Duration: 2021.5-2023.4
• Place: Huawei Nanjing R&D Center in the early stage and NUAA in the later stage
• Role: Team leader