[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]   灵魂画手：随手涂鸦变成精美卡通画

• 项目介绍： 人工智能在生活中应用愈加广泛，很多电影里面的情景不断变成现实。尤其是生成对抗网络（GAN）的长足进步，不管是写诗作曲还是创作画作等，都做到了以假乱真，甚至超出人类的水平。那AI是不是也可以让我们儿时的童话故事变成现实，将儿童或者成人随手涂鸦变成精美的卡通画，或者其他风格的画作，甚至是3D动画？本项目基于深度学习中的生成对抗网络思想，能将用户的随手涂鸦，变成多张色彩丰富、风格各异的精美卡通画。
• 周期： 2019.7-2019.9
• 地点： Deecamp2019，于中国科学院大学
• 担任角色： 小组成员

[2]   视频HDR任务数据有效性验证

• 项目介绍： Live HDR，主要是解决预览和实拍不一致的问题。预览就是镜头中看到的图像，实拍就是拍摄出来的图像。因为预览流和拍照流的通路不一样，两者存在较大差异，主要体现在暗区提亮效果，高曝光区域细节恢复，夜景场景的亮度（预览比实拍结果要差很多）等。项目旨在从数据有效性的层面，提升预览与实拍之间的一致性。
• 周期： 2021.5-2023.4
• 地点： 前期于华为南京研发中心，后期于 南京航空航天大学
• 担任角色： 小组负责人