教师简介

王可泽老师分别于2012年和2017年在中山大学获得学士和博士学位，随后前往美国加州大学洛杉矶分校做博士后研究员。他于2019年获得香港理工大学哲学博士学位。他长期致力于视觉计算与推理的基础研究，提出“引导-自步-协同”长效自主学习等基础学习范式，对混乱数据的自主学习等关键难题提出了有效的解决方案，并取得具有一定影响力的学术成果。他在中科院一区/CCF-A期刊/会议上发表论文30余篇，包括IEEE PAMI（2篇）、TNNLS、IJCV、TIP、TMM、TCSVT，以及CVPR、ICCV等领域顶级会议论文20余篇；Google学术被引用累计1300余次，单篇最高引用342次；两篇论文被评为ESI高被引论文；以第一完成人获得人工智能学会优秀博士学位论文奖（每年评选不超过10名），第二完成人获得吴文俊人工智能自然科学二等奖；获得4项授权国家发明专利。他的科研工作被广泛关注并引用，引用源包括IEEE TPAMI、TNNLS、TIP、CVPR和ICCV等国际顶级学术期刊会议。

研究领域

计算机视觉中的物体检测、三维二维人体维姿估计；

机器学习中的主动学习、半监督学习、自监督学习；

可解释人工智能

获奖及荣誉

2020 Volunteer highlight of IEEE Transactions on Pattern Analysis and Machine Intelligence 

2019 中国人工智能学会优秀博士学位论文奖(每年最多评10名)

2018 吴文俊人工智能自然科学奖（排名第二）

2016 国家博士研究生奖学金(排名前1%)

2015 国家博士研究生奖学金(排名前1%)

海外经历

美国加州大学洛杉矶分校博士后研究员

主要学术兼职

担任国际知名期刊Image and Vision Computing的执行编辑

担任国际知名期刊The Visual Computer的副编辑

担任以下期刊的审稿人:

– IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

– IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

– Applied Soft Computing Journal

– IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)

– IEEE Transactions on Image Processing (TIP)

– IEEE Transactions on Multimedia (TMM)

– Pattern Recognition (PR)

– Neural Networks

– Neurocomputing

 • 担任以下会议的审稿人:

– International Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2019, 2020, 2021

– European Conference on Computer Vision (ECCV) 2020

– Neural Information Processing Systems (NeurIPS) 2020

– AAAI Conference on Artificial Intelligence (AAAI) 2019, 2020, 2021

– IEEE International Conference on Computer Vision (ICCV) 2019, 2021

– International Joint Conference on Artificial Intelligence (IJCAI) 2018, 2019, 2020

– IEEE International Conference on Robotics and Automation (ICRA) 2021

– Winter Conference on Applications of Computer Vision (WACV) 2021

– Asian Conference on Computer Vision (ACCV) 2018

– International Conference on Pattern Recognition (ICPR) 2020

代表性论著

注：(*)表示通讯作者，(+)表示共同作者

[1] Qingxing Cao, Wentao Wan, Keze Wang*, Xiaodan Liang*, Liang Lin. Linguistically Routing Capsule Network for Out-of-distribution Visual Question Answering. In ICCV 2021.

[2] Guangrun Wang, Keze Wang*, Guangcong Wang, Phillip HS Torr, Liang Lin. Solving Inefficiency of Self-supervised Representation Learning. In ICCV 2021.

[3] Arjun Akula+, Keze Wang+, Changsong Liu, Sari Saba-Sadiya, Hongjing Lu, Sinisa Todorovic, Joyce Chai, Song-Chun Zhu*, F-ToM: Explaining with Theory-of-Mind via Fault-Lines for Enhancing Human Trust in Image Recognition Models. In iScience, 2021.

[4] Yang Liu, Keze Wang*, Guanbin Li, Liang Lin. Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition. To appear in IEEE Transactions on Image Processing (T-IP), 2021.

[5] Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, and Pengxu Wei. 3D Human Pose Machines with Self-supervised Learning. In IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 42, no. 5, pp. 1069– 1082, 2020.

[6] Junfan Lin, Zhongzhan Huang, Keze Wang*, Xiaodan Liang, Weiwei Chen, Liang Lin. Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp. In Proc. of International Conference on Robotics and Automation (ICRA), 2021.

[7] Qingxing Cao, Bailin Li, Xiaodan Liang, Keze Wang, Liang Lin*. Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation Embedding. To appear in IEEE Transactions on Neural Networks and Learning System (TNNLS), 2021.

[8] Guangrun Wang, Guangcong Wang, Keze Wang, Xiaodan Liang, Liang Lin, Grammatically Recognizing Images with Tree Convolution. in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.

[9] Guangrun Wang, Keze Wang, Liang Lin. Adaptively Connected Neural Networks. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

[10] Keze Wang, Liang Lin, Xiaopeng Yan, Ziliang Chen, Dongyu Zhang, Lei Zhang. Self-supervised Sample Mining with Switchable Selection Criteria for Object Detection. In IEEE Transactions on Neural Networks and Learning System (T-NNLS), vol. 30, no. 3, pp. 834–850, 2019.

[11] Yukai Shi, Guanbin Li, Qingxing Cao, Keze Wang, Liang Lin. Face hallucination by attentive sequence optimization with reinforcement learning. In IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 42, no. 11, pp. 2809–2824, 2019.

[12] Hefeng Wu, Yafei Hu, Keze Wang, Hanhui Li, Lin Nie, Hui Cheng. Instance-aware representation learning and association for online multiperson tracking. In Pattern Recognition, vol. 94, pp. 25–34, 2019.

[13] Keze Wang, Liang Lin, Chuangjie Ren, Wei Zhang, Wenxiu Sun. Convolutional Memory Blocks for Depth Data Representation Learning. In Proceedings of the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI), 2018.

[14] Keze Wang, Xiaopeng Yan, Dongyu Zhang, Lei Zhang, Liang Lin. Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

[15] Guanbin Li, Yuan Xie, Tianhao Wei, Keze Wang, Liang Lin. Flow Guided Recurrent Neural Encoder for Video Salient Object Detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

[16] Liang Lin, Keze Wang, Deyu Meng, Wangmeng Zuo, and Lei Zhang. Active Self-Paced Learning for Cost-Effective and Progressive Face Identification. In IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 40, no. 1, pp. 7–19, 2018.

[17] Hui Cheng, Zhuoqi Zheng, Jinhao He, Chongyu Chen, Keze Wang, Liang Lin. Embedding Temporally Consistent Depth Recovery for Real-time Dense Mapping in Visual-inertial Odometry. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 693–698, 2018.

[18] Keze Wang, Dongyu Zhang, Liang Lin, Ya Li and Ruimao Zhang, Cost-Effective Active Learning for Deep Image Classification. In IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), vol. 27, no. 12, pp. 2591– 2600, 2017.

[19] Yukai Shi, Keze Wang, Chongyu Chen, Li Xu and Liang Lin. Structure-Preserving Image Superresolution via Contextualized Multi-task Learning. In IEEE Transactions on Mulitmedia (TMM), vol. 19, no. 12, pp. 2804–2815, 2017.

[20] Ziliang Chen, Keze Wang, Xiao Wang, Pai Peng and Liang Lin. Deep Co-Space: Sample Mining Across Feature Transformation for SemiSupervised Learning. In IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2017.

[21] Mude Lin, Liang Lin, Xiaodan Liang, Keze Wang, and Hui Cheng, Recurrent 3D Pose Sequence Machines. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. (oral).

[22] Liang Lin, Keze Wang, Wangmeng Zuo, Meng Wang, Jiebo Luo, Lei Zhang, A Deep Structured Model with Radius–Margin Bound for 3D Human Activity Recognition. In International Journal of Computer Vision (IJCV), 118(2), 256- 273, 2016.

[23] Keze Wang, Liang Lin, Jiangbo Lu, Chenglong Li, Keyang Shi, PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Edge-Preserving Coherence. In IEEE Transactions on Image Processing (TIP), 24(10), 3019-3033, 2015.

[24] Keze Wang, Shengfu Zhai, Hui Cheng, Xiaodan Liang, Liang Lin. Human Pose Estimation from Still Depth Image via Inference Embedded Multi-task Learning. In Proceedings of the ACM International Conference on Multimedia (ACM MM), 2016. (oral, full paper)

[25] Keze Wang, Liang Lin, Wangmeng Zuo, Shuhang Gu, Lei Zhang. Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

[26] Keze Wang, Xiaolong Wang, Liang Lin, Meng Wang, Wangmeng Zuo, 3D human activity recognition with reconfigurable convolutional neural networks. In Proceedings of the ACM International Conference on Multimedia (ACM MM), pp. 97-106, 2014. (oral, full paper)

[27] Yukai Shi, Keze Wang, Li Xu, Liang Lin, Localand Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), 2016. (oral)

[28] Linnan Zhu, Keze Wang, Liang Lin, Lei Zhang, Learning a Lightweight Deep Convolutional Network for Joint Age and Gender Recognition. In Proceedings of the IEEE International Conference on Pattern Recognition (ICPR), 2016. (oral)

[29] Keyang Shi, Keze Wang, Jiangbo Lu, Liang Lin, Pisa: Pixelwise image saliency by aggregating complementary appearance contrast measures with spatial priors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2115-2122, 2013.