PUBLICATIONS

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Books
Selected Conference Publications 
  • H. Dong, Z. Ding, S. Zhang, eds. “Deep Reinforcement Learning: Fundamentals, Research and Applications”, Springer Nature, 2020 Jun 29, (Electronic Edition 95,000 downloads; selectd to Annual High-Impact Publications in Computer Science by Chinese researchers).

Journal Publications
  • S. Zhou, S. Zhang*, et al. “Active Gradual Domain Adaptation: Dataset and Approach”, IEEE Transactions on Multimedia (TMM IF 8.182), 2022.

  • A. Dehban, S. Zhang, N. Cauli, L. Jamone, J. Santos-Victor. Learning Deep Features for Robotic Inference from Physical Interactions. IEEE Transactions on Cognitive and Developmental Systems (TCDS IF 4.546). 2022 Feb 17.

  • S. Zhou, S. Zhang*, et al. “Caching in Dynamic Environments: a Near-optimal Online Learning Approach”, IEEE Transactions on Multimedia (TMM IF 8.182), 2021.

  • S. Zhao, X. Yue*, S. Zhang*, B. Li, H. Zhao, B. Wu, R. Krishna, JE. Gonzalez, AL. Vincentelli, SA. Seshia, K. Keutzer. “A Review of Single-Source Deep Unsupervised Visual Domain Adaptation”, IEEE Transactions on Neural Networks and Learning Systems (IF 14.255). 2020.

  • C. Li, X. Peng, S. Zhang, H. Peng, P. Yu, M. He, L. Du, L. Wang, “Modeling relation paths for knowledge base completion via joint adversarial training”, Knowledge-Based Systems (IF 8.038), 2020: 105865.

  • C. Zhu, H. Jia, S. Zhang, X. Huang, X. Xie and W. Gao, “On a Highly Efficient RDO-based Mode Decision Pipeline Design,” IEEE Transactions on Multimedia (TMM IF 8.182), 15.8 (2013): 1815-1829.

  • Y Zou, S Zhang, Y Li, R Li, Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation, Neural Information Processing Systems (NeurIPS) 2022.

  • H Zhou, S Xiao, S Zhang, J Peng, S Zhang, J Li, Jump Self-attention: Capturing High-order Statistics in Transformers, Neural Information Processing Systems (NeurIPS) 2022.

  • X Wei, Y Zhang, X Zhang, R Gong, S Zhang, Q Zhang, F Yu, X Liu, Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models, Neural Information Processing Systems (NeurIPS) 2022.

  • J. Yu, J. Liu, X.Wei, H. Zhou, Y. Nakata, D. Gudovskiy, T. Okuno, J. Li, K. Keutzer, S. Zhang*, MTTrans: Cross-Domain Object Detection with Mean Teacher Transformer, 17th European Conference on Computer Vision (ECCV) 2022.

  • X. Li, J. Liu, S.Wang, C. Lyu, M. Lu, Y. Chen, A. Yao, Y. Guo, S. Zhang*, Efficient Meta-Tuning for Content-aware Neural Video Delivery, 17th European Conference on Computer Vision (ECCV) 2022.

  • C. Zhang#, M. Zhang#, S. Zhang#, et al. "Delving deep into the generalization of vision transformers under distribution shifts.", Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

  • T. Li, X. Chen, Z. Dong, K. Keutzer, S. Zhang*. Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data, International Joint Conference on Artificial Intelligence (IJCAI), 2022.

  • M. Liu, Q. Zhou, H. Zhao, L. Du, Y. Du, J. Li, K. Keutzer, S. Zhang*. Prototypical Supervised Contrastive Learning for LiDAR Point Cloud Panoptic Segmentation, International Conference on Robotics and Automation (ICRA), 2022.

  • S. Zhou, H. Zhao, S. Zhang*, et al. “Online Continual Adaptation with Active Self-Training”, Artificial Intelligence and Statistics Conference (AISTATS), 2022.

  • CJ . Reed, X. Yue, A. Nrusimha, S. Ebrahimi, V. Vijaykumar, R. Mao, B. Li, S. Zhang, D. Guillory, S. Metzger, K. Keutzer. Self-supervised pretraining improves self-supervised pretraining. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022 (pp. 2584-2594).

  • Z. Luo, Z. Cai, C. Zhou, G. Zhang, H. Zhao, S. Yi, S. Lu, H. Li, S. Zhang, Z. Liu, “Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency”, International Conference on Computer Vision (ICCV), 2021.

  • Y. Liu, Q. Fan, S. Zhang, H. Dong, T. Funkhouser, L. Yi, “Contrastive Multimodal Fusion with TupleInfoNCE”, International Conference on Computer Vision (ICCV), 2021.

  • Y. Zou, S. Zhang, J. Yu. Y. Tian, J. Moura, “Revisiting Mid-Level Patterns for Distant-Domain Few-Shot Recognition”, ACM Multimedia (ACM MM), 2021.

  • Y. Zou, S. Zhang, G. Chen. Y. Tian, K. Keutzer, J. Moura, “Annotation-Efficient Untrimmed Video Action Recognition”, ACM Multimedia (ACM MM), 2021.

  • H. Zhou, J. Li, J. Peng, S. Zhang, S. Zhang, “Triplet Attention: Rethinking the similarity in Transformers”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

  • B. Li#, Y. Wang#, S. Zhang#, D. Li, T. Darrell, K. Keutzer, H. Zhao, “Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation”, Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

  • X. Yue, Z. Zheng, S. Zhang, Y. Gao, T. Darrell, K. Keutzer, AL. Vincentelli, “Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation”, Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

  • X Ma, X Kong, S Zhang, E Hovy, “Decoupling Global and Local Representations via Invertible Generative Flows”, Accepted by International Conference on Learning Representations (ICLR), 2021.

  • T. Li, X. Chen, S. Zhang*, Z. Dong*, K. Keutzer, “Cross-Domain Sentiment Classification With Contrastive Learning and Mutual Information Maximization”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.

  • H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, W. Zhang, “Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting”, Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. AAAI Best Paper Award. (1st place of Trending Research on PaperWithCode, Github Star 1,600+, Visit 60,000+, Integrated into Mindspore Platform of Huawei, Applied to 1,400+ Transformer in Shandong and Anhui)

  • Y. Zou, S. Zhang, K. Chen, Y. Wang, J. Moura, Y. Tian, “Compositional Few-Shot Recognition with Primitive Discovery and Enhancing”, ACM Multimedia (ACM MM), 2020, Oral presentation.

  • X. Sun, Y. Xu, P. Cao, Y. Kong*, L. Hu, S. Zhang*, Y.Wang, “TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning”, 16th European Conference on Computer Vision (ECCV) 2020, Oral presentation (Top 2%).

  • K. Mei, C. Zhu, J. Zou, S. Zhang, “Instance Adaptive Self-Training for Unsupervised Domain Adaptation”, 16th European Conference on Computer Vision (ECCV), 2020.

  • C. Song, S. Zhang, N. Sadoughi, P. Xie, and E. Xing. "Generalized Zero-shot Text Classification for ICD Coding", International Joint Conference on Artificial Intelligence (IJCAI), 2020.

  • S. Zhao#, G. Wang#, S. Zhang#, Y. Gu, Y. Li, Z. Song, P. Xu, R. Hu, H. Chai, K. Keutzer, “Multi-source Distilling Domain Adaptation”, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) 2020, Oral presentation (Top 3%).

  • J. Ni, S. Zhang, H, Xie, “Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning”, Advances in Neural Information Processing Systems (NeurIPS), 2019.

  • X. Ma, X. Kong, S. Zhang, E. Hovy, “MaCow: Masked Convolutional Generative Flow”, Advances in Neural Information Processing Systems (NeurIPS), 2019.

  • H. Zhao#, S. Zhang#, G. Wu, J. Costeira, J. Moura, G. J. Gordon, “Adversarial Multiple Source Domain Adaptation”, Advances in Neural Information Processing Systems (NeurIPS), 2018.

  • S. Zhang, X. Shen, Z. Lin, R. Mech, J. Costeira, J. Moura, “Learning to Understand Image Blur”, Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

  • H. Zhao#, S. Zhang#, G. Wu, J. Costeira, J. Moura, G. J. Gordon, “Multiple Source Domain Adaptation with Adversarial Learning”, International Conference on Learning Representations (ICLR), invited to workshop, 2018.

  • R. Das, A. Gadre,  S. Zhang, S. Kumar, and J. Moura, “A Deep Learning Approach to IoT Authentication”, accepted by IEEE International Conference on Communications (ICC), 2018.

  • S. Zhang#, G. Wu#, J. Costeira, J. Moura, “FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras”, International Conference on Computer Vision (ICCV), 2017.

  • S. Zhang, G. Wu, J. Costeira, J. Moura, “Understanding Traffic Density from Large-Scale Web Camera Data”, accepted by Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

  • F. Xia#, S. Zhang#, “Block-Coordinate Frank-Wolfe Optimization for Counting Objects in Images”, Advances in Neural Information Processing Systems (NIPS) Workshop, 2016.

  • E.Toropov, L. Gui, S. Zhang, S. Kottur, J. M. F. Moura, “Traffic Flow from a Low Frame Rate City Camera,” IEEE International Conference on Image Processing (ICIP), 2015.

  • S. Zhang, X. Li, R.D. Blanton, J. Silva, J. M. Carulli, K. M. Butler, “Bayesian Model Fusion: Enabling Test Cost Reduction of Analog/RF Circuits via Wafer-level Spatial Variation Modeling,” International Test Conference (ITC), 2014.

  • Y. Li, S. Zhang, H. Jia, X. Xie, and W. Gao, “A High-throughput Low-latency Arithmetic Encoder Design for HDTV,” The IEEE International Symposium on Circuits and Systems (ISCAS), 2013.

  • S Zhang, K Wei, H Jia, X Xie, W Gao. "An efficient foreground-based surveillance video coding scheme in low bit-rate compression" Visual Communications and Image Processing (VCIP), 2012 IEEE, 1-6

  • K. Wei, R. Zhou, S. Zhang, H. Jia, D. Xie, and W. Gao, “An Optimized Hardware Video Encoder For AVS With Level C+ Data Reuse Scheme For Motion Estimation,” IEEE International Conference on Multimedia & Expo (ICME), 2012.