1.1 Domain Adaptation
[1] J. Liu, H. Sun, Y. Jia, J. Qin, H. Zhou and N. Liu, SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain Adaptation. arXiv: https://arxiv.org/abs/2310.08928 Code: https://github.com/se111/SIDE
[2] X. Guan, H. Sun, N. Liu and H. Zhou, Polycentric Clustering and Structural Regularization for Source-free Unsupervised Domain Adaptation. Accepted by BMVC2022. arXiv: https://arxiv.org/abs/2210.07463 Code: https://github.com/Gxinuu/PCSR
[3] H. Sun, L. Lin, N. Liu and H. Zhou, Robust Ensembling Network for Unsupervised Domain Adaptation. In: Pham D.N., Theeramunkong T., Governatori G., Liu F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science, vol 13032. pp530-543, Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_40 arXiv: https://arxiv.org/abs/2108.09473 Code: https://github.com/1003389754/REN
[4] Sun H, Chen X, Wang L, Liang D, Liu N, Zhou H. C2DAN: An Improved Deep Adaptation Network with Domain Confusion and Classifier Adaptation. Sensors. 2020; 20(12):3606. DOI: 10.3390/s20123606
1.2 Saliency Detection
[1] Y. Lin, H. Sun, N. Liu, Y. Bian, J. Cen and H. Zhou, Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images
. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13529. pp 25-36. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-15919-0_3 arXiv: https://arxiv.org/abs/2207.01755 Code: https://github.com/NuaaYH/AGNet
[2] Y. Lin, H. Sun, N. Liu, Y. Bian, J. Cen and H. Zhou, A lightweight multi-scale context network for salient object detection in optical remote sensing images. 2022 26th International Conference on Pattern Recognition (ICPR), 2022, pp. 238-244, doi: https://10.1109/ICPR56361.2022.9956350. arXiv: https://arxiv.org/abs/2205.08959 Code: https://github.com/NuaaYH/MSCNet
[3] H. Sun, Y. Bian, N. Liu and H. Zhou, Multi-scale Edge-based U-shape Network for Salient Object Detection. In: Pham D.N., Theeramunkong T., Governatori G., Liu F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science, vol 13032, pp.501-514. Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_38. arXiv: https://arxiv.org/abs/2108.09408 Code: https://github.com/bellatong/MEUNet
[4] H. Sun, J. Cen, N. Liu, D. Liang and H. Zhou, MPI: Multi-receptive and parallel integration for salient object detection. in IET Image Processing, vol. 15, no. 13, pp. 3281-3291, 2021. DOI: 10.1049/ipr2.12324. arXiv: https://arxiv.org/abs/2108.03618 Code: https://github.com/nuaacj/mpi
[5] J. Cen, H. Sun, X. Chen, N. Liu, D. Liang and H. Zhou, WFNet: A Wider and Finer Network for Salient Object Detection. in IEEE Access,vol. 8, pp. 210418-210428, 2020, DOI: 10.1109/ACCESS.2020.3039890.
1.3 Fine-grained Classification & Deep Hashing
[1] H. Sun, W. Lang, C. Xu, N. Liu and H. Zhou. Graph-based discriminative features learning for fine-grained image retrieval. in Singal Processing: Image Communication, vol. 110, pp. 116885 ( 9 pages), 2022. DOI: 10.1016/j.image.2022.116885, Code:
https://github.com/wencylang/GDF-net
[2] Y. Qi, H. Sun, N. Liu and H. Zhou. A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning. In Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13631. pp 606-618,Springer, Cham. https://doi.org/10.1007/978-3-031-20868-3_45 arXiv: https://arxiv.org/abs/2210.12348 Code: https://github.com/qiyanhero/TDSNet[3] W. Lang, H. Sun, C. Xu, N. Liu and H. Zhou. Discriminative feature mining hashing for fine-grained image retrieval. in Journal of Visual Communication an Image Representation, vol. 87, pp. 103592 (11 pages), 2022. DOI: 10.1016/j.jvcir.2022.103592, Code:
https://github.com/wencylang/DFMH.git[4] H. Sun, Y. Fan, J. Shen, N. Liu, D. Liang and H. Zhou, A Novel Semantics-Preserving Hashing for Fine-Grained Image Retrieval. in IEEE Access, vol. 8, pp. 26199-26209, 2020. DOI: 10.1109/ACCESS.2020.2970223
1.4 Small Object Detection
1.5 Object Detection Based on Point Cloud and Image[1] H. Sun, W. Geng, J. Shen, N. Liu, D. Liang and H. Zhou, Deeper SSD: Simultaneous Up-sampling and Down-sampling for Drone Detection, KSII Transactions on Internet and Information Systems, vol. 14, no. 12, pp. 4795-4815, 2020. DOI: 10.3837/tiis.2020.12.010.
[2] H. Sun, J. Yang, J. Shen, D. Liang, N. Liu and H. Zhou, TIB-Net: Drone Detection Network with Tiny Iterative Backbone, in IEEE Access, vol. 8, pp. 130697-130707,DOI: 10.1109/ACCESS.2020.3009518. Code: https://github.com/kyn0v/TIB-Net
[3] Jiaquan Shen, Ningzhong Liu, Han Sun, Huiyu Zhou. Vehicle Detection in Aerial Images Based on Lightweight Deep Convolutional Network and Generative Adversarial Network. in IEEE Access, vol. 7, pp. 148119-148130, 2019. DOI: 10.1109/ACCESS.2019.2947143
[4] Jiaquan Shen, Ningzhong Liu, Han Sun, Xiaoli Tao, Qiangyi Li. Vehicle Detection in Aerial Images Based on Hyper Feature Map in Deep Convolutional Network. TIIS, 2019, 13(4): 1989-2011. DOI: 10.3837/tiis.2019.04.014.
[1] Z. Yin, H. Sun, N. Liu, H. Zhou and J. Shen, FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object Detection, PRCV2023. arXiv: https://arxiv.org/abs/2309.11804 Code: https://github.com/XavierGrool/FGFusion
Lastest Updated on Oct 23, 2023