Biography

I am pursuing my M.S. degree in College of Control Engineering, Zhejiang University, Hangzhou, China. My major research interests are deep learning and computer vision, which include object detection and segmentation.

Research and Interests

• Deep Learning
• Computer Vision

Publications

• Xiaojun Hou, Jiazheng Xing, Yijie Qian, Yaowei Guo, Shuo Xin, Junhao Chen, Kai Tang, Mengmeng Wang, Zhengkai Jiang, Liang Liu, and Yong Liu. SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 26541-26551, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Early research focused on fully fine-tuning RGB-based trackers, which was inefficient and lacked generalized representation due to the scarcity of multimodal data. Therefore, recent studies have utilized prompt tuning to transfer pre-trained RGB-based trackers to multimodal data. However, the modality gap limits pre-trained knowledge recall, and the dominance of the RGB modality persists, preventing the full utilization of information from other modalities. To address these issues, we propose a novel symmetric multimodal tracking framework called SDSTrack. We introduce lightweight adaptation for efficient fine-tuning, which directly transfers the feature extraction ability from RGB to other domains with a small number of trainable parameters and integrates multimodal features in a balanced, symmetric manner. Furthermore, we design a complementary masked patch distillation strategy to enhance the robustness of trackers in complex environments, such as extreme weather, poor imaging, and sensor failure. Extensive experiments demonstrate that SDSTrack outperforms state-of-the-art methods in various multimodal tracking scenarios, including RGB+Depth, RGB+Thermal, and RGB+Event tracking, and exhibits impressive results in extreme conditions. Our source code is available at: https://github.com/hoqolo/SDSTrack.

  @inproceedings{hou2024sds,
  title = {SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking},
  author = {Xiaojun Hou and Jiazheng Xing and Yijie Qian and Yaowei Guo and Shuo Xin and Junhao Chen and Kai Tang and Mengmeng Wang and Zhengkai Jiang and Liang Liu and Yong Liu},
  year = 2024,
  booktitle = {2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages = {26541-26551},
  doi = {10.1109/CVPR52733.2024.02507},
  abstract = {Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Early research focused on fully fine-tuning RGB-based trackers, which was inefficient and lacked generalized representation due to the scarcity of multimodal data. Therefore, recent studies have utilized prompt tuning to transfer pre-trained RGB-based trackers to multimodal data. However, the modality gap limits pre-trained knowledge recall, and the dominance of the RGB modality persists, preventing the full utilization of information from other modalities. To address these issues, we propose a novel symmetric multimodal tracking framework called SDSTrack. We introduce lightweight adaptation for efficient fine-tuning, which directly transfers the feature extraction ability from RGB to other domains with a small number of trainable parameters and integrates multimodal features in a balanced, symmetric manner. Furthermore, we design a complementary masked patch distillation strategy to enhance the robustness of trackers in complex environments, such as extreme weather, poor imaging, and sensor failure. Extensive experiments demonstrate that SDSTrack outperforms state-of-the-art methods in various multimodal tracking scenarios, including RGB+Depth, RGB+Thermal, and RGB+Event tracking, and exhibits impressive results in extreme conditions. Our source code is available at: https://github.com/hoqolo/SDSTrack.}
  }

• Hanchen Tai, Yijie Qian, Xiao Kang, Liang Liu, and Yong Liu. Fusing LiDAR and Radar with Pillars Attention for 3D Object Detection. In 7th International Symposium on Autonomous Systems (ISAS), 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  In recent years, LiDAR has emerged as one of the primary sensors for mobile robots, enabling accurate detection of 3D objects. On the other hand, 4D millimeter-wave Radar presents several advantages which can be a complementary for LiDAR, including an extended detection range, enhanced sensitivity to moving objects, and the ability to operate seamlessly in various weather conditions, making it a highly promising technology. To leverage the strengths of both sensors, this paper proposes a novel fusion method that combines LiDAR and 4D millimeter-wave Radar for 3D object detection. The proposed approach begins with an efficient multi-modal feature extraction technique utilizing a pillar representation. This method captures comprehensive information from both LiDAR and millimeter-wave Radar data, facilitating a holistic understanding of the environment. Furthermore, a Pillar Attention Fusion (PAF) module is employed to merge the extracted features, enabling seamless integration and fusion of information from both sensors. This fusion process results in lightweight detection headers capable of accurately predicting object boxes. To evaluate the effectiveness of our proposed approach, extensive experiments were conducted on the VoD dataset. The experimental results demonstrate the superiority of our fusion method, showcasing improved performance in terms of detection accuracy and robustness across different environmental conditions. The fusion of LiDAR and 4D millimeter-wave Radar holds significant potential for enhancing the capabilities of mobile robots in real-world scenarios. The proposed method, with its efficient multi-modal feature extraction and attention-based fusion, provides a reliable and effective solution for 3D object detection.

  @inproceedings{tai2024lidar,
  title = {Fusing LiDAR and Radar with Pillars Attention for 3D Object Detection},
  author = {Hanchen Tai and Yijie Qian and Xiao Kang and Liang Liu and Yong Liu},
  year = 2024,
  booktitle = {7th International Symposium on Autonomous Systems (ISAS)},
  doi = {10.1109/ISAS61044.2024.10552581},
  abstract = {In recent years, LiDAR has emerged as one of the primary sensors for mobile robots, enabling accurate detection of 3D objects. On the other hand, 4D millimeter-wave Radar presents several advantages which can be a complementary for LiDAR, including an extended detection range, enhanced sensitivity to moving objects, and the ability to operate seamlessly in various weather conditions, making it a highly promising technology. To leverage the strengths of both sensors, this paper proposes a novel fusion method that combines LiDAR and 4D millimeter-wave Radar for 3D object detection. The proposed approach begins with an efficient multi-modal feature extraction technique utilizing a pillar representation. This method captures comprehensive information from both LiDAR and millimeter-wave Radar data, facilitating a holistic understanding of the environment. Furthermore, a Pillar Attention Fusion (PAF) module is employed to merge the extracted features, enabling seamless integration and fusion of information from both sensors. This fusion process results in lightweight detection headers capable of accurately predicting object boxes. To evaluate the effectiveness of our proposed approach, extensive experiments were conducted on the VoD dataset. The experimental results demonstrate the superiority of our fusion method, showcasing improved performance in terms of detection accuracy and robustness across different environmental conditions. The fusion of LiDAR and 4D millimeter-wave Radar holds significant potential for enhancing the capabilities of mobile robots in real-world scenarios. The proposed method, with its efficient multi-modal feature extraction and attention-based fusion, provides a reliable and effective solution for 3D object detection.}
  }