Biography

I am pursuing my Ph.D. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interest is Video Object Segmentaion (VOS).

Research and Interests

• Video Object Segmentation (VOS)

Publications

• Laijian Li, Yukai Ma, Kai Tang, Xiangrui Zhao, Chao Chen, Jianxin Huang, Jianbiao Mei, and Yong Liu. Geo-localization with Transformer-based 2D-3D match Network. IEEE Robotics and Automation Letters (RA-L), 8:4855-4862, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  This letter presents a novel method for geographical localization by registering satellite maps with LiDAR point clouds. This method includes a Transformer-based 2D-3D matching network called D-GLSNet that directly matches the LiDAR point clouds and satellite images through end-to-end learning. Without the need for feature point detection, D-GLSNet provides accurate pixel-to-point association between the LiDAR point clouds and satellite images. And then, we can easily calculate the horizontal offset (Δx,Δy) and angular deviation Δθyaw between them, thereby achieving accurate registration. To demonstrate our network’s localization potential, we have designed a Geo-localization Node (GLN) that implements geographical localization and is plug-and-play in the SLAM system. Compared to GPS, GLN is less susceptible to external interference, such as building occlusion. In urban scenarios, our proposed D-GLSNet can output high-quality matching, enabling GLN to function stably and deliver more accurate localization results. Extensive experiments on the KITTI dataset show that our D-GLSNet method achieves a mean Relative Translation Error (RTE) of 1.43 m. Furthermore, our method outperforms state-of-the-art LiDAR-based geospatial localization methods when combined with odometry.

  @article{li2023glw,
  title = {Geo-localization with Transformer-based 2D-3D match Network},
  author = {Laijian Li and Yukai Ma and Kai Tang and Xiangrui Zhao and Chao Chen and Jianxin Huang and Jianbiao Mei and Yong Liu},
  year = 2023,
  journal = {IEEE Robotics and Automation Letters (RA-L)},
  volume = 8,
  pages = {4855-4862},
  doi = {10.1109/LRA.2023.3290526},
  abstract = {This letter presents a novel method for geographical localization by registering satellite maps with LiDAR point clouds. This method includes a Transformer-based 2D-3D matching network called D-GLSNet that directly matches the LiDAR point clouds and satellite images through end-to-end learning. Without the need for feature point detection, D-GLSNet provides accurate pixel-to-point association between the LiDAR point clouds and satellite images. And then, we can easily calculate the horizontal offset (Δx,Δy) and angular deviation Δθyaw between them, thereby achieving accurate registration. To demonstrate our network's localization potential, we have designed a Geo-localization Node (GLN) that implements geographical localization and is plug-and-play in the SLAM system. Compared to GPS, GLN is less susceptible to external interference, such as building occlusion. In urban scenarios, our proposed D-GLSNet can output high-quality matching, enabling GLN to function stably and deliver more accurate localization results. Extensive experiments on the KITTI dataset show that our D-GLSNet method achieves a mean Relative Translation Error (RTE) of 1.43 m. Furthermore, our method outperforms state-of-the-art LiDAR-based geospatial localization methods when combined with odometry.}
  }

• Yu Yang, Mengmeng Wang, Jianbiao Mei, and Yong Liu. Exploiting Semantic-level Affinities with a Mask-Guided Network for Temporal Action Proposal in Videos. Applied Intelligence, 2022.
  [BibTeX] [Abstract] [DOI]

  Temporal action proposal (TAP) aims to detect the action instances’ starting and ending times in untrimmed videos, which is fundamental and critical for large-scale video analysis and human action understanding. The main challenge of the temporal action proposal lies in modeling representative temporal relations in long untrimmed videos. Existing state-of-the-art methods achieve temporal modeling by building local-level, proposal-level, or global-level temporal dependencies. Local methods lack a wider receptive field, while proposal and global methods lack the focalization of learning action frames and contain background distractions. In this paper, we propose that learning semantic-level affinities can capture more practical information. Specifically, by modeling semantic associations between frames and action units, action segments (foregrounds) can aggregate supportive cues from other co-occurring actions, and nonaction clips (backgrounds) can learn the discriminations between them and action frames. To this end, we propose a novel framework named the Mask-Guided Network (MGNet) to build semantic-level temporal associations for the TAP task. Specifically, we first propose a Foreground Mask Generation (FMG) module to adaptively generate the foreground mask, representing the locations of the action units throughout the video. Second, we design a Mask-Guided Transformer (MGT) by exploiting the foreground mask to guide the self-attention mechanism to focus on and calculate semantic affinities with the foreground frames. Finally, these two modules are jointly explored in a unified framework. MGNet models the intra-semantic similarities for foregrounds, extracting supportive action cues for boundary refinement; it also builds the inter-semantic distances for backgrounds, providing the semantic gaps to suppress false positives and distractions. Extensive experiments are conducted on two challenging datasets, ActivityNet-1.3 and THUMOS14, and the results demonstrate that our method achieves superior performance.

  @article{yang2022esl,
  title = {Exploiting Semantic-level Affinities with a Mask-Guided Network for Temporal Action Proposal in Videos},
  author = {Yu Yang and Mengmeng Wang and Jianbiao Mei and Yong Liu},
  year = 2022,
  journal = {Applied Intelligence},
  doi = {10.1007/s10489-022-04261-1},
  abstract = {Temporal action proposal (TAP) aims to detect the action instances' starting and ending times in untrimmed videos, which is fundamental and critical for large-scale video analysis and human action understanding. The main challenge of the temporal action proposal lies in modeling representative temporal relations in long untrimmed videos. Existing state-of-the-art methods achieve temporal modeling by building local-level, proposal-level, or global-level temporal dependencies. Local methods lack a wider receptive field, while proposal and global methods lack the focalization of learning action frames and contain background distractions. In this paper, we propose that learning semantic-level affinities can capture more practical information. Specifically, by modeling semantic associations between frames and action units, action segments (foregrounds) can aggregate supportive cues from other co-occurring actions, and nonaction clips (backgrounds) can learn the discriminations between them and action frames. To this end, we propose a novel framework named the Mask-Guided Network (MGNet) to build semantic-level temporal associations for the TAP task. Specifically, we first propose a Foreground Mask Generation (FMG) module to adaptively generate the foreground mask, representing the locations of the action units throughout the video. Second, we design a Mask-Guided Transformer (MGT) by exploiting the foreground mask to guide the self-attention mechanism to focus on and calculate semantic affinities with the foreground frames. Finally, these two modules are jointly explored in a unified framework. MGNet models the intra-semantic similarities for foregrounds, extracting supportive action cues for boundary refinement; it also builds the inter-semantic distances for backgrounds, providing the semantic gaps to suppress false positives and distractions. Extensive experiments are conducted on two challenging datasets, ActivityNet-1.3 and THUMOS14, and the results demonstrate that our method achieves superior performance.}
  }

• Mengmeng Wang, Jianbiao Mei, Lina Liu, and Yong Liu. Delving Deeper Into Mask Utilization in Video Object Segmentation. IEEE Transactions on Image Processing, 31:6255-6266, 2022.
  [BibTeX] [Abstract] [DOI] [PDF]

  This paper focuses on the mask utilization of video object segmentation (VOS). The mask here mains the reference masks in the memory bank, i.e., several chosen high-quality predicted masks, which are usually used with the reference frames together. The reference masks depict the edge and contour features of the target object and indicate the boundary of the target against the background, while the reference frames contain the raw RGB information of the whole image. It is obvious that the reference masks could play a significant role in the VOS, but this is not well explored yet. To tackle this, we propose to investigate the mask advantages of both the encoder and the matcher. For the encoder, we provide a unified codebase to integrate and compare eight different mask-fused encoders. Half of them are inherited or summarized from existing methods, and the other half are devised by ourselves. We find the best configuration from our design and give valuable observations from the comparison. Then, we propose a new mask-enhanced matcher to reduce the background distraction and enhance the locality of the matching process. Combining the mask-fused encoder, mask-enhanced matcher and a standard decoder, we formulate a new architecture named MaskVOS, which sufficiently exploits the mask benefits for VOS. Qualitative and quantitative results demonstrate the effectiveness of our method. We hope our exploration could raise the attention of mask utilization in VOS.

  @article{wang2022ddi,
  title = {Delving Deeper Into Mask Utilization in Video Object Segmentation},
  author = {Mengmeng Wang and Jianbiao Mei and Lina Liu and Yong Liu},
  year = 2022,
  journal = {IEEE Transactions on Image Processing},
  volume = {31},
  pages = {6255-6266},
  doi = {10.1109/TIP.2022.3208409},
  abstract = {This paper focuses on the mask utilization of video object segmentation (VOS). The mask here mains the reference masks in the memory bank, i.e., several chosen high-quality predicted masks, which are usually used with the reference frames together. The reference masks depict the edge and contour features of the target object and indicate the boundary of the target against the background, while the reference frames contain the raw RGB information of the whole image. It is obvious that the reference masks could play a significant role in the VOS, but this is not well explored yet. To tackle this, we propose to investigate the mask advantages of both the encoder and the matcher. For the encoder, we provide a unified codebase to integrate and compare eight different mask-fused encoders. Half of them are inherited or summarized from existing methods, and the other half are devised by ourselves. We find the best configuration from our design and give valuable observations from the comparison. Then, we propose a new mask-enhanced matcher to reduce the background distraction and enhance the locality of the matching process. Combining the mask-fused encoder, mask-enhanced matcher and a standard decoder, we formulate a new architecture named MaskVOS, which sufficiently exploits the mask benefits for VOS. Qualitative and quantitative results demonstrate the effectiveness of our method. We hope our exploration could raise the attention of mask utilization in VOS.}
  }

• Zizhang Li, Mengmeng wang, Huaijin Pi, Kechun Xu, Jianbiao Mei, and Yong Liu. E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context. In European Conference on Computer Vision (ECCV), 2022.
  [BibTeX] [Abstract] [DOI]

  Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit representations. However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance. The key reason of this phenomenon is the coupled formulation of NeRV, which outputs the spatial and temporal information of video frames directly from the frame index input. In this paper, we propose E-NeRV, which dramatically expedites NeRV by decomposing the image-wise implicit neural representation into separate spatial and temporal context. Under the guidance of this new formulation, our model greatly reduces the redundant model parameters, while retaining the representation ability. We experimentally find that our method can improve the performance to a large extent with fewer parameters, resulting in a more than 8× faster speed on convergence. Code is available at https://github.com/kyleleey/E-NeRV.

  @inproceedings{li2022ene,
  title = {E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context},
  author = {Zizhang Li and Mengmeng wang and Huaijin Pi and Kechun Xu and Jianbiao Mei and Yong Liu},
  year = 2022,
  booktitle = {European Conference on Computer Vision (ECCV)},
  doi = {10.1007/978-3-031-19833-5_16},
  abstract = {Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit representations. However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance. The key reason of this phenomenon is the coupled formulation of NeRV, which outputs the spatial and temporal information of video frames directly from the frame index input. In this paper, we propose E-NeRV, which dramatically expedites NeRV by decomposing the image-wise implicit neural representation into separate spatial and temporal context. Under the guidance of this new formulation, our model greatly reduces the redundant model parameters, while retaining the representation ability. We experimentally find that our method can improve the performance to a large extent with fewer parameters, resulting in a more than 8× faster speed on convergence. Code is available at https://github.com/kyleleey/E-NeRV.}
  }