Research Projects

Advisor: Prof. Xiaojun Qi

• Proposed Sparse Activation Mapping (SAM) to provide visual explanation for 3D object detection networks, without a need for architecture modification or re-training

• Introduced a customized average pooling of convolutional layer weights to maintain the sparsity pattern of point clouds.

• Applied SAMs to compare the effectiveness and trustworthy of different volumetric-based 3D object detection models. To the best of our knowledge, this is the first systematic interpretations framework of these methods.

Advisor: Prof. Xiaojun Qi

• Proposed an end-to-end network with attention on facial keypoints for face expression classification in the wild, then applied geometric constraints on facial keypoints detections to filter out occluded areas.

• Outperformed the state-of-the-art face expression classification method on AffectNet dataset, especially robust in challenging occlusions.

Advisor: Prof. Xiaojun Qi, Prof. Ziqi Song

• Proposed a robust network for 3D object detection in point cloud, which exploits a novel self-attention algorithm to model the relationship between keypoints from voxel set abstraction.

• Utilized a novel computational efficient decoder to fuse the feature maps from multiple receptive fields, which outperforms our baseline model in KITTI dataset.