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Found 4 papers, 3 papers with code
Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection
The success of knowledge distillation mainly relies on how to keep the feature discrepancy between the teacher and student model, in which it assumes that: (1) the teacher model can jointly represent two different distributions for the normal and abnormal patterns, while (2) the student model can only reconstruct the normal distribution.
Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking
In this paper, we propose a simple yet effective two-stage feature learning paradigm to jointly learn single-shot and multi-shot features for different targets, so as to achieve robust data association in the tracking process.
Exploring the Benefits of Visual Prompting in Differential Privacy
Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model.
Efficient Urban-scale Point Clouds Segmentation with BEV Projection
To demonstrate the benefits of our method, we conduct various experiments on the SensatUrban dataset, in which our model presents competitive evaluation results (61. 17% mIoU and 91. 37% OverallAccuracy).
Ranked #2 on
3D Semantic Segmentation
on SensatUrban
