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Found 4 papers, 1 papers with code
Weakly Supervised Multi-Object Tracking and Segmentation
We introduce the problem of weakly supervised Multi-Object Tracking and Segmentation, i. e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
Learning Multi-Object Tracking and Segmentation from Automatic Annotations
Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1. 9%/+7. 5% on cars/pedestrians), and MOTSNet improves by +4. 1% over previously best methods on the MOTSChallenge dataset.
Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation
Additionally, we propose and analyse network distillation as a learning strategy to reduce the computational cost of the deep learning approach at test time.
Metric Learning for Novelty and Anomaly Detection
When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently.
