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Found 4 papers, 2 papers with code
SeMoLi: What Moves Together Belongs Together
Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to pseudo-label instances of moving objects and use these as supervisory signals to train 3D object detectors in Lidar data without manual supervision.
Simple Cues Lead to a Strong Multi-Object Tracker
For association, most models resourced to motion and appearance cues, e. g., re-identification networks.
The Group Loss++: A deeper look into group loss for deep metric learning
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.
Learning Intra-Batch Connections for Deep Metric Learning
To this end, we propose an approach based on message passing networks that takes all the relations in a mini-batch into account.
Ranked #3 on
Metric Learning
on CARS196
