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Found 4 papers, 1 papers with code
Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking
HD-AMOT defines the diversified informative representation by encoding the geometric and semantic information, and formulates the frame inference strategy as a Markov decision process to learn an optimal sampling policy based on the designed informative representation.
Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification
Person re-identification (Re-ID) models usually show a limited performance when they are trained on one dataset and tested on another dataset due to the inter-dataset bias (e. g. completely different identities and backgrounds) and the intra-dataset difference (e. g. camera invariance).
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking
The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online.
Ranked #3 on
Visual Object Tracking
on OTB-2013
Robust Object Tracking Based on Temporal and Spatial Deep Networks
Our deep architecture contains three networks, a Feature Net, a Temporal Net, and a Spatial Net.
