Vehicle re-identification is the task of identifying the same vehicle across multiple cameras.
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This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain.
Ranked #1 on Vehicle Re-Identification on VeRi-776 (using extra training data)
General Instance Re-identification is a very important task in the computer vision, which can be widely used in many practical applications, such as person/vehicle re-identification, face recognition, wildlife protection, commodity tracing, and snapshop, etc.. To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research.
Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID.
Optimising a ranking-based metric, such as Average Precision (AP), is notoriously challenging due to the fact that it is non-differentiable, and hence cannot be optimised directly using gradient-descent methods.
Ranked #1 on Image Retrieval on iNaturalist
Then we use orientation and camera similarity as penalty to get final similarity.
In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras.
To achieve robustness in Re-Identification, standard methods leverage tracking information in a Video-To-Video fashion.
Ranked #2 on Person Re-Identification on MARS
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).