Vehicle Re-Identification
46 papers with code • 12 benchmarks • 10 datasets
Vehicle re-identification is the task of identifying the same vehicle across multiple cameras.
( Image credit: A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras )
Libraries
Use these libraries to find Vehicle Re-Identification models and implementationsDatasets
Most implemented papers
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data
In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention.
VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification
This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain.
TransReID: Transformer-based Object Re-Identification
Extracting robust feature representation is one of the key challenges in object re-identification (ReID).
Simulating Content Consistent Vehicle Datasets with Attribute Descent
Between synthetic and real data, there is a two-level domain gap, i. e., content level and appearance level.
VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification
This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain.
Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID.
FastReID: A Pytorch Toolbox for General Instance Re-identification
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.
Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval
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.
Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough
Limited by the definition of AP, such methods consider both negative and positive instances ranking before each positive instance.
Recall@k Surrogate Loss with Large Batches and Similarity Mixup
This work focuses on learning deep visual representation models for retrieval by exploring the interplay between a new loss function, the batch size, and a new regularization approach.