Vehicle Re-Identification
53 papers with code • 12 benchmarks • 9 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 )
Benchmarks
These leaderboards are used to track progress in Vehicle Re-Identification
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Libraries
Use these libraries to find Vehicle Re-Identification models and implementationsDatasets
VehicleID
VeRi-776
CityFlow
VeRi-Wild
VehicleX
VRAI
Vehicle-Rear
Vehicle-1M
Car datasets in multiple scenes
Latest papers
Multi-spectral Vehicle Re-identification with Cross-directional Consistency Network and a High-quality Benchmark
superlollipop123/cross-directional-center-network-and-msvr310
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In particular, we design a new cross-directional center loss to pull the modality centers of each identity close to mitigate cross-modality discrepancy, while the sample centers of each identity close to alleviate the sample discrepancy.
UFO: Unified Feature Optimization
UFO aims to benefit each single task with a large-scale pretraining on all tasks.
PP-ShiTu: A Practical Lightweight Image Recognition System
In recent years, image recognition applications have developed rapidly.
Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems
adhirajghosh/rptm_reid
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Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 2023
This leads us to introduce Relation Preserving Triplet Mining (RPTM), a feature-matching guided triplet mining scheme, that ensures that triplets will respect the natural subgroupings within an object ID.
Heterogeneous Relational Complement for Vehicle Re-identification
The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning viewpoint-invariant representations.
Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification
andreyoo/ctam-ctacl-vvreid
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However, this achievement requires large-scale and well-annotated datasets.
Recall@k Surrogate Loss with Large Batches and Similarity Mixup
yash0307/recallatk
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CVPR 2022
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.
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
Unlike most existing methods that learn visual attention based on conventional likelihood, we propose to learn the attention with counterfactual causality, which provides a tool to measure the attention quality and a powerful supervisory signal to guide the learning process.
PhD Learning: Learning With Pompeiu-Hausdorff Distances for Video-Based Vehicle Re-Identification
emdata-ailab/PhD-Learning
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CVPR 2021
Vehicle re-identification (re-ID) is of great significance to urban operation, management, security and has gained more attention in recent years.
Connecting Language and Vision for Natural Language-Based Vehicle Retrieval
ShuaiBai623/AIC2021-T5-CLV
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In this paper, we apply one new modality, i. e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario.
