Part-Guided Attention Learning for Vehicle Re-Identification
@article{Zhang2019PartGuidedAL, title={Part-Guided Attention Learning for Vehicle Re-Identification}, author={Xinyu Zhang and Rufeng Zhang and Jiewei Cao and Dong Gong and Mingyu You and Chunhua Shen}, journal={ArXiv}, year={2019}, volume={abs/1909.06023} }
Vehicle re-identification (Re-ID) often requires one to recognize the fine-grained visual differences between vehicles. [] Key Method PGAN first detects the locations of different part components and salient regions regardless of the vehicle identity, which serve as the bottom-up attention to narrow down the possible searching regions.
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References
SHOWING 1-10 OF 52 REFERENCES
Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle Re-Identification
- Computer ScienceIEEE Transactions on Image Processing
- 2019
A novel Two-level Attention network supervised by a Multi-grain Ranking loss (TAMR) to learn an efficient feature embedding for the vehicle re-ID task and creatively takes the multi-grain relationship between vehicles into consideration.
Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification
- Computer ScienceACCV
- 2018
This work proposes an end-to-end RNN-based Hierarchical Attention (RNN-HA) classification model for vehicle re-identification and demonstrates that the proposed model achieves superior performance over state-of-the-art methods.
Viewpoint-Aware Attentive Multi-view Inference for Vehicle Re-identification
- Computer Science2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- 2018
A Viewpoint-aware Attentive Multi-view Inference (VAMI) model that only requires visual information to solve the multi-view vehicle reID problem and achieves consistent improvements over state-of-the-art vehicle re-ID methods on two public datasets: VeRi and VehicleID.
Part-Regularized Near-Duplicate Vehicle Re-Identification
- Computer Science2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2019
This paper proposes a simple but efficient part-regularized discriminative feature preserving method which enhances the perceptive ability of subtle discrepancies in vehicle re-identification and develops a novel framework to integrate part constrains with the global Re-ID modules by introducing an detection branch.
A Dual-Path Model With Adaptive Attention for Vehicle Re-Identification
- Computer Science2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- 2019
A novel dual-path adaptive attention model for vehicle re-identification (AAVER) that captures macroscopic vehicle features while the orientation conditioned part appearance path learns to capture localized discriminative features by focusing attention on the most informative key-points.
Multi-Task Mutual Learning for Vehicle Re-Identification
- Computer ScienceCVPR Workshops
- 2019
A novel Multi-Task Mutual Learning (MTML) deep model to learn discriminative features simultaneously from multiple branches by fusing features from the final convolutional feature maps from all branches is proposed.
Learning Coarse-to-Fine Structured Feature Embedding for Vehicle Re-Identification
- Computer ScienceAAAI
- 2018
This paper learns a structured feature embedding for vehicle re-ID with a novel coarse-to-fine ranking loss to pull images of the same vehicle as close as possible and achieve discrimination between images from different vehicles as well as vehicles from different vehicle models.
RAM: A Region-Aware Deep Model for Vehicle Re-Identification
- Computer Science2018 IEEE International Conference on Multimedia and Expo (ICME)
- 2018
A novel learning algorithm is introduced to jointly use vehicle IDs, types/models, and colors to train the Region-Aware deep Model (RAM), which fuses more cues for training and results in more discriminative global and regional features.
A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance
- Computer ScienceECCV
- 2016
This paper proposes a novel deep learning-based approach to PROgressive Vehicle re-ID, called “PROVID”, which treats vehicle Re-Id as two specific progressive search processes: coarse-to-fine search in the feature space, and near- to-distantsearch in the real world surveillance environment.
Group-Group Loss-Based Global-Regional Feature Learning for Vehicle Re-Identification
- Computer ScienceIEEE Transactions on Image Processing
- 2020
This work proposes a Group-Group Loss (GGL) to optimize the distance within and across vehicle image groups to accelerate the GRF learning and promote its discrimination power.