The Devil Is in the Details: Self-supervised Attention for Vehicle Re-identification

Khorramshahi, Pirazh; Peri, Neehar; Chen, Jun-Cheng; Chellappa, Rama

doi:10.1007/978-3-030-58568-6_22

Cited by 95 publications

(46 citation statements)

References 39 publications

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“…Moreover, the method including both two attribute models achieves higher Top-1 accuracy. [11] 0.611 0.562 0.514 AAVER [21] 0.747 0.686 0.635 RAM [20] 0.752 0.723 0.677 Part-regularized [18] 0.784 0.750 0.742 SAN [23] 0.797 0.784 0.756 SAVER [22] 0.799 0.776 0.753 HCANet 0.837 0.811 0.780…”

Section: Ablation Studymentioning

confidence: 99%

“…AAVER [21] is a dual-path model which combines macroscopic global and local features. SAVER [22] learns vehicle-specific discriminative features based on self-supervised attention without additional annotations. According to the comparison results in Tab.2, our method achieves the best Top-1 accuracy among other methods on VehicleID dataset.…”

Section: Benchmark Comparisonmentioning

confidence: 99%

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High Confidence Attribute Recognition For Vehicle Re-Identification

Dou

Yang

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Vehicle re-identification aims to associate images or videos of the same vehicle collected from different cameras. Many existing methods address the vehicle re-identification problem by explicitly learning distinguishable global features. However, vehicle attributes, i.e., logo category and orientation, play an indispensable role in identifying vehicles. In this paper, we first propose deep models to recognize vehicle attributes. Then, based on these attributes, we adopt a High Confidence Attribute Network (HCANet) to extract weighted global features. A comprehensive evaluation on the Vehi-cleID dataset shows that our approach achieves competitive results.

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Section: Ablation Studymentioning

confidence: 99%

Section: Benchmark Comparisonmentioning

confidence: 99%

High Confidence Attribute Recognition For Vehicle Re-Identification

Dou

Yang

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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show abstract

“…However, the artificial features depend on human experience to a large extent, and the deep information of image is not easy to be mined, so the effectiveness of artificial features is hard to be ensured. Therefore, the deep learning based vehicle recognition algorithms are paid more attention in recent years, which include some traditional deep learning models such as Convolutional Neural Network model [13][14][15], Deep Belief Network model [16][17], Transfer learning model [18][19][20], Restricted Boltzmann Machine [21][22][23], and some improved models such as Conv5 [24], Teacher-Student Network [25], Parsing-based View-aware Embedding Network [26], Semantics-guided Part Attention Network [27], the model fused by multiple networks [28], and the network based on reconstruction [29], et al For the supervised vehicle classification problem, these deep learning methods have achieved good results, but for vehicle face matching problem under the conditions that the times of each vehicle being captured is very limited and the number of the training samples is too small, the universalities of these models are not very well. Therefore, under a limited number of vehicle face samples, it is very meaningful to propose a vehicle re-identification algorithm with good robustness and universality.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Face Re-identification Based on Nonnegative Matrix Factorization with Time Difference Constraint

2021

KSII TIIS

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Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.

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“…-based approaches: PGAN [51], PRN [11], PVEN [29], and GLAMOR [39]; (2) attribute-based approaches: AGNet-ASL [42], DJDL [24], XG-6-sub-multi [53], and SAN [32]; (3) attention-based approaches: AAVER [18] and SEVER [19]; (4) other interesting approaches: GSTE [1], VAMI [56], and DCDLearn [59].…”

Section: Implementation Detailsmentioning

confidence: 99%

Pseudo Graph Convolutional Network for Vehicle ReID

Qian

Peng

et al. 2021

Proceedings of the 29th ACM International Conference on Multimedia

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Image-based Vehicle ReID methods have suffered from limited information caused by viewpoints, illumination, and occlusion as they usually use a single image as input. Graph convolutional methods (GCN) can alleviate the aforementioned problem by aggregating neighbor samples' information to enhance the feature representation. However, it's uneconomical and computational for the inference processes of GCN-based methods since they need to iterate over all samples for searching the neighbor nodes. In this paper, we propose the first Pseudo-GCN Vehicle ReID method (PGVR) which enables a CNN-based module to performs competitively to GCN-based methods and has a faster and lightweight inference process. To enable the Pseudo-GCN mechanism, a two-branch network and a graph-based knowledge distillation are proposed. The two-branch network consists of a CNN-based student branch and a GCN-based teacher branch. The GCN-based teacher branch adopts a ReID-based GCN to learn the topological optimization ability under the supervision of ReID tasks during training time. Moreover, the graph-based knowledge distillation explicitly transfers the topological optimization ability from the teacher branch to the student branch which acknowledges all nodes. We evaluate our proposed method PGVR on three mainstream Vehicle ReID benchmarks and demonstrate that PGVR achieves state-of-the-art performance. CCS CONCEPTS• Computing methodologies → Computer vision; Neural networks; • Information systems → Retrieval models and ranking.

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The Devil Is in the Details: Self-supervised Attention for Vehicle Re-identification

Cited by 95 publications

References 39 publications

High Confidence Attribute Recognition For Vehicle Re-Identification

High Confidence Attribute Recognition For Vehicle Re-Identification

Vehicle Face Re-identification Based on Nonnegative Matrix Factorization with Time Difference Constraint

Pseudo Graph Convolutional Network for Vehicle ReID

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