RAM: A Region-Aware Deep Model for Vehicle Re-Identification
Xiaobin Liu,Shiliang Zhang,Qingming Huang,Wen Gao +3 more
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TLDR
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.Abstract:
Previous works on vehicle Re-ID mainly focus on extracting global features and learning distance metrics. Because some vehicles commonly share same model and maker, it is hard to distinguish them based on their global appearances. Compared with the global appearance, local regions such as decorations and inspection stickers attached to the windshield, may be more distinctive for vehicle Re-ID. To embed the detailed visual cues in those local regions, we propose a Region-Aware deep Model (RAM). Specifically, in addition to extracting global features, RAM also extracts features from a series of local regions. As each local region conveys more distinctive visual cues, RAM encourages the deep model to learn discriminative features. We also introduce a novel learning algorithm to jointly use vehicle IDs, types/models, and colors to train the RAM. This strategy fuses more cues for training and results in more discriminative global and regional features. We evaluate our methods on two large-scale vehicle Re-ID datasets, i.e., VeRi and VehicleID. Experimental results show our methods achieve promising performance in comparison with recent works.read more
Citations
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Proceedings ArticleDOI
A Dual-Path Model With Adaptive Attention for Vehicle Re-Identification
Pirazh Khorramshahi,Amit Kumar,Neehar Peri,Sai Saketh Rambhatla,Jun-Cheng Chen,Rama Chellappa +5 more
TL;DR: In this paper, a dual-path adaptive attention model for vehicle re-identification (AAVER) is proposed, where the global appearance path captures macroscopic vehicle features and the orientation conditioned part appearance path learns to capture localized discriminative features by focusing attention on the most informative key-points.
Book ChapterDOI
Simulating Content Consistent Vehicle Datasets with Attribute Descent.
TL;DR: This paper introduces a large-scale synthetic dataset VehicleX, which contains 1,362 vehicles of various 3D models with fully editable attributes, and proposes an attribute descent approach to let VehicleX approximate the attributes in real-world datasets.
Book ChapterDOI
The Devil Is in the Details: Self-supervised Attention for Vehicle Re-identification
TL;DR: Self-supervised Attention for Vehicle Re-identification (SAVER) is presented, a novel approach to effectively learn vehicle-specific discriminative features and improves upon the state-of-the-art on challenging VeRi, VehicleID, Vehicle-1M and VERI-Wild datasets.
Proceedings ArticleDOI
Parsing-Based View-Aware Embedding Network for Vehicle Re-Identification
Dechao Meng,Liang Li,Xuejing Liu,Yadong Li,Shijie Yang,Zheng-Jun Zha,Xingyu Gao,Shuhui Wang,Qingming Huang +8 more
TL;DR: Zhang et al. as discussed by the authors proposed a parsing-based view-aware embedding network (PVEN) to achieve the viewaware feature alignment and enhancement for vehicle ReID.
Proceedings ArticleDOI
Vehicle Re-Identification With Viewpoint-Aware Metric Learning
TL;DR: In this article, a novel viewpoint-aware metric learning approach is proposed, which learns two metrics for similar viewpoints and different viewpoints in two feature spaces, respectively, giving rise to viewpointaware network (VANet).
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