|Title:||Orientation-Aware Vehicle Re-Identification with Semantics-Guided Part Attention Network||Authors:||Chen T.-S
|Keywords:||Computer vision; Semantic Web; Semantics; Co-occurrence; Discriminative features; Distance metrics; Feature distance; Re identifications; Semantic labels; Spatial attention; State-of-the-art approach; Vehicles||Issue Date:||2020||Journal Volume:||12347 LNCS||Start page/Pages:||330-346||Source:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||Abstract:||
Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras. It is fundamentally challenging because differences between vehicles are sometimes subtle. While several studies incorporate spatial-attention mechanisms to help vehicle re-ID, they often require expensive keypoint labels or suffer from noisy attention mask if not trained with expensive labels. In this work, we propose a dedicated Semantics-guided Part Attention Network (SPAN) to robustly predict part attention masks for different views of vehicles given only image-level semantic labels during training. With the help of part attention masks, we can extract discriminative features in each part separately. Then we introduce Co-occurrence Part-attentive Distance Metric (CPDM) which places greater emphasis on co-occurrence vehicle parts when evaluating the feature distance of two images. Extensive experiments validate the effectiveness of the proposed method and show that our framework outperforms the state-of-the-art approaches. ? 2020, Springer Nature Switzerland AG.
|Appears in Collections:||電機工程學系|
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