| Autor: |
Pirazh Khorramshahi, Sai Saketh Rambhatla, Rama Chellappa |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
CVPR Workshops |
| DOI: |
10.1109/cvprw53098.2021.00472 |
| Popis: |
In this work, we consider two tracks of the 2021 NVIDIA AI City Challenge, the City-Scale Multi-Camera Vehicle Re-identification and Natural language-based Vehicle Retrieval. For the vehicle re-identification task, we employ the state-of-art Excited Vehicle Re-Identification deep representation learning model coupled with best training practices and domain adaptation techniques to obtain robust embeddings. We further refine the re-identification results through a series of post-processing steps to remove camera and vehicle orientation bias that is inherent in the task of re-identification. We also take advantage of multiple observations of a vehicle using track-level information and finally obtain fine-grained retrieval results. For the task of Natural language-based vehicle retrieval we leverage the recently proposed Contrastive Language-Image Pre-training model and propose a simple yet effective text-based vehicle retrieval system. We compare our performance against the top submissions to the challenge and our systems are ranked 8th in the public leaderboard for both tracks. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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