PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data (2005.00673v1)

Abstract: In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention. Vehicle ReID is challenging due to 1) high intra-class variability (caused by the dependency of shape and appearance on viewpoint), and 2) small inter-class variability (caused by the similarity in shape and appearance between vehicles produced by different manufacturers). To address these challenges, we propose a Pose-Aware Multi-Task Re-Identification (PAMTRI) framework. This approach includes two innovations compared with previous methods. First, it overcomes viewpoint-dependency by explicitly reasoning about vehicle pose and shape via keypoints, heatmaps and segments from pose estimation. Second, it jointly classifies semantic vehicle attributes (colors and types) while performing ReID, through multi-task learning with the embedded pose representations. Since manually labeling images with detailed pose and attribute information is prohibitive, we create a large-scale highly randomized synthetic dataset with automatically annotated vehicle attributes for training. Extensive experiments validate the effectiveness of each proposed component, showing that PAMTRI achieves significant improvement over state-of-the-art on two mainstream vehicle ReID benchmarks: VeRi and CityFlow-ReID. Code and models are available at https://github.com/NVlabs/PAMTRI.

• The paper proposes PAMTRI, which integrates pose-aware multi-task learning to improve vehicle re-identification.
• It employs highly randomized synthetic data to simulate diverse vehicle poses and strengthen training robustness.
• Experimental results demonstrate significant performance gains by effectively addressing pose variation challenges in re-ID tasks.

A Detailed Examination of ICCV LaTeX Author Guidelines

The manuscript titled "LaTeX Author Guidelines for ICCV Proceedings" serves as a comprehensive guideline for preparing and submitting papers intended for the International Conference on Computer Vision (ICCV). This document outlines crucial instructions regarding the formatting and organization of manuscripts, tailored for adherence to ICCV's submission standards.

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Central to the guidelines are the requirements for anonymization under a blind review process. Misunderstandings around anonymity often lead to rejections; thus, this section delineates specific language practices, promoting the avoidance of first-person references to one’s work while permitting appropriate citations.

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Critically, the paper outlines the final submission necessities, specifically the requirement for an IEEE copyright release form, reinforcing the procedural steps of ensuring intellectual property rights are uniformly recognized before publication ensues.

This document functions not merely as a stylistic manual but as a pivotal tool in aligning authors' methodological standards with the peer-review mechanism's logistical and ethical expectations. For practitioners and contributors in the visual computing domain, adherence to these guidelines undoubtedly facilitates a streamlined submission process, promoting a fair and standardized evaluation of scholarly contributions.

In conclusion, this meticulous structuring of paper submission requirements enhances both the academic documentation's quality and the integrity of the peer review process. Compliance with these parameters ensures the smooth integration of new research findings into the ICCV corpus, fostering collective advancements within the field. Future iterations or conferences might build upon these foundational guidelines, possibly integrating more streamline or automated formats, thereby easing the documentation burden on contributors.