Taking A Closer Look at Synthesis: Fine-grained Attribute Analysis for Person Re-Identification

• URL: http://arxiv.org/abs/2010.08145v3
• Date: Tue, 6 Apr 2021 03:44:34 GMT
• Title: Taking A Closer Look at Synthesis: Fine-grained Attribute Analysis for Person Re-Identification
• Authors: Suncheng Xiang, Yuzhuo Fu, Guanjie You, Ting Liu
• Abstract summary: Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from the popularity of synthetic data engine, has achieved remarkable performance. This research helps us have a deeper understanding of the fundamental problems in person re-ID, which also provides useful insights for dataset building and future practical usage.
• Score: 15.388939933009668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from the popularity of synthetic data engine, has achieved remarkable performance. However, in pursuit of high accuracy, researchers in the academic always focus on training with large-scale datasets at a high cost of time and label expenses, while neglect to explore the potential of performing efficient training from millions of synthetic data. To facilitate development in this field, we reviewed the previously developed synthetic dataset GPR and built an improved one (GPR+) with larger number of identities and distinguished attributes. Based on it, we quantitatively analyze the influence of dataset attribute on re-ID system. To our best knowledge, we are among the first attempts to explicitly dissect person re-ID from the aspect of attribute on synthetic dataset. This research helps us have a deeper understanding of the fundamental problems in person re-ID, which also provides useful insights for dataset building and future practical usage.

Related papers

• Best Practices and Lessons Learned on Synthetic Data [83.63271573197026]
  The success of AI models relies on the availability of large, diverse, and high-quality datasets. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
  arXiv  Detail & Related papers  (2024-04-11T06:34:17Z)
• Synthetic data generation for a longitudinal cohort study -- Evaluation, method extension and reproduction of published data analysis results [0.32593385688760446]
  In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data. In this study, we use a state-of-the-art synthetic data generation method.
  arXiv  Detail & Related papers  (2023-05-12T13:13:55Z)
• Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data [91.52783572568214]
  Synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs. We discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data.
  arXiv  Detail & Related papers  (2023-04-07T16:38:40Z)
• Enabling Synthetic Data adoption in regulated domains [1.9512796489908306]
  The switch from a Model-Centric to a Data-Centric mindset is putting emphasis on data and its quality rather than algorithms. In particular, the sensitive nature of the information in highly regulated scenarios needs to be accounted for. A clever way to bypass such a conundrum relies on Synthetic Data: data obtained from a generative process, learning the real data properties.
  arXiv  Detail & Related papers  (2022-04-13T10:53:54Z)
• Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification [16.107661617441327]
  Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data has attracted attention from both academia and the public eye. We construct and label a large-scale synthetic person dataset named FineGPR with fine-grained attribute distribution.
  arXiv  Detail & Related papers  (2021-09-22T03:12:32Z)
• Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification [101.1886788396803]
  Person re-identification (re-ID) has gained more and more attention due to its widespread applications in video surveillance. Unfortunately, the mainstream deep learning methods still need a large quantity of labeled data to train models. In this paper, we develop a data collector to automatically generate synthetic re-ID samples in a computer game, and construct a data labeler to simultaneously annotate them.
  arXiv  Detail & Related papers  (2021-09-12T15:51:41Z)
• Unsupervised Pre-training for Person Re-identification [90.98552221699508]
  We present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" We make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation.
  arXiv  Detail & Related papers  (2020-12-07T14:48:26Z)
• Attribute analysis with synthetic dataset for person re-identification [15.388939933009668]
  Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from the popularity of synthetic data engine, have achieved remarkable performance. Existing synthetic datasets are in small size and lack of diversity, which hinders the development of person re-ID in real-world scenarios.
  arXiv  Detail & Related papers  (2020-06-12T12:51:47Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)
• Deep Learning for Person Re-identification: A Survey and Outlook [233.36948173686602]
  Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings.
  arXiv  Detail & Related papers  (2020-01-13T12:49:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.