Multi-Attribute Enhancement Network for Person Search

• URL: http://arxiv.org/abs/2102.07968v1
• Date: Tue, 16 Feb 2021 05:43:47 GMT
• Title: Multi-Attribute Enhancement Network for Person Search
• Authors: Lequan Chen, Wei Xie, Zhigang Tu, Yaping Tao, Xinming Wang
• Abstract summary: Person Search is designed to jointly solve the problems of Person Detection and Person Re-identification (Re-ID) Visual character attributes play a key role in retrieving the query person, which has been explored in Re-ID but has been ignored in Person Search. We introduce attribute learning into the model, allowing the use of attribute features for retrieval task.
• Score: 7.85420914437147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person Search is designed to jointly solve the problems of Person Detection and Person Re-identification (Re-ID), in which the target person will be located in a large number of uncut images. Over the past few years, Person Search based on deep learning has made great progress. Visual character attributes play a key role in retrieving the query person, which has been explored in Re-ID but has been ignored in Person Search. So, we introduce attribute learning into the model, allowing the use of attribute features for retrieval task. Specifically, we propose a simple and effective model called Multi-Attribute Enhancement (MAE) which introduces attribute tags to learn local features. In addition to learning the global representation of pedestrians, it also learns the local representation, and combines the two aspects to learn robust features to promote the search performance. Additionally, we verify the effectiveness of our module on the existing benchmark dataset, CUHK-SYSU and PRW. Ultimately, our model achieves state-of-the-art among end-to-end methods, especially reaching 91.8% of mAP and 93.0% of rank-1 on CUHK-SYSU. Codes and models are available at https://github.com/chenlq123/MAE.

Related papers

• CLEAR: Cross-Transformers with Pre-trained Language Model is All you need for Person Attribute Recognition and Retrieval [0.18749305679160366]
  Person attribute recognition and attribute-based retrieval are two core human-centric tasks. We introduce a robust cross-transformers network to handle person attribute recognition. We also introduce an effective training strategy to train only a few additional parameters for adapters. CLEAR achieves state-of-the-art performance or competitive results for both tasks.
  arXiv  Detail & Related papers  (2024-03-10T07:31:06Z)
• Multi-Prompts Learning with Cross-Modal Alignment for Attribute-based Person Re-Identification [18.01407937934588]
  We present a new framework called Multi-Prompts ReID (MP-ReID) based on prompt learning and language models. MP-ReID learns to hallucinate diverse, informative, and promptable sentences for describing the query images. Explicit prompts are obtained by ensembling generation models, such as ChatGPT and VQA models.
  arXiv  Detail & Related papers  (2023-12-28T03:00:19Z)
• Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification [78.52704557647438]
  We propose a novel FIne-grained Representation and Recomposition (FIRe$2$) framework to tackle both limitations without any auxiliary annotation or data. Experiments demonstrate that FIRe$2$ can achieve state-of-the-art performance on five widely-used cloth-changing person Re-ID benchmarks.
  arXiv  Detail & Related papers  (2023-08-21T12:59:48Z)
• Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion Retrieval [27.751399400911932]
  We introduce an attribute-guided multi-level attention network (AG-MAN) for fine-grained fashion retrieval. Specifically, we first enhance the pre-trained feature extractor to capture multi-level image embedding. Then, we propose a classification scheme where images with the same attribute, albeit with different values, are categorized into the same class.
  arXiv  Detail & Related papers  (2022-12-27T05:28:38Z)
• Body Part-Based Representation Learning for Occluded Person Re-Identification [102.27216744301356]
  Occluded person re-identification (ReID) is a person retrieval task which aims at matching occluded person images with holistic ones. Part-based methods have been shown beneficial as they offer fine-grained information and are well suited to represent partially visible human bodies. We propose BPBreID, a body part-based ReID model for solving the above issues.
  arXiv  Detail & Related papers  (2022-11-07T16:48:41Z)
• Attribute Prototype Network for Any-Shot Learning [113.50220968583353]
  We argue that an image representation with integrated attribute localization ability would be beneficial for any-shot, i.e. zero-shot and few-shot, image classification tasks. We propose a novel representation learning framework that jointly learns global and local features using only class-level attributes.
  arXiv  Detail & Related papers  (2022-04-04T02:25:40Z)
• Weakly Supervised Person Search with Region Siamese Networks [65.76237418040071]
  Supervised learning is dominant in person search, but it requires elaborate labeling of bounding boxes and identities. We present a weakly supervised setting where only bounding box annotations are available. Our model achieves the rank-1 of 87.1% and mAP of 86.0% on CUHK-SYSU benchmark.
  arXiv  Detail & Related papers  (2021-09-13T16:33:27Z)
• Video Person Re-identification using Attribute-enhanced Features [49.68392018281875]
  We propose a novel network architecture named Attribute Salience Assisted Network (ASA-Net) for attribute-assisted video person Re-ID. To learn a better separation of the target from background, we propose to learn the visual attention from middle-level attribute instead of high-level identities.
  arXiv  Detail & Related papers  (2021-08-16T07:41:27Z)
• Integrating Coarse Granularity Part-level Features with Supervised Global-level Features for Person Re-identification [3.4758712821739426]
  Part-level person Re-ID network (CGPN) integrates supervised global features for both holistic and partial person images. CGPN learns to extract effective body part features for both holistic and partial person images. Single model trained on three Re-ID datasets including Market-1501, DukeMTMC-reID and CUHK03 state-of-the-art performances.
  arXiv  Detail & Related papers  (2020-10-15T11:49:20Z)
• Attribute Prototype Network for Zero-Shot Learning [113.50220968583353]
  We propose a novel zero-shot representation learning framework that jointly learns discriminative global and local features. Our model points to the visual evidence of the attributes in an image, confirming the improved attribute localization ability of our image representation.
  arXiv  Detail & Related papers  (2020-08-19T06:46:35Z)
• Pedestrian Attribute Recognition: A Survey [26.939872088531036]
  We introduce the background of pedestrian attribute recognition (PAR) We introduce existing benchmarks, including popular datasets and evaluation criteria. We show some applications that take pedestrian attributes into consideration and achieve better performance.
  arXiv  Detail & Related papers  (2019-01-22T17:16:49Z)

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.