FPB: Feature Pyramid Branch for Person Re-Identification
- URL: http://arxiv.org/abs/2108.01901v1
- Date: Wed, 4 Aug 2021 08:21:52 GMT
- Title: FPB: Feature Pyramid Branch for Person Re-Identification
- Authors: Suofei Zhang, Zirui Yin, Xiofu Wu, Kun Wang, Quan Zhou, Bin Kang
- Abstract summary: We propose a lightweight Feature Pyramid Branch (FPB) to extract features from different layers of networks and aggregate them in a bidirectional pyramid structure.
FPB significantly prompts the performance of backbone network by only introducing less than 1.5M extra parameters.
It is the first successful application of similar structure in person Re-ID tasks, which empirically proves that pyramid network as affiliated branch could be a potential structure in related feature embedding models.
- Score: 12.178324494736922
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: High performance person Re-Identification (Re-ID) requires the model to focus
on both global silhouette and local details of pedestrian. To extract such more
representative features, an effective way is to exploit deep models with
multiple branches. However, most multi-branch based methods implemented by
duplication of part backbone structure normally lead to severe increase of
computational cost. In this paper, we propose a lightweight Feature Pyramid
Branch (FPB) to extract features from different layers of networks and
aggregate them in a bidirectional pyramid structure. Cooperated by attention
modules and our proposed cross orthogonality regularization, FPB significantly
prompts the performance of backbone network by only introducing less than 1.5M
extra parameters. Extensive experimental results on standard benchmark datasets
demonstrate that our proposed FPB based model outperforms state-of-the-art
methods with obvious margin as well as much less model complexity. FPB borrows
the idea of the Feature Pyramid Network (FPN) from prevailing object detection
methods. To our best knowledge, it is the first successful application of
similar structure in person Re-ID tasks, which empirically proves that pyramid
network as affiliated branch could be a potential structure in related feature
embedding models. The source code is publicly available at
https://github.com/anocodetest1/FPB.git.
Related papers
- A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling [54.05517338122698]
We propose an explicitly controllable query-key feature alignment from both semantic-aware and detail-aware perspectives.
We also develop a fine-grained neighbor selection strategy on HR features, which is simple yet effective for alleviating mosaic artifacts.
Our proposed ReSFU framework consistently achieves satisfactory performance on different segmentation applications.
arXiv Detail & Related papers (2024-07-02T14:12:21Z) - Dynamic Perceiver for Efficient Visual Recognition [87.08210214417309]
We propose Dynamic Perceiver (Dyn-Perceiver) to decouple the feature extraction procedure and the early classification task.
A feature branch serves to extract image features, while a classification branch processes a latent code assigned for classification tasks.
Early exits are placed exclusively within the classification branch, thus eliminating the need for linear separability in low-level features.
arXiv Detail & Related papers (2023-06-20T03:00:22Z) - S$^2$-FPN: Scale-ware Strip Attention Guided Feature Pyramid Network for Real-time Semantic Segmentation [6.744210626403423]
This paper presents a new model to achieve a trade-off between accuracy/speed for real-time road scene semantic segmentation.
Specifically, we proposed a lightweight model named Scale-aware Strip Attention Guided Feature Pyramid Network (S$2$-FPN)
Our network consists of three main modules: Attention Pyramid Fusion (APF) module, Scale-aware Strip Attention Module (SSAM), and Global Feature Upsample (GFU) module.
arXiv Detail & Related papers (2022-06-15T05:02:49Z) - RCNet: Reverse Feature Pyramid and Cross-scale Shift Network for Object
Detection [10.847953426161924]
We propose RCNet, which consists of Reverse Feature Pyramid (RevFP) and Cross-scale Shift Network (CSN)
RevFP utilizes local bidirectional feature fusion to simplify the bidirectional pyramid inference pipeline.
CSN directly propagates representations to both adjacent and non-adjacent levels to enable multi-scale features more correlative.
arXiv Detail & Related papers (2021-10-23T04:00:25Z) - HAT: Hierarchical Aggregation Transformers for Person Re-identification [87.02828084991062]
We take advantages of both CNNs and Transformers for image-based person Re-ID with high performance.
Work is the first to take advantages of both CNNs and Transformers for image-based person Re-ID.
arXiv Detail & Related papers (2021-07-13T09:34:54Z) - A^2-FPN: Attention Aggregation based Feature Pyramid Network for
Instance Segmentation [68.10621089649486]
We propose Attention Aggregation based Feature Pyramid Network (A2-FPN) to improve multi-scale feature learning.
A2-FPN achieves an improvement of 2.0% and 1.4% mask AP when integrated into the strong baselines such as Cascade Mask R-CNN and Hybrid Task Cascade.
arXiv Detail & Related papers (2021-05-07T11:51:08Z) - Efficient Human Pose Estimation by Learning Deeply Aggregated
Representations [67.24496300046255]
We propose an efficient human pose estimation network (DANet) by learning deeply aggregated representations.
Our networks could achieve comparable or even better accuracy with much smaller model complexity.
arXiv Detail & Related papers (2020-12-13T10:58:07Z) - Feature Pyramid Grids [140.11116687047058]
We present Feature Pyramid Grids (FPG), a deep multi-pathway feature pyramid.
FPG can improve single-pathway feature pyramid networks by significantly increasing its performance at similar computation cost.
arXiv Detail & Related papers (2020-04-07T17:59:52Z) - Multi-organ Segmentation over Partially Labeled Datasets with
Multi-scale Feature Abstraction [14.92032083210668]
Shortage of fully annotated datasets has been a limiting factor in developing deep learning based image segmentation algorithms.
We propose a unified training strategy that enables a novel multi-scale deep neural network to be trained on multiple partially labeled datasets.
arXiv Detail & Related papers (2020-01-01T13:51:11Z)
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.