Hierarchical Bi-Directional Feature Perception Network for Person Re-Identification

• URL: http://arxiv.org/abs/2008.03509v1
• Date: Sat, 8 Aug 2020 12:33:32 GMT
• Title: Hierarchical Bi-Directional Feature Perception Network for Person Re-Identification
• Authors: Zhipu Liu, Lei Zhang, Yang Yang
• Abstract summary: Previous Person Re-Identification (Re-ID) models aim to focus on the most discriminative region of an image. We propose a novel model named Hierarchical Bi-directional Feature Perception Network (HBFP-Net) to correlate multi-level information and reinforce each other. Experiments implemented on the mainstream evaluation including Market-1501, CUHK03 and DukeMTMC-ReID datasets show that our method outperforms the recent SOTA Re-ID models.
• Score: 12.259747100939078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous Person Re-Identification (Re-ID) models aim to focus on the most discriminative region of an image, while its performance may be compromised when that region is missing caused by camera viewpoint changes or occlusion. To solve this issue, we propose a novel model named Hierarchical Bi-directional Feature Perception Network (HBFP-Net) to correlate multi-level information and reinforce each other. First, the correlation maps of cross-level feature-pairs are modeled via low-rank bilinear pooling. Then, based on the correlation maps, Bi-directional Feature Perception (BFP) module is employed to enrich the attention regions of high-level feature, and to learn abstract and specific information in low-level feature. And then, we propose a novel end-to-end hierarchical network which integrates multi-level augmented features and inputs the augmented low- and middle-level features to following layers to retrain a new powerful network. What's more, we propose a novel trainable generalized pooling, which can dynamically select any value of all locations in feature maps to be activated. Extensive experiments implemented on the mainstream evaluation datasets including Market-1501, CUHK03 and DukeMTMC-ReID show that our method outperforms the recent SOTA Re-ID models.

Related papers

• PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection [59.355022416218624]
  integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. We propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN) PVAFN uses a multi-pooling strategy to integrate both multi-scale and region-specific information effectively.
  arXiv  Detail & Related papers  (2024-08-26T19:43:01Z)
• Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images [28.560068780733342]
  A novel context aggregation network (CATNet) is proposed to improve the feature extraction process. The proposed model exploits three lightweight plug-and-play modules, namely dense feature pyramid network (DenseFPN), spatial context pyramid ( SCP), and hierarchical region of interest extractor (HRoIE)
  arXiv  Detail & Related papers  (2021-11-22T08:55:25Z)
• LC3Net: Ladder context correlation complementary network for salient object detection [0.32116198597240836]
  We propose a novel ladder context correlation complementary network (LC3Net) FCB is a filterable convolution block to assist the automatic collection of information on the diversity of initial features. DCM is a dense cross module to facilitate the intimate aggregation of different levels of features. BCD is a bidirectional compression decoder to help the progressive shrinkage of multi-scale features.
  arXiv  Detail & Related papers  (2021-10-21T03:12:32Z)
• Feedback Pyramid Attention Networks for Single Image Super-Resolution [37.58180059860872]
  We propose feedback pyramid attention networks (FPAN) to fully exploit the mutual dependencies of features. In our method, the output of each layer in the first stage is also used as the input of the corresponding layer in the next state to re-update the previous low-level filters. We introduce a pyramid non-local structure to model global contextual information in different scales and improve the discriminative representation of the network.
  arXiv  Detail & Related papers  (2021-06-13T11:32:53Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration [65.11022516031463]
  We present a deep interleaved network (DIN) that learns how information at different states should be combined for high-quality (HQ) images reconstruction. In this paper, we propose asymmetric co-attention (AsyCA) which is attached at each interleaved node to model the feature dependencies. Our presented DIN can be trained end-to-end and applied to various image restoration tasks.
  arXiv  Detail & Related papers  (2020-10-29T15:32:00Z)
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification [84.43394420267794]
  We propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. Our framework significantly outperforms state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.
  arXiv  Detail & Related papers  (2020-03-18T12:18:35Z)
• Global Context-Aware Progressive Aggregation Network for Salient Object Detection [117.943116761278]
  We propose a novel network named GCPANet to integrate low-level appearance features, high-level semantic features, and global context features. We show that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-03-02T04:26:10Z)
• Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification [71.96618723152487]
  We introduce Attention Pyramid Convolutional Neural Network (AP-CNN) AP-CNN learns both high-level semantic and low-level detailed feature representation. It can be trained end-to-end, without the need of additional bounding box/part annotations.
  arXiv  Detail & Related papers  (2020-02-09T12:33:23Z)
• Selective Segmentation Networks Using Top-Down Attention [1.0152838128195465]
  Convolutional neural networks model the transformation of the input sensory data at the bottom of a network hierarchy to the semantic information at the top of the visual hierarchy. We propose a unified 2-pass framework for object segmentation that augments Bottom-Up convnets with a Top-Down selection network. We evaluate the proposed network on benchmark datasets for semantic segmentation, and show that networks with the Top-Down selection capability outperform the baseline model.
  arXiv  Detail & Related papers  (2020-02-04T04:47:23Z)

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.