Journal ArticleDOI
Learning discriminative region representation for person retrieval
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TLDR
Zhang et al. as discussed by the authors proposed an identity-guided human region segmentation (HRS) method for person retrieval, which learns a set of distinct region bases that are consistent across a given dataset, and predicts informative region segments by grouping intermediate feature vectors based on their similarity to these bases.About:
This article is published in Pattern Recognition.The article was published on 2022-01-01. It has received 10 citations till now. The article focuses on the topics: Discriminative model & Computer science.read more
Citations
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Journal ArticleDOI
SPARE: Self-supervised part erasing for ultra-fine-grained visual categorization
TL;DR: SPARE as discussed by the authors proposes a self-supervised part erasing module to predict the contextual position of the erased parts, which enables the network to exploit the intrinsic structure of data, i.e. understanding and recognizing the contextual information of the objects.
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EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation
TL;DR: Zhang et al. as discussed by the authors proposed a novel error attention refining network (ERA-Net) that is capable of learning and predicting the potential false predictions in a two-stage manner for effective retinal vessel segmentation.
Journal ArticleDOI
Mix-ViT: Mixing attentive vision transformer for ultra-fine-grained visual categorization
TL;DR: Wang et al. as mentioned in this paper proposed Mix-ViT, a self-supervised vision transformer for ultra-fine-grained visual categorization (ultra-FGVC) tasks.
Journal ArticleDOI
A Lie algebra representation for efficient 2D shape classification
TL;DR: Wang et al. as mentioned in this paper proposed a novel shape representation based on the Block Diagonal Symmetric Positive Definite Matrix Lie Algebra (BDSPDMLA) for 2D curved shapes, which extends the existing Lie group representations to a compact yet informative Lie algebra representation.
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A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization.
TL;DR: Wang et al. as mentioned in this paper proposed a novel compositional feature embedding and similarity metric (CECS), which randomly select patches in the original input image, and these patches are then replaced by patches from the images of different categories or masked out.
References
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
TL;DR: This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities.
Proceedings ArticleDOI
Scalable Person Re-identification: A Benchmark
TL;DR: A minor contribution, inspired by recent advances in large-scale image search, an unsupervised Bag-of-Words descriptor is proposed that yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large- scale 500k dataset.
Proceedings ArticleDOI
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
TL;DR: A novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter is proposed and significantly outperforms state-of-the-art methods on this dataset.
Journal ArticleDOI
Random Erasing Data Augmentation
TL;DR: Random Erasing as mentioned in this paper randomly selects a rectangle region in an image and erases its pixels with random values, which reduces the risk of overfitting and makes the model robust to occlusion.
Book ChapterDOI
Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
TL;DR: In this paper, a part-based convolutional baseline (PCB) is proposed to learn discriminative part-informed features for person retrieval and two contributions are made: (i) a network named Part-based Convolutional Baseline (PCBB) which outputs a convolutionAL descriptor consisting of several part-level features.