Discriminative Learning of Latent Features for Zero-Shot Recognition

Li, Yan; Zhang, Junge; Zhang, Jianguo; Huang, Kaiqi

doi:10.1109/cvpr.2018.00779

Cited by 167 publications

(138 citation statements)

References 20 publications

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“…Zero-Shot Learning: In ZSL, the model is required to learn from the seen classes and then to be capable of utilizing the learned knowledge to distinguish the unseen classes. It has been studied in image classification [28,4], video recognition [13] and image retrieval/clustering [5]. Interestingly, person ReID matches the setting of ZSL well where training identities have no intersection with testing identities, but most the existing ReID works ignore the problem of ZSL.…”

Section: Related Workmentioning

confidence: 99%

Mixed High-Order Attention Network for Person Re-Identification

Chen¹,

Deng

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

372

188

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Attention has become more attractive in person reidentification (ReID) as it is capable of biasing the allocation of available resources towards the most informative parts of an input signal. However, state-of-the-art works concentrate only on coarse or first-order attention design, e.g. spatial and channels attention, while rarely exploring higher-order attention mechanism. We take a step towards addressing this problem. In this paper, we first propose the High-Order Attention (HOA) module to model and utilize the complex and high-order statistics information in attention mechanism, so as to capture the subtle differences among pedestrians and to produce the discriminative attention proposals. Then, rethinking person ReID as a zero-shot learning problem, we propose the Mixed High-Order Attention Network (MHN) to further enhance the discrimination and richness of attention knowledge in an explicit manner. Extensive experiments have been conducted to validate the superiority of our MHN for person ReID over a wide variety of state-of-the-art methods on three large-scale datasets, including Market-1501, DukeMTMC-ReID and CUHK03-NP. Code is available at

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Section: Related Workmentioning

confidence: 99%

Mixed High-Order Attention Network for Person Re-Identification

Chen¹,

Deng

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

372

188

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“…Focus-area location. Existing studies show that learning from object regions could benefit object recognition at imagelevel [51], [52]. Such focus-area in an image which benefit few-shot learning.…”

Section: ) Convnet-based Feature Extractormentioning

confidence: 99%

Few-Shot Learning for Domain-Specific Fine-Grained Image Classification

Sun

Dong

et al. 2021

IEEE Trans. Ind. Electron.

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Learning to recognize novel visual categories from a few examples is a challenging task for machines in realworld applications. In contrast, humans have the ability to discriminate even similar objects with little supervision. This paper attempts to address the few-shot fine-grained recognition problem. We propose a feature fusion model to explore the largest discriminative features by focusing on key regions. The model utilizes focus-area location to discover the perceptually similar regions among objects. High-order integration is employed to capture the interaction information among intra-parts. We also design a Center Neighbor Loss to form robust embedding space distribution for generating discriminative features. Furthermore, we build a typical fine-grained and few-shot learning dataset miniPPlankton from the real-world application in the area of marine ecological environment. Extensive experiments are carried out to validate the performance of our model. First the model is evaluated with two challenging experiments based on the miniDogsNet and Caltech-UCSD public datasets. The results demonstrate that our model achieves competitive performance compared with state-of-the-art models. Then, we implement our model for the real-world phytoplankton recognition task. The experimental results show the superiority of the proposed model compared with others on the miniPPlankton dataset.

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“…Early work of zero-shot learning makes use of attribute with a two-stage approaches that first train different attribute classifiers and then recognize an image by comparing its predicted attributes with those of unseen classes [11]. For instance, DAP model [12] predicts the posterior of each attribute and then the class posteriors are calculated by maximizing a posterior.…”

Section: Related Workmentioning

confidence: 99%

Discriminative Embedding Autoencoder With a Regressor Feedback for Zero-Shot Learning

Shi

Wei

2020

IEEE Access

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Zero-shot learning (ZSL) aims to recognize the novel object categories using the semantic representation of categories, and the key idea is to explore the knowledge of how the novel class is semantically related to the familiar classes. Some typical models are to learn the proper embedding between the image feature space and the semantic space, whilst it is important to learn discriminative features and comprise the coarse-to-fine image feature and semantic information. In this paper, we propose a discriminative embedding autoencoder with a regressor feedback model for ZSL. The encoder learns a mapping from the image feature space to the discriminative embedding space, which regulates both interclass and intra-class distances between the learned features by a margin, making the learned features be discriminative for object recognition. The regressor feedback learns to map the reconstructed samples back to the the discriminative embedding and the semantic embedding, assisting the decoder to improve the quality of the samples and provide a generalization to the unseen classes. The proposed model is validated extensively on four benchmark datasets: SUN, CUB, AWA1, AWA2, the experiment results show that our proposed model outperforms the stateof-the-art models, and especially in the generalized zero-shot learning (GZSL), significant improvements are achieved.

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Discriminative Learning of Latent Features for Zero-Shot Recognition

Cited by 167 publications

References 20 publications

Mixed High-Order Attention Network for Person Re-Identification

Mixed High-Order Attention Network for Person Re-Identification

Few-Shot Learning for Domain-Specific Fine-Grained Image Classification

Discriminative Embedding Autoencoder With a Regressor Feedback for Zero-Shot Learning

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