Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization

Ji, Ruyi; Wen, Longyin; Zhang, Libo; Du, Dawei; Wu, Yicheng; Zhao, Chen; Liu, Xianglong; Huang, Feiyue

doi:10.1109/cvpr42600.2020.01048

Cited by 185 publications

(76 citation statements)

References 19 publications

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“…Zhuang et al [22] have also proposed an attentive pairwise interaction framework, inspired by the human mechanism, to identify contrastive clues by comparing two images. Recently, Ji et al [23] have proposed an attention convolutional binary neural tree that characterizes a coarse-to-fine hierarchical model.…”

Section: Related Work a Fine-grained Image Recognitionmentioning

confidence: 99%

Localization-Aware Adaptive Pairwise Margin Loss for Fine-Grained Image Recognition

Kim

Byun

2021

IEEE Access

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Section: Related Work a Fine-grained Image Recognitionmentioning

confidence: 99%

Localization-Aware Adaptive Pairwise Margin Loss for Fine-Grained Image Recognition

Kim

Byun

2021

IEEE Access

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“…The task of Fine-Grained Visual Categorization (FGVC) is one of the most active research topics very recently [1,2,3]. The goal of FGVC is to distinguish the objects shared similar patterns in a specific scenario from different subordinate categories.…”

Section: Introductionmentioning

confidence: 99%

“…However, we argue two problems: (1) There is a lack of works devoting to FGVC on practical electronic commerce (e-commerce) as far as we known, making it an important yet not well studied issue in this domain. (2) The aforementioned approaches simply ignore the internal semantic region(or part) correlation, which reveals the salient information about the images.…”

Section: Introductionmentioning

confidence: 99%

Object Decoupling with Graph Correlation for Fine-Grained Image Classification

Guo

Zhuge

Gao

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Fine-grained image classification has drawn increasing attention as it is much closer to practical applications than generic image classification. The majority of current fine-grained approaches locate the discriminative regions and leverage the features of these regions for classification as their magic weapons. However, these approaches simply ignore the internal semantic region correlation. As is well known, the correlation reveals the salient information of images, which can further boost the performance of fine-grained image classification. To this end, we propose an Object Decoupling with Graph Correlation network (ODGC) to explore the informative potentials of region correlation. A Responsive Object Location Module (ROLM) is first introduced to obtain the finegrained object within a bounding box automatically. A Semantic Decoupling Module (SDM) then segments the object into different parts. ODGC learns the representations of these parts by transferring these part features into a Graph Correlation Module (GCM). Consists of these three main modules, ODGC is trained for fine-grained image classification in an end-to-end way. Extensive experiments conducted on CUB-200-2011 demonstrate that the aforementioned modules significantly improve the ODGC, and it achieves a new stateof-the-art performance to 88.2% top-1 accuracy. Besides, we collect a practical business e-commercial dataset, named Ecom-15K. The evaluation on it further validates the applicability of our method in practical scenarios.

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“…The feature extraction network is an essential stage in image processing. Existing FGVC approaches (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27) first localize the vehicle parts in the picture and then extract the discriminative features for classification. Most of the previous localization methods (2,15,16) are supervised algorithms and require a lot of auxiliary data.…”

mentioning

confidence: 99%

“…Meanwhile, images labeled with the appropriate vehicle manufacturer and model require professional knowledge in the auto industry. Also, a weakly supervised method (3,11,12,14) to generate discriminative regions not only maximizes the utilization of the semantic information in these regions but also avoids the problem of excessive reliance on labels. However, these methods usually have complex network structures and are not conducive to optimization.…”

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confidence: 99%

Hierarchical Scheme for Vehicle Make and Model Recognition

Wang

Cheng

Wang

et al. 2021

Transportation Research Record: Journal of the Transportation R

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A vehicle make and model recognition (VMMR) system is a common requirement in the field of intelligent transportation systems (ITS). However, it is a challenging task because of the subtle differences between vehicle categories. In this paper, we propose a hierarchical scheme for VMMR. Specifically, the scheme consists of (1) a feature extraction framework called weighted mask hierarchical bilinear pooling (WMHBP) based on hierarchical bilinear pooling (HBP) which weakens the influence of invalid background regions by generating a weighted mask while extracting features from discriminative regions to form a more robust feature descriptor; (2) a hierarchical loss function that can learn the appearance differences between vehicle brands, and enhance vehicle recognition accuracy; (3) collection of vehicle images from the Internet and classification of images with hierarchical labels to augment data for solving the problem of insufficient data and low picture resolution and improving the model’s generalization ability and robustness. We evaluate the proposed framework for accuracy and real-time performance and the experiment results indicate a recognition accuracy of 95.1% and an FPS (frames per second) of 107 for the framework for the Stanford Cars public dataset, which demonstrates the superiority of the method and its availability for ITS.

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Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization

Cited by 185 publications

References 19 publications

Localization-Aware Adaptive Pairwise Margin Loss for Fine-Grained Image Recognition

Localization-Aware Adaptive Pairwise Margin Loss for Fine-Grained Image Recognition

Object Decoupling with Graph Correlation for Fine-Grained Image Classification

Hierarchical Scheme for Vehicle Make and Model Recognition

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