A New Benchmark for Instance-Level Image Classification

To efficiently capture feature information in tasks of fine-grained image classification, this study introduces a new network model for fine-grained image classification, which utilizes a hybrid attention approach. The model is built upon a hybrid attention module (MA), and with the assistance of the attention erasure module (EA), it can adaptively enhance the prominent areas in the image and capture more detailed image information. Specifically, for tasks involving fine-grained image classification, this study designs an attention module capable of applying the attention mechanism to both the channel and spatial dimensions. This highlights the important regions and key feature channels in the image, allowing for the extraction of distinct local features. Furthermore, this study presents an attention erasure module (EA) that can remove significant areas in the image based on the features identified; thus, shifting focus to additional feature details within the image and improving the diversity and completeness of the features. Moreover, this study enhances the pooling layer of ResNet50 to augment the perceptual region and the capability to extract features from the network's less deep layers. For the objective of fine-grained image classification, this study extracts a variety of features and merges them effectively to create the final feature representation. To assess the effectiveness of the proposed model, experiments were conducted on three publicly available fine-grained image classification datasets: Stanford Cars, FGVC-Aircraft, and CUB-200-2011. The method achieved classification accuracies of 92.8, 94.0, and 88.2% on these datasets, respectively. In comparison with existing approaches, the efficiency of this method has significantly improved, demonstrating higher accuracy and robustness.

Fine-grained image classification, which is a challenging task in computer vision, requires precise differentiation among visually similar object categories. In this paper, we design a novel framework, namely Relationship Batch Integration (RBI), allowing the discernment of vital visual features that may remain elusive when examining a singular image representative of a particular class. Our proposed method, validated through extensive experiments, significantly boosts the accuracy of fine-grained classifiers, achieving state-of-the-art performance with ( 97 . 79 % ) on the Stanford Dog dataset, even attaining a top result of ( 93 . 71 % ) on the Tiny-Imagenet dataset for general image classification.

This paper studies the real-time water quality prediction model based on fine-grained image classification and home aerobic exercise rehabilitation for the elderly. Fine-grained image classification refers to the classification of sub-categories under image classification. In the past, image classification mainly classified images into large categories. In contrast, the most important feature of fine-grained image classification is the large differences within the class and the small differences between the classes, which are very difficult. And because it has a wide range of application requirements in human production and life, it has attracted a large number of scholars to conduct in-depth research on it. Based on the above research, the performance of the proposed model is analyzed and verified for specific water quality parameter prediction scenarios. The water quality model is an important part of the study of water environment simulation and water environment planning and management. Starting from the research review of water quality models, this paper introduces the development history of water quality models and several major non-mechanical water quality models and application research progress. It recognizes the objective facts of uncertainty in the water environment system and the limitations of organic water quality model in the application of water environment system in China. Finally, based on the above conclusions, elastic band resistance training combined with cardiac rehabilitation education improved the physical function of patients with coronary heart disease in the community, especially muscle health and cardiopulmonary rehabilitation training, as well as the quality of life of patients. The Heart Rehabilitation Training Program at Home can improve the awareness of cardiac rehabilitation of patients with coronary heart disease in the region, strengthen exercise compliance, popularize and apply to the home care of patients with coronary heart disease in the region, and also provide theoretical reference materials for regional medical personnel helping patients at home with training.

Thus far, few studies have been conducted on fine-grained classification tasks for the latest convolutional neural network ConvNeXt, and no effective optimization method has been made available. To achieve more accurate fine-grained classification, this paper proposes two attention embedding methods based on ConvNeXt network and designs a new bilinear CBAM; simultaneously, a multiscale, multi-perspective and all-around attention framework is proposed, which is then applied in ConvNeXt. Experimental verification shows that the accuracy rate of the improved ConvNeXt for fine-grained image classification reaches 87.8%, 91.2%, and 93.2% on fine-grained classification datasets CUB-200-2011, Stanford Cars, and FGVC Aircraft, respectively, showing increases of 2.7%, 0.3% and 0.4%, respectively, compared to those of the original network without optimization, and increases of 3.7%, 8.0% and 2.0%, respectively, compared to those of the traditional BCNN. In addition, ablation experiments are set up to verify the effectiveness of the proposed attention framework.

Feature relocation network for fine-grained image classification

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 fine-grained 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 state-of-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.

Fine-grained image classification is a difficult problem, and previous studies mainly overcome this problem by locating multiple discriminative regions in different scales and then aggregating complementary information explored from the located regions. However, locating discriminative regions introduces heavy overhead and is not suitable for real-world application. In this paper, we propose the recursive multi-scale channel-spatial attention module (RMCSAM) for addressing this problem. Following the experience of previous research on fine-grained image classification, RMCSAM explores multi-scale attentional information. However, the attentional information is explored by recursively refining the deep feature maps of a convolutional neural network (CNN) to better correspond to multi-scale channel-wise and spatial-wise attention, instead of localizing attention regions. In this way, RMCSAM provides a lightweight module that can be inserted into standard CNNs. Experimental results show that RMCSAM can improve the classification accuracy and attention capturing ability over baselines. Also, RMCSAM performs better than other state-of-the-art attention modules in fine-grained image classification, and is complementary to some state-of-the-art approaches for fine-grained image classification. Code is available at https://github.com/Dichao-Liu/Recursive-Multi-Scale-Channel-Spatial-Attention-Module.

Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval. The code of this model will be publicly available at1.

The public has long been interested in computer vision research projects on image classification. Over the past 20 years, fine-grained image classification (FGIC) has advanced quickly because of the ongoing development of deep neural network models. The FDIC is based on the traditional image classification and further identifies the subtle differences between subclasses within the same category. Deep learning-based image categorization techniques are separated into two groups in this article: FGIC based on intensely supervised learning and weakly supervised learning. Briefly, it introduces the algorithms included in each category. Additionally, this article lists the performance of several methods on the well-known CUB-200 dataset and gives typical fine-grained picture datasets. By comparing several algorithms' outputs, it is determined that weakly supervised learning has the advantages of lower cost and higher accuracy than intensely supervised learning. Finally, the paper proposes a summary and a discussion of fine-grained images' potential future development prospects.

Fine-grained image classification is a challenging research topic in the field of computer vision, whose goal is to identify subclasses, such as distinguishing between different kinds of dogs. In order to overcome this problem, we combine the advantages of artificial feature with deep convolutional activation feature and design support vector machines (SVM) based on the importance of features. In this paper, we first use the bilinear neural network model to extract the deep convolutional activation feature of samples and combine it with artificial feature. The bilinear form simplifies gradient computation and allows end-to-end training. Then, multi-kernel SVM based on weighted features are trained to complete the image classification task. Finally, we present experiments and visualizations on FGVC-Aircraft and Stanford Dogs databases that analyze the effects of combined features and the multi-kernel SVM on the fine-grained object classification. The 83.8% and 66.1% accuracy proves the effectiveness of our strategy.

The fine-grained image classification task is about differentiating between different object classes. The difficulties of the task are large intra-class variance and small inter-class variance. For this reason, improving models’ accuracies on the task heavily relies on discriminative parts’ annotations and regional parts’ annotations. Such delicate annotations’ dependency causes the restriction on models’ practicability. To tackle this issue, a saliency module based on a weakly supervised fine-grained image classification model is proposed by this article. Through our salient region localization module, the proposed model can localize essential regional parts with the use of saliency maps, while only image class annotations are provided. Besides, the bilinear attention module can improve the performance on feature extraction by using higher- and lower-level layers of the network to fuse regional features with global features. With the application of the bilinear attention architecture, we propose the different layer feature fusion module to improve the expression ability of model features. We tested and verified our model on public datasets released specifically for fine-grained image classification. The results of our test show that our proposed model can achieve close to state-of-the-art classification performance on various datasets, while only the least training data are provided. Such a result indicates that the practicality of our model is incredibly improved since fine-grained image datasets are expensive.

In recent years, deep learning techniques have demonstrated their powerful feature extraction capabilities in a variety of visual analysis tasks. Especially in the field of fine-grained image classification, deep feature extraction technology has achieved great performance improvement. The depth features extracted based on the deep learning model have strong expressive power. Fine-grained image classification is intended to subclass large categories, such as different types of birds. This identification task is more challenging because these sub-classes have large interclass differences and small inter-class differences. This paper first introduces the research progress of fine-grained image classification. Then, the data set used in the experiment and the extended data set are introduced. A variable convolution method for fine-grained image classification is proposed, and the research direction of the next step is summarized and explored.

Based on the demand of fine-grained image classification and recognition technology of red tide algae, the Faster R-CNN algorithm in the deep learning algorithm is studied. At the same time, according to the characteristics of subtle inter-class difference and intra-class difference between red tide algae, FPN algorithm was studied in the detection algorithm by computer aided technology.. On this basis, TensorFlow is used to carry out experimental simulation, and the results of classification and recognition of fine-grained image of tidal algae based on different scale feature information are compared. The experimental results show that the Faster R-CNN algorithm performs generally in the classification and recognition of fine-grained image of tidal algae, but the detection results are significantly improved by combining the FPN algorithm.

Image classification is an important research branch in the field of computer vision. Ordinary image classification focuses on the discrimination of object differences, while fine-grained image classification focuses on the distinction between different sub-categories. Considering the small differences between sub-categories and the larger differences within sub-categories due to factors such as shooting angle, background, and posture, fine-grained image recognition becomes a challenging task. In order to solve the problem of fine-grained object classification, this paper proposes a finegrained object classification method based on block diagonal feature and ensemble learning. The Gabor feature of the object image is block diagonalized by using low-rank recovery technology, and the low-rank feature representation of the object is constructed by introducing the block diagonal sparse regular term to increase the discriminability of features between object sub-categories. On this basis, the stacking ensemble learning method is used to classify objects, and a fine-grained real animal image and non-real animal image (toy animal image) classification database is constructed. And the algorithm design and experiment are completed. Through the proposed algorithm, accuracy of fine-grained classification of objects is improved

Fine-grained image classification is a challenging problem, due to the small inter-class variance caused by highly similar subordinate categories and large intra-class variance in poses, viewpoints and rotations. In this paper, we propose a novel end-to-end model for fine-grained image classification(FGIC). The proposed model consists of two sub-networks: detection sub-network and classification sub-network. The detection sub-network is constructed on the basis of R-FCN, and the classification sub-network contains a two-steam CNN for feature extraction and three fully connected layers for object classification. In addition, the network compression technology is adopted in both of the sub-networks to improve efficiency and reduce storage space. Experimental results on the CUB-200-2011 shows that the accuracy of our method is close to state-of-the-art with higher efficiency and lower storage requirement than the other compared methods (10 frames/sec during inference on TitanX). The proposed high-efficiency framework is believed to be effective in some of the practical applications, especially in the applications of mobile terminals.