Multi-Scale Feature Fusion of Covariance Pooling Networks for Fine-Grained Visual Recognition

Qian, Lulu; Yu, Ting; Yang, Jianyu

doi:10.3390/s23083970

Cited by 4 publications

(2 citation statements)

References 58 publications

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“…Liu et al 35 proposed a deep recursive multiscale feature fusion network to address the multiscale feature fusion problem in single-image super-resolution, introducing the concept of gradient information aggregation. Qian et al 36 introduced a multiscale covariance pooling network, which combines features of different scales to generate more representative characteristics. It enhanced the effectiveness of fine-grained image classification.…”

Section: Object Detection Algorithms In Aerial Imagesmentioning

confidence: 99%

AIOD-YOLO: an algorithm for object detection in low-altitude aerial images

Yan,

Liu,

Lyu

et al. 2024

J. Electron. Imag.

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Aerial image object detection has a wide range of application values in civilian or military fields. Due to its unique high-altitude imaging viewpoint and the multiangle shooting method, aerial images lead to problems, such as small objects being detected in the image, large variations in object scales, and dense distribution. To alleviate the above problems, we propose an improved aerial image object detection algorithm aerial images object detection based on YOLO (AIOD-YOLO) based on YOLOv8-s. First, we propose the multibranch contextual information aggregation module. It enhances the network's perception of small objects by associating object information with the surrounding environment, thereby compensating for the lack of feature information for small objects. In addition, we propose the multilayer feature cascade efficient aggregation network, which leverages multigradient flow fusion of features at different scales. This approach aids the network in capturing a wide range of scale information and effectively mitigates the issue of missed detections caused by variations in object scales. Finally, we propose the adaptive task alignment label assignment strategy to address the issue of dense object distribution. The strategy incorporates the cosine similarity calculation to assess alignment globally and simultaneously adjusts the weights of positive and negative samples. We optimize the precision of label assignment for dense objects in aerial images, effectively resolving the challenges posed by their close proximity. The experiments on the VisDrone dataset demonstrate that AIOD-YOLO achieves a significant 7.2% improvement in mAP compared to the baseline model YOLOv8-s. The mAP0.5 of AIOD-YOLO is also improved by 14.1%, 7.9%, and 7.5% on SeaDronesSee v2, AI-TOD, and TinyPerson datasets, respectively, which validates the generalization of our proposed algorithm. AIOD-YOLO offers a superior information processing approach for tasks related to aerial image object detection in both civilian and military applications.

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Section: Object Detection Algorithms In Aerial Imagesmentioning

confidence: 99%

AIOD-YOLO: an algorithm for object detection in low-altitude aerial images

Yan,

Liu,

Lyu

et al. 2024

J. Electron. Imag.

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show abstract

“…Content-based image retrieval methods have made some progress, but most of them focus on global features, when faced with fine-grained image retrieval problems that need to focus on local features, the existing content-based image retrieval methods cannot achieve the desired retrieval effect ( Qian, Yu & Yang, 2023 ; Zeng et al, 2024 ).…”

Section: Related Workmentioning

confidence: 99%

Multi-FusNet: fusion mapping of features for fine-grained image retrieval networks

Cui,

Li,

Liu

et al. 2024

PeerJ Computer Science

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As the diversity and volume of images continue to grow, the demand for efficient fine-grained image retrieval has surged across numerous fields. However, the current deep learning-based approaches to fine-grained image retrieval often concentrate solely on the top-layer features, neglecting the relevant information carried in the middle layer, even though these information contains more fine-grained identification content. Moreover, these methods typically employ a uniform weighting strategy during hash code mapping, risking the loss of critical region mapping—an irreversible detriment to fine-grained retrieval tasks. To address the above problems, we propose a novel method for fine-grained image retrieval that leverage feature fusion and hash mapping techniques. Our approach harnesses a multi-level feature cascade, emphasizing not just top-layer but also intermediate-layer image features, and integrates a feature fusion module at each level to enhance the extraction of discriminative information. In addition, we introduce an agent self-attention architecture, marking its first application in this context, which steers the model to prioritize on long-range features, further avoiding the loss of critical regions of the mapping. Finally, our proposed model significantly outperforms existing state-of-the-art, improving the retrieval accuracy by an average of 40% for the 12-bit dataset, 22% for the 24-bit dataset, 16% for the 32-bit dataset, and 11% for the 48-bit dataset across five publicly available fine-grained datasets. We also validate the generalization ability and performance stability of our proposed method by another five datasets and statistical significance tests. Our code can be downloaded from https://github.com/BJFU-CS2012/MuiltNet.git.

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A Novel Multiscale Contrastive Learning Network for Fine-Grained Ocean Ship Classification

Dong,

Feng,

Fang

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Fine-grained ocean ship classification plays a crucial role in maritime military surveillance, traffic management, and anti-smuggling operations. However, the complex backgrounds of remote sensing images (RSIs), as well as significant inter-class similarities and intra-class differences, result in poor classification performance. Hence, we propose MSCL-Net, a multi-scale contrastive learning network for fine-grained ship classification (FGSC). First, we introduce ResNet50 as the backbone network and extract the multi-layer features by using the FPN for FGSC. Second, a channel spatial attention module (CSAM) is proposed to extract the similarity (contrastive) feature of the same class, strengthening the representation learning ability for addressing issues caused by significant inter-class similarity and intra-class difference. Third, a region cropping and enlargement module (RCEM) is proposed to extract the fine-grained features of local discriminant regions in RSIs to overcome the challenge of background complexity. Finally, we used the CSAM to fuse the features of the original image and the cropped region image for FGSC. Additionally, we introduce a combined loss based on center loss and PolyLoss to enhance the discrimination ability of features and make it more suitable for the imbalance dataset compared with cross-entropy. We use a public finegrained ship classification dataset, FGSC-23, and our FGSC-41 to evaluate the performance of MSCL-Net. The experimental results show superior performance compared to other stateof-the-art methods, highlighting the effectiveness of MSCL-Net in addressing the challenges associated with fine-grained ship classification. Ablation experiments also suggest the effectiveness of our design.

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Multi-Scale Feature Fusion of Covariance Pooling Networks for Fine-Grained Visual Recognition

Cited by 4 publications

References 58 publications

AIOD-YOLO: an algorithm for object detection in low-altitude aerial images

AIOD-YOLO: an algorithm for object detection in low-altitude aerial images

Multi-FusNet: fusion mapping of features for fine-grained image retrieval networks

A Novel Multiscale Contrastive Learning Network for Fine-Grained Ocean Ship Classification

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