A Competitive Method for Dog Nose-print Re-identification

Shen, Fei; Wang, Zhe; Wang, Zijun; Xiaode, Fu,; Chen, Jiayi; Du, Xiaoyu; Tang, Jinhui

doi:10.48550/arxiv.2205.15934

Cited by 5 publications

(7 citation statements)

References 14 publications

(24 reference statements)

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“…The AdamW optimizer was utilized with an initial learning rate of 6e −4 and a weight decay of 2.5e −4 . To manage the learning rate, we adopted the Cosine Annealing Strategy with Warmup [51] and restart, where T-0 was set to 15 and T-mult to 2. Moreover, a batch size of 8 and 200 epochs were set for the Potsdam and Vaihingen datasets, respectively.…”

Section: Experiments Setting and Complement Detailsmentioning

confidence: 99%

HRFNet: A Novel Hierarchical Rich-scale Fusion Network for Remote Sensing Semantic Segmentation

Zhang,

Xie

2023

Preprint

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The semantic segmentation of high-resolution remote sensing images (HR-RSI) is crucial for a wide range of applications, such as precision agriculture, urban planning, natural resource assessment, and ecological monitoring. However, accurately classifying pixels in HR-RSI faces challenges due to densely distributed small objects and scale variations. Existing techniques, including Convolutional Neural Networks (CNNs) and other methods for hierarchical feature extraction and fusion of remote sensing image, often do not achieve the desired accuracy. In this paper, we propose a novel approach called the Hierarchical Rich-scale Fusion Network (HRFNet) to address these challenges. HRFNet utilizes advanced information rating and image partition techniques to extract rich-scale features within image layers. This allows for the adaptive exploration of both local and global contextual information. Moreover, we introduce a structured intra-layer to inter-layer feature aggregation module, which enables the adaptive extraction of fine-grained details and high-level semantic information from multi-layer feature maps in a highly flexible manner. Extensive experimentation has been conducted to validate the effectiveness of our proposed method. Our results demonstrate that HRFNet outperforms existing techniques, achieving state-of-the-art (SOTA) results on benchmark datasets, specifically the ISPRS Potsdam and Vaihingen datasets.

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Section: Experiments Setting and Complement Detailsmentioning

confidence: 99%

HRFNet: A Novel Hierarchical Rich-scale Fusion Network for Remote Sensing Semantic Segmentation

Zhang,

Xie

2023

Preprint

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“…Based on the general target detection evaluation index [19] when training VOC datasets [39], we effectively evaluate our proposed method and compare it with the existing state-of-the-art method.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…In SSS image detection field, methods [10][11][12] are mainly based on one-stage or two-stage detection methods, such as Faster R-CNN [10], Cascade RCNN [13], SSD [14], Yolo series [11,[15][16][17][18], et al Considering the characteristics of small targets, less target amount, and the seawater noise, many methods follow the improved strategy of general target detection. The backbone and neck, the basic part of existing detection methods [19], are improved based on self-structured and existing advanced modules [8,20,21]. EMRN [22] proposes a multi-resolution features dimension uniform module to fix dimensional features from images of varying resolutions.…”

Section: Introductionmentioning

confidence: 99%

D2MFNet: a Dual-Domain Multi-Frequency Network for Side-Scan Sonar Image Detection

Wang,

Zhang,

et al. 2023

Preprint

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The utilization of side-scan sonar (SSS) technology plays a pivotal role in ensuring the safety of underwater environments and facilitating the discovery of aquatic resources during daily production activities. Within this field, the use of SSS images for seabed target detection has gained widespread recognition. However, many existing detection approaches primarily concentrate on tracking the evolution path of optical image object detection tasks, resulting in complex structures and limited versatility. To tackle this issue, we introduce a pioneering Dual-Domain Multi-Frequency Network (D2MFNet) meticulously crafted to harness the distinct characteristics of SSS image detection. To maximize the information extracted from SSS images across various frequency ranges, we introduce a novel method for calculating the information flow of target ranges and propose a Multi-Frequency Combined Attention Mechanism (MFCAM). This mechanism amplifies the relevance of dual-domain features across different channels. Moreover, recognizing that SSS images can provide richer insights after frequency domain conversion, we introduce a Dual-Domain Feature Pyramid Network D2FPN. By incorporating frequency domain information representation, D2FPN significantly augments the depth and breadth of feature information in underwater small datasets. Our methods are seamlessly designed for integration into existing networks, offering plug-and-play functionality with substantial performance enhancements. We have conducted extensive experiments to validate the efficacy of our proposed techniques, and the results showcase their state-of-the-art performance. We will make our code and models publicly available at https://dagshub.com/estrellaww00/D2MFNet.

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“…This viewpoint change may cause deformation or occlusion of the target shape, thus causing difficulties for feature extraction. To solve the above problems, it is necessary to add a mechanism [1,[13][14][15] that can extract more detailed features when ReID extracts features to deal with the challenges brought by the drone perspective. The change in the UAV viewpoint makes the feature extraction algorithm need a certain robustness, which can correctly identify and describe the target in the case of significant changes in the viewpoint.…”

Section: Introductionmentioning

confidence: 99%

A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification

Yin,

Peng,

et al. 2023

Applied Sciences

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Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of unmanned aerial vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top–down features, and significant changes in vehicle scales. To address this, we propose a novel dual mixing attention network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play dual mixing attention module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of spatial mixing attention (SMA) and channel mixing attention (CMA). First, the original feature is divided according to the spatial and channel dimensions to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. In addition, DMAM can be easily plugged into any depth of the backbone network to improve vehicle recognition. The experimental results show that the proposed structure performs better than the representative method in the UAV-based vehicle ReID. Our code and models will be published publicly.

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A Competitive Method for Dog Nose-print Re-identification

Cited by 5 publications

References 14 publications

HRFNet: A Novel Hierarchical Rich-scale Fusion Network for Remote Sensing Semantic Segmentation

HRFNet: A Novel Hierarchical Rich-scale Fusion Network for Remote Sensing Semantic Segmentation

D2MFNet: a Dual-Domain Multi-Frequency Network for Side-Scan Sonar Image Detection

A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification

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