Fine-grained Object Recognition based on Multi-Scale Destruction and Construction Learning

2021 ◽  
Author(s):  
Jun Luo ◽  
Yingzhe Jiang ◽  
Jinyi Qiu
Keyword(s):  
Object Recognition ◽  
Fine Grained ◽  
Multi Scale

scholarly journals Non-Local and Multi-Scale Mechanisms for Image Inpainting

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3281
Author(s):  
Xu He ◽  
Yong Yin
Keyword(s):  
Markov Random Fields ◽  
Receptive Fields ◽  
Image Inpainting ◽  
Long Distance ◽  
Visual Appearance ◽  
Fine Grained ◽  
Multi Scale ◽  
Non Local ◽  
Relationship Of

Recently, deep learning-based techniques have shown great power in image inpainting especially dealing with squared holes. However, they fail to generate plausible results inside the missing regions for irregular and large holes as there is a lack of understanding between missing regions and existing counterparts. To overcome this limitation, we combine two non-local mechanisms including a contextual attention module (CAM) and an implicit diversified Markov random fields (ID-MRF) loss with a multi-scale architecture which uses several dense fusion blocks (DFB) based on the dense combination of dilated convolution to guide the generative network to restore discontinuous and continuous large masked areas. To prevent color discrepancies and grid-like artifacts, we apply the ID-MRF loss to improve the visual appearance by comparing similarities of long-distance feature patches. To further capture the long-term relationship of different regions in large missing regions, we introduce the CAM. Although CAM has the ability to create plausible results via reconstructing refined features, it depends on initial predicted results. Hence, we employ the DFB to obtain larger and more effective receptive fields, which benefits to predict more precise and fine-grained information for CAM. Extensive experiments on two widely-used datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art approaches both in quantity and quality.

RAMS-Trans: Recurrent Attention Multi-scale Transformer for Fine-grained Image Recognition

2021 ◽  
Author(s):  
Yunqing Hu ◽  
Xuan Jin ◽  
Yin Zhang ◽  
Haiwen Hong ◽  
Jingfeng Zhang ◽  
...  
Keyword(s):  
Image Recognition ◽  
Fine Grained ◽  
Multi Scale

scholarly journals Fine-grained classification based on multi-scale pyramid convolution networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254054
Author(s):  
Gaihua Wang ◽  
Lei Cheng ◽  
Jinheng Lin ◽  
Yingying Dai ◽  
Tianlun Zhang
Keyword(s):  
Data Augmentation ◽  
Convolution Kernel ◽  
Residual Network ◽  
Complementary Information ◽  
Fine Grained ◽  
Multi Scale ◽  
Key Factor ◽  
Object Part ◽  
Weakly Supervised ◽  
Subtle Feature

The large intra-class variance and small inter-class variance are the key factor affecting fine-grained image classification. Recently, some algorithms have been more accurate and efficient. However, these methods ignore the multi-scale information of the network, resulting in insufficient ability to capture subtle changes. To solve this problem, a weakly supervised fine-grained classification network based on multi-scale pyramid is proposed in this paper. It uses pyramid convolution kernel to replace ordinary convolution kernel in residual network, which can expand the receptive field of the convolution kernel and use complementary information of different scales. Meanwhile, the weakly supervised data augmentation network (WS-DAN) is used to prevent over fitting and improve the performance of the model. In addition, a new attention module, which includes spatial attention and channel attention, is introduced to pay more attention to the object part in the image. The comprehensive experiments are carried out on three public benchmarks. It shows that the proposed method can extract subtle feature and achieve classification effectively.

scholarly journals Scaling property of ideal granitic sequences

2007 ◽  
Vol 14 (3) ◽  
pp. 237-246 ◽  
Author(s):  
D. Xu ◽  
Q. Cheng ◽  
F. Agterberg
Keyword(s):  
Lake Area ◽  
Fine Grained ◽  
Multi Scale ◽  
Invariance Properties ◽  
The Past ◽  
Ideal Granite ◽  
Markov Properties ◽  
The Ideal

Abstract. Quantification of granite textures and structures using a mathematical model for characterization of granites has been a long-term attempt of mathematical geologists over the past four decades. It is usually difficult to determine the influence of magma properties on mineral crystallization forming fined-grained granites due to its irregular and fine-grained textures. The ideal granite model was originally developed for modeling mineral sequences from first and second-order Markov properties. This paper proposes a new model for quantifying scale invariance properties of mineral clusters and voids observed within mineral sequences. Sequences of the minerals plagioclase, quartz and orthoclase observed under the microscope for 104 aplite samples collected from the Meech Lake area, Gatineau Park, Québec were used for validation of the model. The results show that the multi-scale approaches proposed in this paper may enable quantification of the nature of the randomness of mineral grain distributions. This, in turn, may be related to original properties of the magma.

scholarly journals The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets

2020 ◽  
Vol 9 (9) ◽  
pp. 497
Author(s):  
Haydn Lawrence ◽  
Colin Robertson ◽  
Rob Feick ◽  
Trisalyn Nelson
Keyword(s):  
Social Media ◽  
Data Quality ◽  
Spatial Scales ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Quality Metric ◽  
Fine Grained ◽  
Multi Scale ◽  
Feasible Study

Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplace, the relevant scales over which these data apply to is typically unknown. For researchers to use VGI appropriately (e.g., aggregated to areal units (e.g., neighbourhoods) to elicit key trend or demographic information), general methods for assessing the quality are required, particularly, the explicit linkage of data quality and relevant spatial scales, as there are no accepted standards or sampling controls. We present a data quality metric, the Spatial-comprehensiveness Index (S-COM), which can delineate feasible study areas or spatial extents based on the quality of uneven and dynamic geographically referenced VGI. This scale-sensitive approach to analyzing VGI is demonstrated over different grains with data from two citizen science initiatives. The S-COM index can be used both to assess feasible study extents based on coverage, user-heterogeneity, and density and to find feasible sub-study areas from a larger, indefinite area. The results identified sub-study areas of VGI for focused analysis, allowing for a larger adoption of a similar methodology in multi-scale analyses of VGI.

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