Learning Representation of Multi-Scale Object for Fine-Grained Image Retrieval

2021 ◽  
Author(s):  
Kangbo Sun ◽  
Jie Zhu
Keyword(s):  
Image Retrieval ◽  
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 A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Haopeng Lei ◽  
Simin Chen ◽  
Mingwen Wang ◽  
Xiangjian He ◽  
Wenjing Jia ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Online Shopping ◽  
Unsolved Problem ◽  
Correct Match ◽  
Retrieval Accuracy ◽  
Fine Grained ◽  
Cross Domain ◽  
Domain Transformation ◽  
Domain Similarity ◽  
Natural Way

Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.

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.

Sign in / Sign up

Export Citation Format

Share Document