Reasearch on Cross Domain Few-shot Learning Method Based on Local Feature Association

2021 ◽  
Author(s):  
Yuan Ding ◽  
Ping Wang
Keyword(s):  
Local Feature ◽  
Learning Method ◽  
Cross Domain ◽  
Feature Association

Transfer Dictionary Learning Method for Cross-Domain Multimode Process Monitoring and Fault Isolation

2020 ◽  
Vol 69 (11) ◽  
pp. 8713-8724 ◽  
Author(s):  
Keke Huang ◽  
Haofei Wen ◽  
Can Zhou ◽  
Chunhua Yang ◽  
Weihua Gui
Keyword(s):  
Process Monitoring ◽  
Dictionary Learning ◽  
Fault Isolation ◽  
Learning Method ◽  
Cross Domain

scholarly journals From design experiences to generic mechanisms: Model-based learning in analogical design

1996 ◽  
Vol 10 (2) ◽  
pp. 131-136 ◽  
Author(s):  
Sambasiva R. Bhatta ◽  
Ashok K. Goel
Keyword(s):  
Analogical Reasoning ◽  
Feedback Mechanism ◽  
Short Paper ◽  
Creative Design ◽  
Learning Method ◽  
Model Based ◽  
Cross Domain ◽  
Behavior Function ◽  
Function Models ◽  
Case Based

AbstractAnalogical reasoning plays an important role in design. In particular, cross-domain analogies appear to be important in innovative and creative design. However, making cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions useful in adapting past designs. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from design experiences in familiar domains by abstraction over patterns of regularity. Three important issues in abstraction from experiences are what to abstract from an experience, how far to abstract, and what methods to use. In this short paper, we describe how structure-behavior-function models of designs in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we describe the model-based learning method with a scenario of learning feedback mechanism.

scholarly journals Part-Based Visual Tracking via Online Weighted P-N Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Heng Fan ◽  
Jinhai Xiang ◽  
Jun Xu ◽  
Honghong Liao
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
The State ◽  
Tracking Algorithm ◽  
Local Feature ◽  
Experimental Results ◽  
Learning Method ◽  
Target Model ◽  
Tracking Process

We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.

scholarly journals Selecting Discriminative Binary Patterns for a Local Feature

2015 ◽  
Vol 15 (3) ◽  
pp. 104-113
Author(s):  
Yingying Li ◽  
Jieqing Tan ◽  
Jinqin Zhong
Keyword(s):  
Object Recognition ◽  
Image Matching ◽  
State Of The Art ◽  
Local Feature ◽  
High Dimensionality ◽  
Learning Method ◽  
Local Descriptor ◽  
Local Descriptors

Abstract The local descriptors based on a binary pattern feature have state-of-the-art distinctiveness. However, their high dimensionality resists them from matching faster and being used in a low-end device. In this paper we propose an efficient and feasible learning method to select discriminative binary patterns for constructing a compact local descriptor. In the selection, a searching tree with Branch&Bound is used instead of the exhaustive enumeration, in order to avoid tremendous computation in training. New local descriptors are constructed based on the selected patterns. The efficiency of selecting binary patterns has been confirmed by the evaluation of these new local descriptors’ performance in experiments of image matching and object recognition.

Cross-Domain Change Object Detection Using Generative Adversarial Networks

2019 ◽  
Vol 31 (2) ◽  
pp. 221-230
Author(s):  
Takuma Sugimoto ◽  
◽  
Kanji Tanaka ◽  
Kousuke Yamaguchi
Keyword(s):  
Change Detection ◽  
Fundamental Problem ◽  
Local Feature ◽  
Generative Adversarial Networks ◽  
Recent Success ◽  
Cross Domain ◽  
Object Proposal ◽  
Reference Images ◽  
Visual Place Recognition ◽  
Object Level

Image change detection is a fundamental problem for robotic map maintenance and long-term map learning. Local feature-based image comparison is one of the most basic schemes for addressing this problem. However, the local-feature approach encounters difficulties when the query and reference images involve different domains (e.g., time of the day, weather, season). In this paper, we address the local-feature approach from the novel perspective of object-level region features. This study is inspired by the recent success of object-level region features in cross-domain visual place recognition (CD-VPR). Unlike the previous contributions of the CD-VPR task, in the cross-domain change detection (CD-CD) tasks, we consider matching a small part (i.e., the change) of the scene and not the entire image, which is considerably more demanding. To address this issue, we explore the use of two independent object proposal techniques: supervised object proposal (e.g., YOLO) and unsupervised object proposal (e.g., BING). We combine these techniques and compute appearance features of their arbitrarily shaped objects by aggregating local features from a deep convolutional neural network (DCN). Experiments using a publicly available cross-season NCLT dataset validate the efficacy of the proposed approach.

A deep transfer learning method based on stacked autoencoder for cross-domain fault diagnosis

2021 ◽  
Vol 408 ◽  
pp. 126318
Author(s):  
Ziwei Deng ◽  
Zhuoyue Wang ◽  
Zhaohui Tang ◽  
Keke Huang ◽  
Hongqiu Zhu
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Learning Method ◽  
Stacked Autoencoder ◽  
Cross Domain
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