1,262 Hits in 3.1 sec

Digging Into Self-Supervised Learning of Feature Descriptors [article]

Iaroslav Melekhov and Zakaria Laskar and Xiaotian Li and Shuzhe Wang and Juho Kannala
2021 arXiv   pre-print
In this work, we focus on understanding the limitations of existing self-supervised approaches and propose a set of improvements that combined lead to powerful feature descriptors.  ...  To address this challenge, recent weakly- and self-supervised methods can learn feature descriptors from relative camera poses or using only synthetic rigid transformations such as homographies.  ...  In this paper, we delve deeper into these factors for the task of local image descriptors learning.  ... 
arXiv:2110.04773v1 fatcat:h35nlejkifhjrl25nha4sqj5ti

3rd Place: A Global and Local Dual Retrieval Solution to Facebook AI Image Similarity Challenge [article]

Xinlong Sun, Yangyang Qin, Xuyuan Xu, Guoping Gong, Yang Fang, Yexin Wang
2021 arXiv   pre-print
Specifically, we attempt many strategies to optimize global descriptors, including abundant data augmentations, self-supervised learning with a single Transformer model, overlay detection preprocessing  ...  We show some ablation experiments of our method, which reveals the complementary advantages of global and local features.  ...  Global Descriptor Recently, self-supervised learning (SSL) with Transformers [2, 6] enjoyed the same success in CV.  ... 
arXiv:2112.02373v2 fatcat:7rwuh2fncbgpffubtspus7cuiu

RS-SSKD: Self-Supervision Equipped with Knowledge Distillation for Few-Shot Remote Sensing Scene Classification

Pei Zhang, Ying Li, Dong Wang, Jiyue Wang
2021 Sensors  
Few-shot classification offers a different picture under the umbrella of meta-learning: digging rich knowledge from a few data are possible.  ...  Secondly, we set a round of self-knowledge distillation to prevent overfitting and boost the performance.  ...  C O N V C O N V C O N V Self-Supervised Network Most of the prior works [61, 62] in computer vision weave self-supervision into fewshot learning by adding pretext tasks loss.  ... 
doi:10.3390/s21051566 pmid:33668138 pmcid:PMC7956409 fatcat:x6znnh3lqfbz3jmvzckreh37km

Implementation of Transfer Learning Using VGG16 on Fruit Ripeness Detection

Jasman Pardede, Department of Informatics Engineering, Institut Teknologi Nasional Bandung, Bandung, Indonesia, Benhard Sitohang, Saiful Akbar, Masayu Leylia Khodra
2021 International Journal of Intelligent Systems and Applications  
In previous studies, researchers have determined the classification of fruit ripeness using the feature descriptor using color features (RGB, GSL, HSV, and L * a * b *).  ...  This study shows that the performance of deep learning using transfer learning always gets better performance than using machine learning with traditional feature extraction to determines fruit ripeness  ...  Deep learning approaches categorized into four categories [15] , namely: Deep Supervised learning, Deep semi-supervised learning, deep unsupervised learning, and deep reinforcement learning.  ... 
doi:10.5815/ijisa.2021.02.04 fatcat:dss22rga4ngx5co5rludhisfxy

Continuous Geodesic Convolutions for Learning on 3D Shapes [article]

Zhangsihao Yang, Or Litany, Tolga Birdal, Srinath Sridhar, Leonidas Guibas
2020 arXiv   pre-print
In this work, we wish to challenge this practice and use a neural network to learn descriptors directly from the raw mesh. To this end, we introduce two modules into our neural architecture.  ...  The majority of descriptor-based methods for geometric processing of non-rigid shape rely on hand-crafted descriptors.  ...  In the following, we will first present the details of our LRF computation and then dig deeper into the continuous convolutions.  ... 
arXiv:2002.02506v1 fatcat:u2qs2e4vrbghfpujtffyyofhfe

Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning

Jie Feng, Hongshuai Wang, Yujin Ji, Youyong Li
2021 Nano Select  
In this review, we introduce a state-of-art theoretical methodology of the synergy of high-throughput screening and machine learning (ML) in accelerating the discovery of high-efficient OSC materials.  ...  We present key details, rules and experience in database construction, selection of molecular features, fast-screening calculations, models training and their predication capabilities.  ...  According to whether there is known output data or not, ML models are mainly divided into supervised learning and unsupervised learning.  ... 
doi:10.1002/nano.202100006 fatcat:aerwe5x4bbb5rktdi4qvviz3ei

Context-Aware Mouse Behavior Recognition Using Hidden Markov Models

Zheheng Jiang, Danny Crookes, Brian D Green, Yunfeng Zhao, Haiping Ma, Ling Li, Shengping Zhang, Dacheng Tao, Huiyu Zhou
2018 IEEE Transactions on Image Processing  
In particular, we here propose a hybrid deep learning architecture, where the first unsupervised layer relies on an advanced spatial-temporal segment Fisher vector encoding both visual and contextual features  ...  Subsequent supervised layers based on our segment aggregate network are trained to estimate the state-dependent observation probabilities of the HMM.  ...  Wang [26] designed a trajectory-pooled deep convolutional descriptor (TDD), whose goal is to combine the benefits of both trajectory-based and deep-learned features.  ... 
doi:10.1109/tip.2018.2875335 pmid:30307863 fatcat:r6ubbqh36bg45bshecimyb72jq

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning [article]

Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, Berend Smit
2020 arXiv   pre-print
An important part of this review are the different approaches that are used to represent these materials in feature space.  ...  The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised  ...  Supervised learning can also be used as part of an active learning loop for self-driving laboratories and to efficiently optimize reaction conditions.  ... 
arXiv:2001.06728v2 fatcat:cqtzxirbr5dkrplrwyo7yt7lze

STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text [article]

Nawshad Farruque, Randy Goebel, Osmar Zaiane, Sudhakar Sivapalan
2021 arXiv   pre-print
We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task notorious for training data scarcity, i.e.  ...  We start with a comprehensive synthesis of different components of our ZSL modeling and analysis of our ground truth samples and Depression symptom clues curation process with the help of a practicing  ...  Since we use a supervised classification baseline, we randomly split our data-set into 80% train-set (≈ 205 Tweets) and 20% (≈ 50 Tweets) test-set.  ... 
arXiv:2106.10928v2 fatcat:kc6b34verzaklj5ntm5anvhs2q

A Comprehensive Review of Group Activity Recognition in Videos

Li-Fang Wu, Qi Wang, Meng Jian, Yu Qiao, Bo-Xuan Zhao
2021 International Journal of Automation and Computing  
Second, we survey the group activity recognition methods, including those based on handcrafted features and those based on deep learning networks.  ...  For better understanding of the pros and cons of these methods, we compare various models from the past to the present.  ...  Compared with learned features, handcrafted descriptors are often not learned and quantified automatically for discrimination and their discrimination powers are usually not guaranteed.  ... 
doi:10.1007/s11633-020-1258-8 fatcat:ycka4thcy5a6vghpenpthtrndi

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, Berend Smit
2020 Chemical Reviews  
We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation  ...  In the second part, we review how the different approaches of machine learning have been applied to porous materials.  ...  Supervised Learning: Feature Matrix and Labels Are Given. The most widely used flavor, which is also the focus of this review, is supervised learning.  ... 
doi:10.1021/acs.chemrev.0c00004 pmid:32520531 pmcid:PMC7453404 fatcat:l2745cqxl5fcnnwty73j2ckkyq

Semi-supervised Node Classification with Discriminable Squeeze Excitation Graph Convolutional Networks

Nan Jia, Xiaolin Tian, Yang Zhang, Fengge Wang
2020 IEEE Access  
GCNs can reveal and dig deep into irregular data with spatial topological structure.  ...  In recent years, Graph Convolutional Networks (GCNs) have been increasingly and widely used in graph data representation and semi-supervised learning.  ...  Each node has a 500 dimension feature descriptor and all the nodes are divided into three classes. B.  ... 
doi:10.1109/access.2020.3015838 fatcat:2edlkfw4cvbene2fpdkl52g3ve

Coming to Grips with Age Prediction on Imbalanced Multimodal Community Question Answering Data

Alejandro Figueroa, Billy Peralta, Orietta Nicolis
2021 Information  
This trailing brings about an under-representation that has a harmful influence on the demographic analysis and on supervised machine learning models.  ...  As for textual inputs, we propose an age-batched greedy curriculum learning (AGCL) approach to lessen the effects of their inherent class imbalances.  ...  In the case of supervised machine learning, this means that model generalization can be hurt by an excessive amount of training material.  ... 
doi:10.3390/info12020048 fatcat:xvmng6a4yncgdfo5ef3xooeh3m

From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining

Alexandre Garcia, Pierre Colombo, Florence d'Alché-Buc, Slim Essid, Chloé Clavel
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
The task of predicting fine grained user opinion based on spontaneous spoken language is a key problem arising in the development of Computational Agents as well as in the development of social network  ...  We take advantage of the implicit hierarchical structure of opinions to build a joint fine and coarse grained opinion model that exploits different views of the opinion expression.  ...  Acknowledgements We would like to thanks Thibaud Besson and the whole french Cognitive Systems team of IBM for supporting our research with the server IBM Power AC922.  ... 
doi:10.18653/v1/d19-1556 dblp:conf/emnlp/GarciaCdEC19 fatcat:bhosavvjyvdh7hxckcawuur27q

Domain-invariant Similarity Activation Map Metric Learning for Retrieval-based Long-term Visual Localization [article]

Hanjiang Hu, Hesheng Wang, Zhe Liu, Weidong Chen
2020 arXiv   pre-print
We also propose a new adaptive triplet loss to boost the metric learning of the embedding in a self-supervised manner.  ...  In this work, a general architecture is first formulated probabilistically to extract domain-invariant feature through multi-domain image translation.  ...  ACKNOWLEDGMENT The authors would like to thank Zhijian Qiao from Department of Automation at Shanghai Jiao Tong University for his contribution to the real-site experiments including the collection of  ... 
arXiv:2009.07719v3 fatcat:liaewmrmkjgqtabqiewc7t4dlm
« Previous Showing results 1 — 15 out of 1,262 results