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Maximum-Entropy Fine-Grained Classification [article]

Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik
2018 arXiv   pre-print
Utilizing this notion of small visual diversity, we revisit Maximum-Entropy learning in the context of fine-grained classification, and provide a training routine that maximizes the entropy of the output  ...  Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data.  ...  We will now proceed to describing the results obtained from maximum-entropy fine-grained classification.  ... 
arXiv:1809.05934v2 fatcat:7sisnsbvrraytjp3bkhdlybxhq

Exploiting Category Similarity-based Distributed Labeling for Fine-Grained Visual Classification

Pengzhen Du, Zeren Sun, Yazhou Yao, Zhenmin Tang
2020 IEEE Access  
FINE-GRAINED VISUAL CLASSIFICATION Fine-grained visual classification essentially focuses on representing visual differences between subcategories [48] , [49] .  ...  INDEX TERMS Fine-grained classification, label distributions, category similarity, distributed labels.  ... 
doi:10.1109/access.2020.3030249 fatcat:k32y6jwqhfextmngzi5b5vbecm

Maximum Entropy Regularization and Chinese Text Recognition [article]

Changxu Cheng, Wuheng Xu, Xiang Bai, Bin Feng, Wenyu Liu
2020 arXiv   pre-print
Experiments on Chinese character recognition, Chinese text line recognition and fine-grained image classification achieve consistent improvement, proving that the regularization is beneficial to generalization  ...  We propose to apply Maximum Entropy Regularization to regularize the training process, which is to simply add a negative entropy term to the canonical cross-entropy loss without any additional parameters  ...  It is a typical and popular dataset for fine-grained image classification.  ... 
arXiv:2007.04651v1 fatcat:3tahtsd7w5gpjehtjwnnsw6gku

Fine-Grained Named Entity Recognition Using Conditional Random Fields for Question Answering [chapter]

Changki Lee, Yi-Gyu Hwang, Hyo-Jung Oh, Soojong Lim, Jeong Heo, Chung-Hee Lee, Hyeon-Jin Kim, Ji-Hyun Wang, Myung-Gil Jang
2006 Lecture Notes in Computer Science  
We used CRFs to detect boundary of named entities and Maximum Entropy (ME) to classify named entity classes.  ...  In many QA systems, fine-grained named entities are extracted by coarse-grained named entity recognizer and fine-grained named entity dictionary.  ...  To solve this problem, we break down the NE task in two parts; boundary detection using CRFs and NE classification using Maximum Entropy (ME).  ... 
doi:10.1007/11880592_49 fatcat:x5e3gsijpbb63bx4wqra3qoqtu

Seabed Characterization through Image Processing of Side Scan Sonar Case Study: Bontang and Batam

Subarsyah Subarsyah, Lukman Arifin
2019 Bulletin of the Marine Geology  
In contrast to Bontang, in Batam the entropy exhibit the opposite value, high value are correlated to fine sediment and vice versa.  ...  Entropy value is maximum when most of pixel value image is in the middle of the colour spectrum range (between very dark to very bright), in contrast, it is minimum when pixel value is in the spectrum  ...  Very fine grain sediments are associated with entropy 1 -75 and intensity 1-48, fine grain sediments are associated with entropy 1 -75 and intensity 33-99, medium grain sediments correlated to entropy  ... 
doi:10.32693/bomg.34.1.2019.590 fatcat:ahkmhap6tjg25didq3knkh5hhi

Bag of Tricks for Retail Product Image Classification [article]

Muktabh Mayank Srivastava
2020 arXiv   pre-print
Two other tricks we find to increase accuracy on retail product identification are using an instagram-pretrained Convnet and using Maximum Entropy as an auxiliary loss for classification.  ...  These tricks enable us to increase the accuracy of fine tuned convnets for retail product image classification by a large margin.  ...  Maximum Entropy loss as an auxiliary loss Maximum Entropy loss has been previously used for fine grained visual classification [7] .  ... 
arXiv:2001.03992v1 fatcat:es6ew55tknfqhdg7z5dqlklysq

A New State-of-The-Art Czech Named Entity Recognizer [chapter]

Jana Straková, Milan Straka, Jan Hajič
2013 Lecture Notes in Computer Science  
The recognizer is based on Maximum Entropy Markov Model and a Viterbi algorithm decodes an optimal sequence labeling using probabilities estimated by a maximum entropy classifier.  ...  The classification features utilize morphological analysis, two-stage prediction, word clustering and gazetteers.  ...  The results for the fine-grained classification were not published.  ... 
doi:10.1007/978-3-642-40585-3_10 fatcat:2j2gvhkvenc7nfkj4fddbmklce

Topic and Sentiment Unification Maximum Entropy Model for Online Review Analysis

Changlin Ma, Meng Wang, Xuewen Chen
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15 Companion  
To address these problems, a novel topic and sentiment unification maximum entropy LDA model is proposed in this paper for fine-grained opinion mining of online reviews.  ...  In this model, a maximum entropy component is first added to the traditional LDA model to distinguish background words, aspect words and opinion words and further realize both the local and global extraction  ...  In this paper, a topic and sentiment unification maximum entropy LDA model (TSU MaxEnt-LDA) is proposed for fine-grained opinion mining.  ... 
doi:10.1145/2740908.2741704 dblp:conf/www/MaWC15 fatcat:spfest47uzc5vhgvpdql65yw5u

Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification

Chuanyi Zhang, Yazhou Yao, Huafeng Liu, Guo-Sen Xie, Xiangbo Shu, Tianfei Zhou, Zheng Zhang, Fumin Shen, Zhenmin Tang
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Accordingly, learning directly from web images for fine-grained visual classification (FGVC) has attracted broad attention.  ...  Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is much superior to state-of-the-art webly supervised methods.  ...  Related Work Fine-grained Classification The task of fine-grained classification is to distinguish objects at subordinate level.  ... 
doi:10.1609/aaai.v34i07.6973 fatcat:wsyr5wxgubevvnuox6f77stknq

An Effective Feature-Weighting Model for Question Classification

Peng Huang, Jiajun Bu, Chun Chen, Guang Qiu
2007 2007 International Conference on Computational Intelligence and Security (CIS 2007)  
Question classification is one of the most important subtasks in Question Answering systems. Now question taxonomy is getting larger and more fine-grained for better answer generation.  ...  Many approaches to question classification have been proposed and achieve reasonable results.  ...  accuracies for coarse and fine-grained categories.  ... 
doi:10.1109/cis.2007.12 dblp:conf/cis/HuangBCQ07 fatcat:vzttwb2ylbcenlwkivnyxh27r4

Using Dependency Analysis to Improve Question Classification [chapter]

Phuong Le-Hong, Xuan-Hieu Phan, Tien-Dung Nguyen
2015 Advances in Intelligent Systems and Computing  
This paper proposes to use typed dependencies which are extracted automatically from dependency parses of questions to improve accuracy of classification.  ...  Question classification is a first necessary task of automatic question answering systems. Linguistic features play an important role in developing an accurate question classifier.  ...  Maximum Entropy Classifier Maximum Entropy (ME) models (a.k.a multinomial logistic regression model) is a general purpose discriminative learning method for classification and prediction which has been  ... 
doi:10.1007/978-3-319-11680-8_52 fatcat:qos22d7gmfh5tfnehbrqdbhxre

Mining Discriminative Triplets of Patches for Fine-Grained Classification [article]

Yaming Wang, Jonghyun Choi, Vlad I. Morariu, Larry S. Davis
2016 arXiv   pre-print
Fine-grained classification involves distinguishing between similar sub-categories based on subtle differences in highly localized regions; therefore, accurate localization of discriminative regions remains  ...  Its effectiveness is demonstrated using four publicly available fine-grained datasets, on which it outperforms or achieves comparable performance to the state-of-the-art in classification.  ...  Fine-Grained Classification We demonstrate fine-grained classification results on three standard fine-grained car datasets.  ... 
arXiv:1605.01130v1 fatcat:gsf5nxmilrfw5go3plonqn7cwa

Mining Discriminative Triplets of Patches for Fine-Grained Classification

Yaming Wang, Jonghyun Choi, Vlad I. Morariu, Larr S. Davis
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Fine-grained classification involves distinguishing between similar sub-categories based on subtle differences in highly localized regions; therefore, accurate localization of discriminative regions remains  ...  Its effectiveness is demonstrated using four publicly available fine-grained datasets, on which it outperforms or achieves comparable performance to the state-of-the-art in classification.  ...  Fine-Grained Classification We demonstrate fine-grained classification results on three standard fine-grained car datasets.  ... 
doi:10.1109/cvpr.2016.131 dblp:conf/cvpr/WangCMD16 fatcat:4gpmqjy24jefhdos3krju2d7qq

Combining contextual features for word sense disambiguation

Hoa Trang Dang, Martha Palmer
2002 Proceedings of the ACL-02 workshop on Word sense disambiguation recent successes and future directions -  
In this paper we present a maximum entropy Word Sense Disambiguation system we developed which performs competitively on SENSEVAL-2 test data for English verbs.  ...  We demonstrate that using richer linguistic contextual features significantly improves tagging accuracy, and compare the system's performance with human annotator performance in light of both fine-grained  ...  Maximum entropy models have been applied to a wide range of classification tasks in NLP (Ratnaparkhi, 1998) .  ... 
doi:10.3115/1118675.1118688 dblp:conf/semeval/DangP02 fatcat:bj4llw2hcjbitn57tpbz7yw64a

Regularizing Class-wise Predictions via Self-knowledge Distillation [article]

Sukmin Yun, Jongjin Park, Kimin Lee, Jinwoo Shin
2020 arXiv   pre-print
Our experimental results on various image classification tasks demonstrate that the simple yet powerful method can significantly improve not only the generalization ability but also the calibration performance  ...  Interestingly, this simple idea significantly improves the performances of fine-grained classification tasks.  ...  (c) Top-1 error rates (%) of fine-grained label classification. ours does not use additional teacher networks.  ... 
arXiv:2003.13964v2 fatcat:v4dxtzyj7vfmdhdirbluo2ttte
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