Proceedings of the 7th ACM IKDD CoDS and 25th COMAD最新文献_第8页

IIITM Face: A Database for Facial Attribute Detection in Constrained and Simulated Unconstrained Environments IIITM Face:一种约束与模拟无约束环境下的人脸属性检测数据库

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2019-10-02 DOI: 10.1145/3371158.3371182

R. K. Gupta, Shresth Verma, K. Arya, Soumya Agarwal, Prince Gupta

{"title":"IIITM Face: A Database for Facial Attribute Detection in Constrained and Simulated Unconstrained Environments","authors":"R. K. Gupta, Shresth Verma, K. Arya, Soumya Agarwal, Prince Gupta","doi":"10.1145/3371158.3371182","DOIUrl":"https://doi.org/10.1145/3371158.3371182","url":null,"abstract":"This paper addresses the challenges of face attribute detection specifically in the Indian context. While there are numerous face datasets in unconstrained environments, none of them captures emotions in different facial orientations. Moreover, there is an under-representation of people of Indian ethnicity in these datasets since they have been scraped from popular search engines. As a result, the performance of state-of-the-art techniques can't be evaluated on Indian faces. In this work, we introduce a new dataset IIITM Face for scientific community to address these challenges. Our dataset includes 107 participants who exhibit 6 emotions in 3 different face orientations. Each of theses images are further labelled on attributes like gender, presence of moustache, beard or eyeglasses, clothes worn by the subjects and the density of their hair. Moreover, the images are captured in high resolution with specific background colors which can be easily replaced by cluttered backgrounds to simulate 'in the Wild' behavior. We demonstrate the same by constructing IIITM Face-SUE. Both IIITM Face and IIITM Face-SUE have been benchmarked across key multi-label metrics for the research community to compare their results.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128872579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots 基于深度强化学习的损伤机器人单次诊断与自适应

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2019-10-02 DOI: 10.1145/3371158.3371168

Shresth Verma, Haritha S. Nair, Gaurav Agarwal, J. Dhar, A. Shukla

引用次数: 6

Meta-Learning for Few-Shot Time Series Classification 基于元学习的少次时间序列分类

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2019-09-13 DOI: 10.1145/3371158.3371162

Jyoti Narwariya, Pankaj Malhotra, L. Vig, Gautam M. Shroff, T. Vishnu

{"title":"Meta-Learning for Few-Shot Time Series Classification","authors":"Jyoti Narwariya, Pankaj Malhotra, L. Vig, Gautam M. Shroff, T. Vishnu","doi":"10.1145/3371158.3371162","DOIUrl":"https://doi.org/10.1145/3371158.3371162","url":null,"abstract":"Deep neural networks (DNNs) have achieved state-of-the-art results on time series classification (TSC) tasks. In this work, we focus on leveraging DNNs in the often-encountered practical scenario where access to labeled training data is difficult, and where DNNs would be prone to overfitting. We leverage recent advancements in gradient-based meta-learning, and propose an approach to train a residual neural network with convolutional layers as a meta-learning agent for few-shot TSC. The network is trained on a diverse set of few-shot tasks sampled from various domains (e.g. healthcare, activity recognition, etc.) such that it can solve a target task from another domain using only a small number of training samples from the target task. Most existing meta-learning approaches are limited in practice as they assume a fixed number of target classes across tasks. We overcome this limitation in order to train a common agent across domains with each domain having different number of target classes, we utilize a triplet-loss based learning procedure that does not require any constraints to be enforced on the number of classes for the few-shot TSC tasks. To the best of our knowledge, we are the first to use meta-learning based pre-training for TSC. Our approach sets a new benchmark for few-shot TSC, outperforming several strong baselines on few-shot tasks sampled from 41 datasets in UCR TSC Archive. We observe that pre-training under the meta-learning paradigm allows the network to quickly adapt to new unseen tasks with small number of labeled instances.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122519318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

Taxonomical hierarchy of canonicalized relations from multiple Knowledge Bases 来自多个知识库的规范化关系的分类层次结构

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2019-09-13 DOI: 10.1145/3371158.3371186

Akshay Parekh, Ashish Anand, Amit Awekar

引用次数: 1

Competing Topic Naming Conventions in Quora: Predicting Appropriate Topic Merges and Winning Topics from Millions of Topic Pairs 在Quora竞争的主题命名约定:预测适当的主题合并和赢得主题从数以百万计的主题对

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2019-09-10 DOI: 10.1145/3371158.3371173

Binny Mathew, S. Maity, Pawan Goyal, Animesh Mukherjee

{"title":"Competing Topic Naming Conventions in Quora: Predicting Appropriate Topic Merges and Winning Topics from Millions of Topic Pairs","authors":"Binny Mathew, S. Maity, Pawan Goyal, Animesh Mukherjee","doi":"10.1145/3371158.3371173","DOIUrl":"https://doi.org/10.1145/3371158.3371173","url":null,"abstract":"Quora is a popular Q&A site which provides users with the ability to tag questions with multiple relevant topics which helps to attract quality answers. These topics are not predefined but user-defined conventions and it is not so rare to have multiple such conventions present in the Quora ecosystem describing exactly the same concept. In almost all such cases, users (or Quora moderators) manually merge the topic pair into one of the either topics, thus selecting one of the competing conventions. An important application for the site therefore is to identify such competing conventions early enough that should merge in future. In this paper, we propose a two-step approach that uniquely combines the anomaly detection and the supervised classification frameworks to predict whether two topics from among millions of topic pairs are indeed competing conventions, and should merge, achieving an F-score of 0.711. We also develop a model to predict the direction of the topic merge, i.e., the winning convention, achieving an F-score of 0.898. Our system is also able to predict ~ 25% of the correct case of merges within the first month of the merge and ~ 40% of the cases within a year. This is an encouraging result since Quora users on average take 936 days to identify such a correct merge.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114705556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2