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Unified Embedding and Metric Learning for Zero-Exemplar Event Detection [article]

Noureldien Hussein, Efstratios Gavves, Arnold W.M. Smeulders
2017 arXiv   pre-print
In contrast, we learn a joint space in which the visual and textual representations are embedded. The space casts a novel event as a probability of pre-defined events.  ...  Given a text describing a novel event, the goal is to rank related videos accordingly. This task is zero-exemplar, no video examples are given to the novel event.  ...  Acknowledgment We thank Dennis Koelma, Masoud Mazloom and Cees Snoek 2 for lending their insights and technical support for this work.  ... 
arXiv:1705.02148v1 fatcat:bunj6b6etfechp6vmkojf3x2aa

Event Detection Using Multi-level Relevance Labels and Multiple Features

Zhongwen Xu, Ivor W. Tsang, Yi Yang, Zhigang Ma, Alexander G. Hauptmann
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
We address the challenging problem of utilizing related exemplars for complex event detection while multiple features are available.  ...  In this paper, we propose an algorithm which adaptively utilizes the related exemplars by cross-feature learning.  ...  The procedure is repeated until convergence and the final unified detector is used for event detection.  ... 
doi:10.1109/cvpr.2014.20 dblp:conf/cvpr/XuTYMH14 fatcat:dldia55stjgf3amcwqbtvamzuq

Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos [article]

Mohamed Elhoseiny, Jingen Liu, Hui Cheng, Harpreet Sawhney, Ahmed Elgammal
2015 arXiv   pre-print
We propose a new zero-shot Event Detection method by Multi-modal Distributional Semantic embedding of videos.  ...  To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information  ...  Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.  ... 
arXiv:1512.00818v2 fatcat:yw5bdk55k5cbdhnqzskneiotva

Recent Advances in Zero-shot Recognition [article]

Yanwei Fu, Tao Xiang, Yu-Gang Jiang, Xiangyang Xue, Leonid Sigal, and Shaogang Gong
2017 arXiv   pre-print
One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/ learning.  ...  This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, and from datasets and evaluation settings.  ...  Yanwei Fu is supported by The Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning.  ... 
arXiv:1710.04837v1 fatcat:u3mp6dgj2rgqrarjm4dcywegmy

Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases [article]

Shrimai Prabhumoye, Rafal Kocielnik, Mohammad Shoeybi, Anima Anandkumar, Bryan Catanzaro
2022 arXiv   pre-print
We select a few class-balanced exemplars from a small support repository that are closest to the query to be labeled in the embedding space.  ...  We observe that the largest 530B parameter model is significantly more effective in detecting social bias compared to smaller models (achieving at least 13 AUC metric compared to other models).  ...  TF-IDF We use Term Frequency-Inverse Document Frequency (TF-IDF) to project the text Q and the posts from D in the common embedding space (Scikit-learn, 2022b).  ... 
arXiv:2112.07868v2 fatcat:fpuhidapxjcftnab32u34r2yre

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  ., +, TMM 2021 2843-2856 Domain-Oriented Semantic Embedding for Zero-Shot Learning. Min, S., +, TMM 2021 3919-3930 Driver Yawning Detection Based on Subtle Facial Action Recognition.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Recent approaches to non-intrusive load monitoring techniques in residential settings

Yung Fei Wong, Y. Ahmet Sekercioglu, Tom Drummond, Voon Siong Wong
2013 2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG)  
In this paper, we provide a review of recent research efforts on state-of-the-art NILM algorithms before concluding with a baseline and overall vision for our future research direction.  ...  The concept of Smart Grids is closely related to energy conservation and load shedding concepts.  ...  Various machine learning techniques have been used in previous works Event Based Non Event Based Event detection is performed on every sample but inference is done only on detected edges.  ... 
doi:10.1109/ciasg.2013.6611501 dblp:conf/ciasg/WongSDW13 fatcat:cn7vlb4ktvbgvpr75xesgoh75m

Semi-Supervised Machine Condition Monitoring by Learning Deep Discriminative Audio Features

Iordanis Thoidis, Marios Giouvanakis, George Papanikolaou
2021 Electronics  
In this study, we aim to learn highly descriptive representations for a wide set of machinery sounds and exploit this knowledge to perform condition monitoring of mechanical equipment.  ...  By fusing the supervised feature learning approach with an unsupervised deep one-class neural network, we are able to model the characteristics of each source and implicitly detect anomalies in different  ...  Acknowledgments: We would like to thank Lazaros Vrysis for his collaboration in the conceptual-  ... 
doi:10.3390/electronics10202471 fatcat:cqzs32cy2vhmrkxu3cg4j7izfe

Small Sample Learning in Big Data Era [article]

Jun Shu, Zongben Xu, Deyu Meng
2018 arXiv   pre-print
This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures.  ...  The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis.  ...  Afterwards, Cao et al. (2017a) proposed a unified multi-view subspace learning method for CCA using the graph embedding framework for visual recognition and cross-modal retrieval.  ... 
arXiv:1808.04572v3 fatcat:lqqzzrmgfnfb3izctvdzgopuny

Reviewing continual learning from the perspective of human-level intelligence [article]

Yifan Chang, Wenbo Li, Jian Peng, Bo Tang, Yu Kang, Yinjie Lei, Yuanmiao Gui, Qing Zhu, Yu Liu, Haifeng Li
2021 arXiv   pre-print
Humans' continual learning (CL) ability is closely related to Stability Versus Plasticity Dilemma that describes how humans achieve ongoing learning capacity and preservation for learned information.  ...  According to the taxonomy, evaluation metrics, algorithms, applications as well as some open issues are then introduced.  ...  for future events.  ... 
arXiv:2111.11964v1 fatcat:je5lyidbongfxj4v67zxs2a3bi

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 2552-2567 Deep Unbiased Embedding Transfer for Zero-Shot Learning.  ...  Zeng, Z., +, TIP 2020 5912-5923 Deep Salient Object Detection With Contextual Information Guidance. Liu, Y., +, TIP 2020 360-374 Deep Unbiased Embedding Transfer for Zero-Shot Learning.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Table of contents

2020 IEEE Transactions on Image Processing  
Hou, and F. Xu 1944 Deep Unbiased Embedding Transfer for Zero-Shot Learning ........ Z. Jia, Z. Zhang, L. Wang, C. Shan, and T.  ...  Yokoya, and W. He 3652 Zero-VAE-GAN: Generating Unseen Features for Generalized and Transductive Zero-Shot Learning .................. ................................................. R. Gao, X.  ... 
doi:10.1109/tip.2019.2940372 fatcat:h23ul2rqazbstcho46uv3lunku

Table of Contents

2021 IEEE/ACM Transactions on Audio Speech and Language Processing  
Nelus and R. Martin Multi-Branch Convolutional Macaron net for Sound Event Detection . . . . . . . . . . . . . . . . ......T. K. Chan and C. S.  ...  Wu Sentiment Time Series Calibration for Event Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..J. Wu, L. Shang, and X.  ...  Speech Enhancement and Separation  ... 
doi:10.1109/taslp.2021.3137066 fatcat:ocit27xwlbagtjdyc652yws4xa

An Empirical Study of Dimensional Reduction Techniques for Facial Action Units Detection [article]

Zhuo Hui, Wen-Sheng Chu
2016 arXiv   pre-print
For F1, results were mixed. For both metrics, the pattern of results varied among action units.  ...  For further comparison, a no-DR control condition was included as well. Linear support vector machine classifiers with independent train and test sets were used for AU detection.  ...  of facial events.  ... 
arXiv:1603.08039v1 fatcat:fwml735ttvgcji4hq25az3pjzi

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective [article]

Jing Zhang and Wanqing Li and Philip Ogunbona and Dong Xu
2019 arXiv   pre-print
This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look  ...  This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition.  ...  They critically compare and analyse the state-of-the-art methods and unifies the data splits of training and test sets as well as the evaluation protocols for zero-shot learning.  ... 
arXiv:1705.04396v3 fatcat:iknfmppi5zca7ljovdlwvdwluu
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