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Few-Shot Learning for Video Object Detection in a Transfer-Learning Scheme [article]

Zhongjie Yu, Gaoang Wang, Lin Chen, Sebastian Raschka, Jiebo Luo
2021 arXiv   pre-print
In this paper, we study the new problem of few-shot learning for video object detection.  ...  We first define the few-shot setting and create a new benchmark dataset for few-shot video object detection derived from the widely used ImageNet VID dataset.  ...  Therefore, it becomes more imperative to design a model for video object detection given a few videos of novel-class objects. This poses a new problem of few-shot learning for video object detection.  ... 
arXiv:2103.14724v2 fatcat:fnprbfwpajgflineeamb5uytli

Comparison Network for One-Shot Conditional Object Detection [article]

Tengfei Zhang, Yue Zhang, Xian Sun, Hao Sun, Menglong Yan, Xue Yang, Kun Fu
2020 arXiv   pre-print
However, there may not always be sufficient samples in many scenarios, which leads to the research on few-shot detection as well as its extreme variation one-shot detection.  ...  In this paper, the one-shot detection has been formulated as a conditional probability problem.  ...  Compared to the advances in few-shot image classification, few works have addressed on few-shot object detection so far. [28] uses few-shot detection in a semi-supervised learning framework.  ... 
arXiv:1904.02317v2 fatcat:cbv4xx4re5dwtgp2cesnrb7tpm

Incorporating Concept Ontology for Hierarchical Video Classification, Annotation, and Visualization

Jianping Fan, Hangzai Luo, Yuli Gao, R. Jain
2007 IEEE transactions on multimedia  
To avoid the inter-level error transmission problem, a novel hierarchical boosting scheme is proposed by incorporating concept ontology and multitask learning to boost hierarchical video classifier training  ...  Automatic video concept detection via semantic classification is one promising solution to bridge the semantic gap.  ...  Worring for handling the review process of their paper.  ... 
doi:10.1109/tmm.2007.900143 fatcat:ts2cgdhakreanildysxdmcbncm

Cross Modal Few-Shot Contextual Transfer for Heterogenous Image Classification

Zhikui Chen, Xu Zhang, Wei Huang, Jing Gao, Suhua Zhang
2021 Frontiers in Neurorobotics  
To alleviate the difficulty, we propose a cross-modal few-shot contextual transfer method that leverages the contextual information as a supplement and learns context awareness transfer in few-shot image  ...  Besides, to better extract local visual features and reorganize the recognition pattern, the deep transfer scheme is also used for reusing a powerful extractor from the pre-trained model.  ...  Zhou and Mu (2020) used meta-learning to transfer large-scale richly annotated image data between domains for few-shot video classification.  ... 
doi:10.3389/fnbot.2021.654519 pmid:34108871 pmcid:PMC8180855 fatcat:ue5u75pc6nf7hgpkbj6zjry4ie

Guest Editorial Introduction to the Special Section on Representation Learning for Visual Content Understanding

Jiwen Lu, Yuxin Peng, Guo-Jun Qi, Jun Yu
2020 IEEE transactions on circuits and systems for video technology (Print)  
Over the past years, his research interests have included multimedia analysis, machine learning, and image processing. In 2017, he received the IEEE SPS Best Paper Award.  ...  He has served as a program committee member or reviewer for top conferences and prestigious journals.  ...  to tackle two drawbacks in few-shot image classification: image independent and class imbalance.  ... 
doi:10.1109/tcsvt.2020.3009095 fatcat:5gew2gv32zg3tfwjaavrtknr2e

Cross Modal Distillation for Supervision Transfer [article]

Saurabh Gupta, Judy Hoffman, Jitendra Malik
2015 arXiv   pre-print
In this work we propose a technique that transfers supervision between images from different modalities.  ...  We use learned representations from a large labeled modality as a supervisory signal for training representations for a new unlabeled paired modality.  ...  This work was supported by ONR SMARTS MURI N00014-09-1-1051, a Berkeley Graduate Fellowship, a Google Fellowship in Computer Vision and a NSF Graduate Research Fellowship.  ... 
arXiv:1507.00448v2 fatcat:gf5pjshslnekho67oqzjdexjli

Cross Modal Distillation for Supervision Transfer

Saurabh Gupta, Judy Hoffman, Jitendra Malik
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
In this work we propose a technique that transfers supervision between images from different modalities.  ...  We use learned representations from a large labeled modality as supervisory signal for training representations for a new unlabeled paired modality.  ...  We report performance for three different schemes for initializing the flow model: a) Random Init (No PreTr) when the flow network is initialized randomly using the weight initialization scheme used for  ... 
doi:10.1109/cvpr.2016.309 dblp:conf/cvpr/GuptaHM16 fatcat:ajmyho3735dhnkkwo3qkjebpl4

A Real-Time Deep Transfer Learning-Based Classification and Social Distance Alert Framework Based on Covid-19

Anurag Singh, Naresh Kumar, Tapas Kumar
2021 International Journal of Current Research and Review  
Methods: In this novel research work, we are implementing transfer learning methodology to improve the learning of a related objective task on top of base deep learning model in developing a mask/non-mask  ...  Results: We used object detection model Single Shot Multi-box Detector and classification model mobile net, which achieved significant accuracy and much faster for both training and inference with prediction  ...  This use treats transfer learning as a kind of weight initialization scheme.  ... 
doi:10.31782/ijcrr.2021.sp203 fatcat:v4wcdoggrffmhnifl4jklfomcu

A Survey of Deep Learning for Low-Shot Object Detection [article]

Qihan Huang, Haofei Zhang, Mengqi Xue, Jie Song, Mingli Song
2022 arXiv   pre-print
Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce  ...  Low-Shot Object Detection (LSOD) is an emerging research topic of detecting objects from a few or even no annotated samples, consisting of One-Shot Object Detection (OSOD), Few-Shot Object Detection (FSOD  ...  Transfer-Learning Methods Transfer-learning methods regard Few-Shot Object Detection as a transfer-learning problem in which the source domain is the base dataset, and the target domain is the novel dataset  ... 
arXiv:2112.02814v3 fatcat:54s6meub5rcrtiaeo4ylzviofy

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  -that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  ., +, TMM 2021 4220-4231 Learning Dual-Pooling Graph Neural Networks for Few-Shot Video Classi-fication.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Progressive Object Transfer Detection

Hao Chen, Yali Wang, Guoyou Wang, Xiang Bai, Yu Qiao
2019 IEEE Transactions on Image Processing  
In WSTD, we design a recurrent object labelling mechanism for learning to annotate weakly-labeled images.  ...  Inspired by this procedure of learning to detect, we propose a novel Progressive Object Transfer Detection (POTD) framework. Specifically, we make three main contributions in this paper.  ...  In this work, we design a novel Progressive Object Transfer Detection (POTD) framework, for learning to detect like humans.  ... 
doi:10.1109/tip.2019.2938680 fatcat:vcqeip2q3vc2vfzp7vfqshj3xq

One-Shot Action Localization by Learning Sequence Matching Network

Hongtao Yang, Xuming He, Fatih Porikli
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
To address this challenge, we conceptualize a new example-based action detection problem where only a few examples are provided, and the goal is to find the occurrences of these examples in an untrimmed  ...  Towards this objective, we introduce a novel oneshot action localization method that alleviates the need for large amounts of training samples.  ...  Acknowledgments The Titan X used for this research was donated by the NVIDIA Corporation.  ... 
doi:10.1109/cvpr.2018.00157 dblp:conf/cvpr/YangHP18 fatcat:3wjwgzpulvgh5adsm3b4xx37jq

Learning object class detectors from weakly annotated video

Alessandro Prest, C. Leistner, J. Civera, C. Schmid, V. Ferrari
2012 2012 IEEE Conference on Computer Vision and Pattern Recognition  
We propose a fully automatic pipeline that localizes objects in a set of videos of the class and learns a detector for it.  ...  This paper introduces an approach for learning object detectors from realworld web videos known only to contain objects of a target class.  ...  IT 26/10 and a Google Research Award.  ... 
doi:10.1109/cvpr.2012.6248065 dblp:conf/cvpr/PrestLCSF12 fatcat:u5kgn6tkhveszfwef2kmrphrka

Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

Samee Ullah Khan, Ijaz Ul Haq, Seungmin Rho, Sung Wook Baik, Mi Young Lee
2019 Applied Sciences  
In this paper, we proposed a violence detection scheme for movies that is comprised of three steps.  ...  Next, these selected frames are passed from a light-weight deep learning model, which is fine-tuned using a transfer learning approach to classify violence and non-violence shots in a movie.  ...  In this paper, a three folded movie analysis scheme is proposed to detect the violent scenes.  ... 
doi:10.3390/app9224963 fatcat:2pyhsug6srh3laf5mcwfmij4oy

Special Issue on Robustness and Efficiency in the Convergence of Artificial Intelligence and IoT

Meikang Qiu, Bhavani Thuraisingham, Mahmoud Daneshmand, Huansheng Ning, Payam Barnaghi
2021 IEEE Internet of Things Journal  
In the article "FSLM: An intelligent few-shot learning model based on Siamese networks for IoT technology," Yang et al. propose an intelligent few-shot learning model based on Siamese networks.  ...  In the article "DDLPF: A practical decentralized deep learning paradigm for Internet-of-Things applications," Wu et al. propose a decentralized DL paradigm with privacypreservation and fast few-shot learning  ... 
doi:10.1109/jiot.2021.3073800 fatcat:yyhchydxabfsxjnvvfi7hsoexq
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