2,092 Hits in 11.3 sec

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends [article]

Asmat Zahra, Nazia Perwaiz, Muhammad Shahzad, Muhammad Moazam Fraz
2022 arXiv   pre-print
It aims to automatically identify/search persons in a multi-camera network having non-overlapping field-of-views.  ...  Person re-identification (Re-ID) is one of the primary components of an automated visual surveillance system.  ...  In order to reduce the impact of false positive matches they have employed hard negative mining. KNN searching was then repeated in a reverse manner to ensure the best match found.  ... 
arXiv:2202.13121v1 fatcat:luwwbcwspndqpauj4dosmmojee

The Elements of Temporal Sentence Grounding in Videos: A Survey and Future Directions [article]

Hao Zhang, Aixin Sun, Wei Jing, Joey Tianyi Zhou
2022 arXiv   pre-print
Then we review the techniques for multimodal understanding and interaction, which is the key focus of TSGV for effective alignment between the two modalities.  ...  Lastly, we discuss issues with the current TSGV research and share our insights about promising research directions.  ...  TGA learns visual-text alignment at the video-level by maximizing matching scores of positive samples while minimizing scores of negative samples.  ... 
arXiv:2201.08071v1 fatcat:2k2if6dsyveinec2dmmujcmhkq

Multi-Task Learning in Natural Language Processing: An Overview [article]

Shijie Chen, Yu Zhang, Qiang Yang
2021 arXiv   pre-print
In this paper, we give an overview of the use of MTL in NLP tasks.  ...  Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP).  ...  Therefore, M(z ) injects task-specific bias into the attention map in the self-attention mechanism. Similar adaptation operations are used in the input alignment and layer normalization as well.  ... 
arXiv:2109.09138v1 fatcat:hlgzjykuvzczzmsgnl32w5qo5q

Survey on graph embeddings and their applications to machine learning problems on graphs

Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj
2021 PeerJ Computer Science  
compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems.  ...  Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding.  ...  The authors replace the adjacency matrix by learnable self-attention in form of a fully-connected layer with activation and further normalization with softmax.  ... 
doi:10.7717/peerj-cs.357 pmid:33817007 pmcid:PMC7959646 fatcat:ntronyrbgfbedez5dks6h4hoq4

A Roadmap for Big Model [article]

Sha Yuan, Hanyu Zhao, Shuai Zhao, Jiahong Leng, Yangxiao Liang, Xiaozhi Wang, Jifan Yu, Xin Lv, Zhou Shao, Jiaao He, Yankai Lin, Xu Han (+88 others)
2022 arXiv   pre-print
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.  ...  At present, there is a lack of research work that sorts out the overall progress of BMs and guides the follow-up research.  ...  A typical entity matching system usually consists of a blocking module and a matching module. Matching between two tables with N and M entity records requires determining a total N × M entity pairs.  ... 
arXiv:2203.14101v4 fatcat:rdikzudoezak5b36cf6hhne5u4

Deep Entity Matching with Pre-Trained Language Models [article]

Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, Wang-Chiew Tan
2020 arXiv   pre-print
This way, Ditto is forced to learn "harder" to improve the model's matching capability. The optimizations we developed further boost the performance of Ditto by up to 9.8%.  ...  We present Ditto, a novel entity matching system based on pre-trained Transformer-based language models.  ...  Each dataset consists of candidate pairs from two structured tables of entity records of the same schema. The pairs are sampled from the results of blocking and manually labeled.  ... 
arXiv:2004.00584v2 fatcat:cflewd3mrvcmnerazqrzcqzcxq

Deep Learning Techniques for Future Intelligent Cross-Media Retrieval [article]

Sadaqat ur Rehman, Muhammad Waqas, Shanshan Tu, Anis Koubaa, Obaid ur Rehman, Jawad Ahmad, Muhammad Hanif, Zhu Han
2020 arXiv   pre-print
With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention.  ...  The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems  ...  [133] proposed Cross-media Relation Attention Network (CRAN) with multilevel alignment.  ... 
arXiv:2008.01191v1 fatcat:t63bg55w2vdqjcprzaaidrmprq

Visual and Object Geo-localization: A Comprehensive Survey [article]

Daniel Wilson, Xiaohan Zhang, Waqas Sultani, Safwan Wshah
2021 arXiv   pre-print
The entity of interest may be an image, sequence of images, a video, satellite image, or even objects visible within the image.  ...  The concept of geo-localization refers to the process of determining where on earth some 'entity' is located, typically using Global Positioning System (GPS) coordinates.  ...  It is well proven that based which aligned the camera location to the center of its quality hard samples in training dataset decrease the training corresponding satellite image.  ... 
arXiv:2112.15202v1 fatcat:ipwas72ro5ho5fjiakm6de7ji4

Recent Trends in Deep Learning Based Natural Language Processing [article]

Tom Young, Devamanyu Hazarika, Soujanya Poria, Erik Cambria
2018 arXiv   pre-print
We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.  ...  Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP).  ...  [174] LSTM seq2seq with MMI objective 5.22 Lowe et al. [97] Dual LSTM encoders for semantic matching 55.22 Dodge et al. [177] Memory networks 63.72 Zhou et al.  ... 
arXiv:1708.02709v8 fatcat:guliplxoqfb43pw7jtlrhebcui

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey [article]

Julian Wörmann, Daniel Bogdoll, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Ludger van Elst, Tobias Gleißner, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels (+34 others)
2022 arXiv   pre-print
The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.  ...  This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge.  ...  On the other hand, when hard constraints are a part of the neural network structure [370, 495] the networks should be attentive to the physics involved, and their predictions should not deviate from  ... 
arXiv:2205.04712v1 fatcat:u2bgxr2ctnfdjcdbruzrtjwot4

Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies [article]

Yu Huang, Yue Chen
2020 arXiv   pre-print
This is a survey of autonomous driving technologies with deep learning methods.  ...  Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019.  ...  It is seen that pedestrian behaviour modelling methods mostly employ the RNN/LSTM to formulate the temporal pattern, while GAN-based methods boost the prediction model training with adversarial samples  ... 
arXiv:2006.06091v3 fatcat:nhdgivmtrzcarp463xzqvnxlwq

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases [article]

Gerhard Weikum, Luna Dong, Simon Razniewski, Fabian Suchanek
2021 arXiv   pre-print
Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI.  ...  On top of this, the article discusses the automatic extraction of entity-centric properties.  ...  We also appreciate the sustained encouragement and support by our editors Surajit Chaudhuri, Joe Hellerstein and Ihab Ilyas.  ... 
arXiv:2009.11564v2 fatcat:vh2lqfmhhbcwpf6dcsej3hhvgy

A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data

Kshitij Khare, Sang-Yun Oh, Syed Rahman, Bala Rajaratnam
2019 Machine Learning  
Acknowledgment We have been fortunate to have our colleagues and collaborators give us their impressions and contributions toward the contents of this book. We would like to  ...  Classification Techniques in Bioinformatics ◾ 287 Boosting As with bagging, the boosting technique is characterized by three components: sampling of the train set, a set of classifiers that form the  ...  Hash Suffix-Based Trees -With the short sequence reads it is a challenge to obtain the exact matches of the reads using BLAST. us researchers tend to favor inexact matches of sequence for alignments.  ... 
doi:10.1007/s10994-019-05810-5 fatcat:nulmjvxvwjgojfoe2ywv3pjrpu

DeepHealth: Review and challenges of artificial intelligence in health informatics [article]

Gloria Hyunjung Kwak, Pan Hui
2020 arXiv   pre-print
Artificial intelligence has provided us with an exploration of a whole new research era.  ...  This article presents a comprehensive review of research applying artificial intelligence in health informatics, focusing on the last seven years in the fields of medical imaging, electronic health records  ...  They used the Bi-LSTM conditional random field network to recognize entities and the Bi-LSTM attention network to extract relations.  ... 
arXiv:1909.00384v2 fatcat:sy7pm2c2uvdd3pal2russn4xri

Machine Learning Applications for Therapeutic Tasks with Genomics Data [article]

Kexin Huang, Cao Xiao, Lucas M. Glass, Cathy W. Critchlow, Greg Gibson, Jimeng Sun
2021 arXiv   pre-print
In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development.  ...  Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and development tasks.  ...  Biomedical Text Mining (BioTxtM2016)’, pp. 10–19.  ... 
arXiv:2105.01171v1 fatcat:d2nbrjt4tvak7momoxxjlmqk2m
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