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Spatio-temporal Video Re-localization by Warp LSTM
[article]
2019
arXiv
pre-print
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In this paper, we make an answer to the question of when and where by formulating a new task, namely spatio-temporal video re-localization. ...
To accurately localize the desired tubelets in the reference video, we propose a novel warp LSTM network, which propagates the spatio-temporal information for a long period and thereby captures the corresponding ...
Acknowledgement This work is partially supported by NSF awards #1704309, #1722847, and #1813709. ...
arXiv:1905.03922v1
fatcat:nzwrmbleijbunfy2e7ztiidmz4
DeepFake Video Detection: A Time-Distributed Approach
2020
SN Computer Science
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Arduous efforts have been put to detect the forgery in still images, but the authors leveraged the spatio-temporal features of the videos by taking sequences of frames as input to the model. ...
Thus, this paper aims to efficiently and holistically detect manipulated videos generated using DeepFake, which is the most effective deep learning powered technique developed so far by the researchers ...
EfficientNet wrapped in a timedistributed layer followed by an LSTM layer, extending the CNN Architecture to learn spatio-temporal features has been proposed. ...
doi:10.1007/s42979-020-00225-9
fatcat:6ze2k2gctbehfa3mzb552rzvz4
Spatio-Temporal Texture Features for Presentation Attack Detection in Biometric Systems
2019
2019 Eighth International Conference on Emerging Security Technologies (EST)
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Patterns of motion history are used as primary features followed by secondary feature extraction using Local Binary Patterns and Convolutional Neural Networks, and evaluated using the Replay Attack and ...
In this paper, we propose a novel spatio-temporal feature, based on motion history, which can offer an efficient way to encapsulate temporal texture changes. ...
We follow their paper and re-implement their algorithm for the CASIA-FA dataset. The CNN+LSTM method represents the effectiveness of the end-to-end neural network for spatio-temporal texture changes. ...
doi:10.1109/est.2019.8806220
dblp:conf/est/PanD19
fatcat:5odjko3wtjfjpfpupyjn2wrmge
Video Super Resolution Based on Deep Learning: A Comprehensive Survey
[article]
2022
arXiv
pre-print
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We also discuss some challenges, which need to be further addressed by researchers in the community of VSR. ...
It is well known that the leverage of information within video frames is important for video super-resolution. ...
This work was supported by the National Natural Science Foundation of China (Nos. 61976164, 61876220, 61876221, and 61906184). ...
arXiv:2007.12928v3
fatcat:nxoejcfdnzas3jznbqsale36ty
Temporal Memory Relation Network for Workflow Recognition from Surgical Video
[article]
2021
arXiv
pre-print
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To effectively incorporate the two types of cues without disturbing the joint learning of spatio-temporal features, we introduce a non-local bank operator to attentively relate the past to the present. ...
Through our designed temporal variation layer, the supportive cues are further enhanced by multi-scale temporal-only convolutions. ...
Deep residual network and LSTM module are seamlessly integrated into a unified framework to jointly capture spatio-temporal features of videos. Lea et al. ...
arXiv:2103.16327v1
fatcat:fcgn4be4gbe7zhqpmol6uesjai
Inferring, Predicting, and Denoising Causal Wave Dynamics
[article]
2020
arXiv
pre-print
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Results confirm that DISTANA is ready to model real-world spatio-temporal dynamics such as brain imaging, supply networks, water flow, or soil and weather data patterns. ...
We show that DISTANA is very well-suited to denoise data streams, given that re-occurring patterns are observed, significantly outperforming alternative approaches, such as temporal convolution networks ...
the spatio-temporal wave dynamics). ...
arXiv:2009.09187v1
fatcat:exd7uh6bjjftjfqwe67bqyxlzm
Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey
[chapter]
2017
Gesture Recognition
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Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for ...
Hugo Jair Escalante was supported by CONACyT under grants CB2014-241306 and PN-215546. ...
Acknowledgments This work has been partially supported by the Spanish projects TIN2015-66951-C2-2-R and TIN2016-74946-P (MINECO/FEDER, UE) and CERCA Programme / Generalitat de Catalunya. ...
doi:10.1007/978-3-319-57021-1_19
fatcat:d2m5oyomsjhkbfpunhefho6ayq
Hierarchical LSTM for Sign Language Translation
2018
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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It firstly explores spatio-temporal cues of video clips by 3D CNN and packs appropriate visemes by online key clip mining with adaptive variable-length. ...
It tackles different granularities by conveying spatio-temporal transitions among frames, clips and viseme units. ...
Zhou was supported in part by NSFC under Contract 61472378 and Contract 61632019, in part by the Young Elite Scientists Sponsorship Program by CAST under Grant 2016QNRC001, and in part by the Fundamental ...
doi:10.1609/aaai.v32i1.12235
fatcat:vnkmy3xe3fhhdaof3oist6d7vq
Video Processing using Deep learning Techniques: A Systematic Literature Review
2021
IEEE Access
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We observe the significant advancements in video processing between 2017 and 2020, primarily due to the advent of AlexNet, ResNet, and LSTM based deep learning techniques. ...
This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by ...
Authors listed convolutional RBM, Fast R-CNN, 3D CNN, PCANet, deep Gaussian Mixture Model, Convolutional AutoEncoder (CAE) with LSTM (CAE -LSTM), Spatio-temporal CNN, and GAN based approach. ...
doi:10.1109/access.2021.3118541
fatcat:oadlu4uyirc2tanqrixz3sn6ny
Semantic Image Networks for Human Action Recognition
[article]
2019
arXiv
pre-print
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The semantic image is obtained by applying localized sparse segmentation using global clustering (LSSGC) prior to the approximate rank pooling which summarizes the motion characteristics in single or multiple ...
We also propose the sequential combination of Inception-ResNetv2 and long-short-term memory network (LSTM) to leverage the temporal variances for improved recognition performance. ...
We know the SemI use ARP which retains the temporal information from the video sequences by ranking them. ...
arXiv:1901.06792v1
fatcat:lloazaywnnhphe3poe7f6n6dci
Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action Recognition
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Each 3D action is presented by a sequence of 3D poses. Similar to NBNN, our proposed Spatio-Temporal-NBNN applies stage-to-class distance to classify actions. ...
However, ST-NBNN takes the spatio-temporal structure of 3D actions into consideration and relaxes the Naive Bayes assumption of NBNN. ...
Acknowledgement This work is supported in part by Singapore Ministry of Education Academic Research Fund Tier 2 MOE2015-T2-2-114. ...
doi:10.1109/cvpr.2017.55
dblp:conf/cvpr/WengWY17
fatcat:ru63lgvrc5cxzolc4hytvvx4hu
Self-Supervised Video Object Segmentation by Motion-Aware Mask Propagation
[article]
2021
arXiv
pre-print
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We propose a self-supervised spatio-temporal matching method, coined Motion-Aware Mask Propagation (MAMP), for video object segmentation. ...
MAMP then propagates the masks from the memory bank to subsequent frames according to our proposed motion-aware spatio-temporal matching module to handle fast motion and long-term matching scenarios. ...
Acknowledgements This research was supported by the ARC Industrial Transformation Research Hub IH180100002. ...
arXiv:2107.12569v2
fatcat:escvlh6fsbclllgv34xtsd5paq
Single Shot Video Object Detector
[article]
2020
arXiv
pre-print
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A valid question is how to explore temporal coherence across frames for boosting detection. ...
In this paper, we propose to address the problem by enhancing per-frame features through aggregation of neighboring frames. ...
Spatio-temporal Sampling. ...
arXiv:2007.03560v1
fatcat:xnyo6fmp3jd4pcuelttjgoi6ly
Deep Video Deblurring: The Devil is in the Details
[article]
2019
arXiv
pre-print
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State-of-the-art deep networks exploit temporal information from neighboring frames, either by means of spatio-temporal transformers or by recurrent architectures. ...
Video deblurring for hand-held cameras is a challenging task, since the underlying blur is caused by both camera shake and object motion. ...
[22] extend the DBN model by a 3D spatio-temporal transformer, which transforms the inputs to the reference frame. ...
arXiv:1909.12196v1
fatcat:qnpy7jucsrfwrlbcsusuhxqoey
Detection of Physical Strain and Fatigue in Industrial Environments Using Visual and Non-Visual Low-Cost Sensors
2022
Technologies
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Moreover, a new multi-modal dataset of video and heart rate data captured in a real manufacturing workplace during car door assembly activities is introduced. ...
In this work, we demonstrate how the input from low-cost sensors, specifically, passive camera sensors installed in a real manufacturing workplace, and smartwatches used by the workers can provide useful ...
The analysis of this information is usually handled as a spatio-temporal mining task on 2D or 3D skeletal body representations into consecutive video frames. ...
doi:10.3390/technologies10020042
fatcat:v5c6zwqueneqdbq6exsjxzt4im
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