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Conditional Prior Networks for Optical Flow
[chapter]
2018
Lecture Notes in Computer Science
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We introduce a novel architecture, called Conditional Prior Network (CPN), and show how to train it to yield a conditional prior. ...
Once the prior is learned in a supervised fashion, one can easily learn the full map to infer optical flow directly from two or more images, without any need for (additional) supervision. ...
However, it provides a base for unsupervised learning of optical flow, and a stage to show the benefit of semi-unsupervised optical flow learning, that utilizes both the conditional prior (CPN) learned ...
doi:10.1007/978-3-030-01267-0_17
fatcat:q4z7topa7vaa3hftp22zyyxalq
Perceptual Loss for Convolutional Neural Network Based Optical Flow Estimation
2017
DEStech Transactions on Computer Science and Engineering
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Motivated by the success in image transformation tasks, a perceptual loss function is used for training the network for optical flow estimation. ...
In this work, rather training feature descriptors via CNNs, an end-to-end fully convolutional network, is developed for solving optical flow from a pair of images. ...
Variational Auto-encoder For optical flow field, there is no label to train a network for classification task. ...
doi:10.12783/dtcse/smce2017/12437
fatcat:6oeq4zzshffjbfrhimeuytf36q
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Thus, in this paper, we propose to exploit the temporal correlation using both first-order optical flow and second-order flow prediction. ...
We suggest an one-stage learning approach to encapsulate flow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors ...
Our unsupervised flow learning does not rely on a well pre-trained optical flow estimation network, such as FlowNet2 (Ilg et al. 2017; Sun et al. 2018) , and can derive the compressed optical flow from ...
doi:10.1609/aaai.v34i07.6825
fatcat:naduixdarnfy3ebtcw55ht2h5e
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration
[article]
2019
arXiv
pre-print
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Thus, in this paper, we propose to exploit the temporal correlation using both first-order optical flow and second-order flow prediction. ...
We suggest an one-stage learning approach to encapsulate flow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors ...
Our unsupervised flow learning does not rely on a well pre-trained optical flow estimation network, such as FlowNet2 (Ilg et al. 2017; Sun et al. 2018) , and can derive the compressed optical flow from ...
arXiv:1912.06348v1
fatcat:h6chbcl52nbwtbpx6hrrzj7fme
Multimodal reconstruction of microvascular-flow distributions using combined two-photon microscopy and Doppler optical coherence tomography
2015
Neurophotonics
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Here, we investigated whether the use of Doppler optical coherence tomography (DOCT) flow measurements in individual vessel segments can help in reconstructing [Formula: see text] across the entire vasculature ...
Computing microvascular cerebral blood flow ([Formula: see text]) in real cortical angiograms is challenging. ...
Acknowledgments We thank Axle Pries and David Kleinfeld for fruitful discussions regarding this work. ...
doi:10.1117/1.nph.2.1.015008
pmid:26157987
pmcid:PMC4478873
fatcat:djgrl6mwffdtjh54ek54hqknji
All One Needs to Know about Priors for Deep Image Restoration and Enhancement: A Survey
[article]
2022
arXiv
pre-print
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Due to its ill-posed property, plenty of works have explored priors to facilitate training deep neural networks (DNNs). ...
Our work covers five primary contents: (1) A theoretical analysis of priors for deep image restoration and enhancement; (2) A hierarchical and structural taxonomy of priors commonly used in the DL-based ...
For example, [17] , [68] use optical flow to guide the DCN or self-attention for VSR, and [69] , [70] , [71] use the optical flow to generate the temporal sharpness prior for video deblurring. ...
arXiv:2206.02070v1
fatcat:icu7hwua3jggbp7owl2l5mgyfu
Optical Flow Estimation for Spiking Camera
[article]
2022
arXiv
pre-print
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Codes and datasets refer to https://github.com/Acnext/Optical-Flow-For-Spiking-Camera. ...
Further, for training SCFlow, we synthesize two sets of optical flow data for the spiking camera, SPIkingly Flying Things and Photo-realistic High-speed Motion, denoted as SPIFT and PHM respectively, corresponding ...
Due to high similarity of motion in adjacent time, the last predicted optical flow is used as a prior motion for the current testing i.e., the prior motion for estimating W i,i+∆t is Ŵi−∆t,i . ...
arXiv:2110.03916v3
fatcat:zulze65yvzg55dlhubfp6suwkm
Using Visual Anomaly Detection for Task Execution Monitoring
[article]
2021
arXiv
pre-print
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A probabilistic U-Net architecture is used to learn to predict optical flow, and the robot's kinematics and 3D model are used to model camera and body motion. ...
We find that modeling camera and body motion, in addition to the learning-based optical flow prediction, results in an improvement of the area under the receiver operating characteristic curve from 0.752 ...
The network predicts a future optical flow image conditioned on a past optical flow image and a latent vector sampled from the distribution output by the posterior network (during training) or the prior ...
arXiv:2107.14206v1
fatcat:cyncui2gjjexfka2rl7uzgehqe
MoNet: Deep Motion Exploitation for Video Object Segmentation
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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Concretely, MoNet exploits computed motion cue (i.e., optical flow) to reinforce the representation of the target frame by aligning and integrating representations from its neighbors. ...
Moreover, MoNet exploits motion inconsistency and transforms such motion cue into foreground/background prior to eliminate distraction from confusing instances and noisy regions. ...
The triple inputs are passed to a segmentation network [4] and an optical flow estimation network [9] , outputting their appearance features and optical flow. ...
doi:10.1109/cvpr.2018.00125
dblp:conf/cvpr/XiaoFLLZ18
fatcat:cn2j6jdzmrajjasc7gl7g77jxi
Consistent depth of moving objects in video
2021
ACM Transactions on Graphics
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2021 pre-print arXiv:2108.01166v1 fatcat:2tmm2nvrhzgq7dqnwrmdgtfduy
By recursively unrolling the scene-flow prediction MLP over varying time steps, we compute both short-range scene flow to impose local smooth motion priors directly in 3D, and long-range scene flow to ...
We formulate this objective in a new test-time training framework where a depth-prediction CNN is trained in tandem with an auxiliary scene-flow prediction MLP over the entire input video. ...
The depth network is first initialized using a data-driven prior (pretrained weights), and then finetuned in tandem with the scene-flow network for a given input video, using a smooth-motion prior and ...
doi:10.1145/3450626.3459871
fatcat:3syvszwnl5cwhbaqkphv63vdca
Dance with Flow: Two-in-One Stream Action Detection
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1904.00696v2 fatcat:u7ofnfw24rgcdn5vyqnhxfjzr4Other Versions
We propose to embed RGB and optical-flow into a single two-in-one stream network with new layers. ...
A motion condition layer extracts motion information from flow images, which is leveraged by the motion modulation layer to generate transformation parameters for modulating the low-level RGB features. ...
Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. ...
arXiv:1904.00696v3
fatcat:vu4tytftizdvdazzdxgbxyne5y
Dance With Flow: Two-In-One Stream Action Detection
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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We propose to embed RGB and optical-flow into a single twoin-one stream network with new layers. ...
A motion condition layer extracts motion information from flow images, which is leveraged by the motion modulation layer to generate transformation parameters for modulating the low-level RGB features. ...
Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. ...
doi:10.1109/cvpr.2019.01017
dblp:conf/cvpr/ZhaoS19
fatcat:dn6dihzvufeshnmt6kdvm46dmq
Unsupervised Domain Adaptation by Optical Flow Augmentation in Semantic Segmentation
[article]
2019
arXiv
pre-print
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Hence, by augmenting images with dense optical flow map, domain adaptation in semantic segmentation can be improved. ...
Solving this can totally eliminate the need for labeling real-life datasets completely. Class balanced self-training is one of the existing techniques that attempt to reduce the domain gap. ...
Augment RGB image with optical flow maps and then use class balanced domain adaptation for domain adaptation.
Optical Flow Generator [7] was created for the sole purpose of generating flow maps. ...
arXiv:1911.09652v1
fatcat:qq5shbwxpvd2neetnvx45x2qmi
Neural Video Compression using Spatio-Temporal Priors
[article]
2019
arXiv
pre-print
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In this work, we propose a neural video compression framework, leveraging the spatial and temporal priors, independently and jointly to exploit the correlations in intra texture, optical flow based temporal ...
Spatial priors are generated using downscaled low-resolution features, while temporal priors (from previous reference frames and residuals) are captured using a convolutional neural network based long-short ...
optical flow encoder and decoder network [18] . ...
arXiv:1902.07383v2
fatcat:brynmcohtzdtdo3nyymhsshubi
Im2Flow: Motion Hallucination from Static Images for Action Recognition
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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First, we devise an encoder-decoder convolutional neural network and a novel optical flow encoding that can translate a static image into an accurate flow map. ...
Second, we show the power of hallucinated flow for recognition, successfully transferring the learned motion into a standard two-stream network for activity recognition. ...
We thank Suyog Jain, Chao-Yeh Chen, Aron Yu, Yu-Chuan Su, Tushar Nagarajan and Zhengpei Yang for helpful input on experiments or reading paper drafts, and also gratefully acknowledge a GPU donation from ...
doi:10.1109/cvpr.2018.00622
dblp:conf/cvpr/GaoXG18
fatcat:ofat4dobw5aj7apx4ikmjymyqe
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