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Fusing Image and Segmentation Cues for Skeleton Extraction in the Wild
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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Extracting skeletons from natural images is a challenging problem, due to complex backgrounds in the scene and various scales of objects. ...
We find that the semantic segmentation probability map is complementary to the corresponding color image and can boost the performance of our baseline model which trained only on color images. ...
To simplify the complex groundtruth fitting problem, we train a model which learns to combine image and segmentation cues for skeleton extraction. ...
doi:10.1109/iccvw.2017.205
dblp:conf/iccvw/LiuLBC17
fatcat:q7wzv6xwvvfwhpjlwk5bi7qghe
DeepFlux for Skeletons in the Wild
[article]
2018
arXiv
pre-print
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Computing object skeletons in natural images is challenging, owing to large variations in object appearance and scale, and the complexity of handling background clutter. ...
We evaluate the proposed method on three benchmark datasets for skeleton detection and two for symmetry detection, achieving consistently superior performance over state-of-the-art methods. ...
[24] develop a two-stream network that combines image and segmentation cues to capture complementary information for skeleton localization. ...
arXiv:1811.12608v1
fatcat:pahjsjqtfbhzbmaawn4yd3ktxq
DeepSkeleton: Skeleton Map for 3D Human Pose Regression
[article]
2017
arXiv
pre-print
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The effectiveness of our approach is validated on challenging in-the-wild dataset MPII and indoor dataset Human3.6M. ...
For the first time, we show that training regression network from skeleton map alone is capable of meeting the performance of state-of-theart 3D human pose estimation works. ...
The network structure shown in Figure 2 starts with a 224×224 image and extracts features along the downsampling process. Herein only res2c, res3d, res4f and res5c are sketched for brevity. ...
arXiv:1711.10796v1
fatcat:evdz5vymj5g4dpwfocdwhufm2i
Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases
[article]
2020
arXiv
pre-print
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A graph neural network (GNN) for image understanding based on multiple cues is proposed in this paper. ...
Two image understanding tasks, namely group-level emotion recognition (GER) and event recognition, which are highly semantic and require the interaction of several deep models to synthesize multiple cues ...
Acknowledgements The work is supported by the National Science Foundation under Grant No. 1319598. ...
arXiv:1909.12911v2
fatcat:qpaxwqgmwjhc3nqwah7bxtvd2y
Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases
2020
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
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A graph neural network (GNN) for image understanding based on multiple cues is proposed in this paper. ...
Two image understanding tasks, namely group-level emotion recognition (GER) and event recognition, which are highly semantic and require the interaction of several deep models to synthesize multiple cues ...
OpenPose [6, 42, 51] is used to extract skeleton images in the same way as described in [20, 22] . The SE-ResNet-50 is fine-tuned on skeleton images in the same way as described in [22] . ...
doi:10.1109/wacv45572.2020.9093547
dblp:conf/wacv/GuoPZBB20
fatcat:lzz7pps6fvhgjatqe4d76kjcae
SRN: Side-Output Residual Network for Object Symmetry Detection in the Wild
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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symmetry detection in the wild. ...
SK506 Figure 1: We propose a new benchmark, named Sym-PASCAL, for object symmetry detection in the wild. ...
Acknowledgement This work is supported in partial by the NSFC under ...
doi:10.1109/cvpr.2017.40
dblp:conf/cvpr/KeCJZY17
fatcat:e7upwfjwmbe73cqlvsthw26jhe
SRN: Side-output Residual Network for Object Symmetry Detection in the Wild
[article]
2017
arXiv
pre-print
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symmetry detection in the wild. ...
In this paper, we establish a baseline for object symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for ...
Acknowledgement This work is partially supported by NSFC under Grant 61671427, Beijing Municipal Science and Technology Commission under Grant Z161100001616005, and Science and Technology Innovation Foundation ...
arXiv:1703.02243v2
fatcat:qgrtemx3a5hdrbyr6e7v64hqhq
RSRN: Rich Side-Output Residual Network for Medial Axis Detection
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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In this paper, we propose a Rich Side-output Residual Network (RSRN) for medial axis detection for the ICCV 2017 workshop challenge on detecting symmetry in the wild. ...
RSRN uses the rich features of fully convolutional network by hierarchically fusing side-outputs in a deep-toshallow manner to decrease the residual between the detection result and the ground-truth, which ...
Acknowledgement This work is partially supported by NSFC under Grant 61671427 and Grant 61771447. ...
doi:10.1109/iccvw.2017.204
dblp:conf/iccvw/LiuKJY17
fatcat:jbi4d2wz7fawzpzhhnsw3iv4xa
Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild
[article]
2017
arXiv
pre-print
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Motivated by a new study demonstrating the extremely high inter-person accuracy of human perceived symmetries in the wild, we have constructed the first deep-learning neural network for reflection and ...
We employ novel methods to convert discrete human labels into symmetry heatmaps, capture symmetry densely in an image and quantitatively evaluate Sym-NET against multiple existing computer vision algorithms ...
[59] use a deep CNN to learn symmetry at multiple scales and fuse the final output together. The network needs object skeleton ground-truth for the particular scale of the objects. ...
arXiv:1704.03568v2
fatcat:hzg7p2d6sjfsdb5nutrdf4rfmy
SRN: Side-output Residual Network for Object Reflection Symmetry Detection and Beyond
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1807.06621v1 fatcat:vbhytsvzbjagvbexno77fa66te
for symmetry detection in the wild. ...
In this paper, we establish a baseline for object reflection symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction ...
ACKNOWLEDGMENTS The authors would like to express their sincere appreciation to Prof. David Doermann and Dr. Pengxu Wei for their comments and suggestions. ...
arXiv:1807.06621v2
fatcat:qhrzqziwyrhg3p64wifyhbtt6u
RGB-D-based Human Motion Recognition with Deep Learning: A Survey
[article]
2018
arXiv
pre-print
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In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. ...
The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. ...
[151] took a different approach and adopted scene flow to extract features that fused the RGB and depth from the onset. ...
arXiv:1711.08362v2
fatcat:cugugpqeffcshnwwto4z2aw4ti
Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation
[article]
2021
arXiv
pre-print
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Motivated by this insight, we propose DIGIT, a novel method for estimating the 3D poses of two interacting hands from a single monocular image. ...
In contrast to prior work, we do not decouple the segmentation from the pose estimation stage, but rather leverage the per-pixel probabilities directly in the downstream pose estimation task. ...
We thank Korrawe Karunratanakul, Emre Aksan, and Dimitrios Tzionas for their feedback. Disclosure. MJB has received research gift funds from Adobe, Intel, Nvidia, Facebook, and Amazon. ...
arXiv:2107.00434v2
fatcat:frrzchccbja4pclwkh4ebhposi
Characterness: An Indicator of Text in the Wild
2014
IEEE Transactions on Image Processing
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In order to measure the characterness we develop three novel cues that are tailored for character detection, and a Bayesian method for their integration. ...
Text in an image provides vital information for interpreting its contents, and text in a scene can aide with a variety of tasks from navigation, to obstacle avoidance, and odometry. ...
ACKNOWLEDGMENT This work is in part supported by ARC Grants FT120100969 and LP130100156; the UTS FEIT Industry and Innovation Project Scheme. ...
doi:10.1109/tip.2014.2302896
pmid:24808338
fatcat:lonk5aciqzf3tjb72fjb6p2c6u
Characterness: An Indicator of Text in the Wild
[article]
2013
arXiv
pre-print
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In order to measure the characterness we develop three novel cues that are tailored for character detection, and a Bayesian method for their integration. ...
Text in an image provides vital information for interpreting its contents, and text in a scene can aide with a variety of tasks from navigation, to obstacle avoidance, and odometry. ...
ACKNOWLEDGMENT This work is in part supported by ARC Grants FT120100969 and LP130100156; the UTS FEIT Industry and Innovation Project Scheme. ...
arXiv:1309.6691v1
fatcat:dktsea3psvhvxayjnh247w74ku
Unsupervised 3D Pose Estimation with Geometric Self-Supervision
[article]
2019
arXiv
pre-print
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Additionally, to learn from 2D poses "in the wild", we train an unsupervised 2D domain adapter network to allow for an expansion of 2D data. ...
By lifting the synthetic 2D poses back to 3D and re-projecting them in the original camera view, we can define self-consistency loss both in 3D and in 2D. ...
Exemplar based methods [5, 19, 51] use a database/dictionary of 3D skeletons for nearest-neighbor look-up. Tekin et al. [42] fused 2D and 3D image cues re-lying on 2D-3D correspondences. ...
arXiv:1904.04812v1
fatcat:4scdgh64ffavzcevewwmvo3he4
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