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Markov processes
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convolution, feature extraction, feedforward neural nets,
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Visual attribute
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IEEE DOI
1805
Analytical models, Computational complexity,
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weak prediction
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IEEE DOI
1905
convolutional neural nets, Internet,
learning (artificial intelligence), object detection,
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VCIP15(1-4)
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1605
Estimation
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Salient object detection, Webly-supervised learning, Deep learning
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IEEE DOI
2104
Object detection, Annotations, Detectors, Training, Proposals,
Computer science, Task analysis, Saliency bounding boxes,
weak supervision
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2112
Saliency detection, Task analysis, Object detection,
Feature extraction, Training, Image segmentation, Annotations,
object subitizing
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Liang, Z.J.[Zi-Jian],
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IP(31), 2022, pp. 6295-6305.
IEEE DOI
2210
Light fields, Saliency detection, Object detection, Annotations,
Visualization, Image color analysis, Feature extraction, weak supervision
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CVPR16(5723-5732)
IEEE DOI
1612
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ECCV14(III: 110-124).
Springer DOI
1408
Only closer (foreground or salient) objects are well lit by the flash.
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Zhou, Z.H.[Zhi-Heng],
Guo, Y.F.[Yong-Fan],
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ICCV21(2856-2865)
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2203
Training, Location awareness, Codes, Computational modeling,
Object detection, Classification algorithms,
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Salient Object Detection Based on Progressively Supervised Learning
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2111
Annotations, Training, Supervised learning, Remote sensing,
Object detection, Image segmentation, Feature extraction,
weakly supervised learning (WSL)
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2209
Salient object detection, Weakly supervised learning,
Edge fusion module, Feature correlation module,
Self-supervised salient detection loss
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Yang, J.[Jie],
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Learning deep feature correspondence for unsupervised anomaly
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2209
Anomaly detection, Anomaly segmentation,
Feature correspondence, Dual network
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Xu, Y.[Yong],
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IEEE DOI
2301
Proposals, Training, Object detection, Loss measurement, Detectors,
Feature extraction, Phase measurement,
high-quality supervision
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Wang, Y.F.[Yi-Fan],
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Wang, L.J.[Li-Jun],
Liu, T.[Ting],
Lu, H.C.[Hu-Chuan],
Multi-Source Uncertainty Mining for Deep Unsupervised Saliency
Detection,
CVPR22(11717-11726)
IEEE DOI
2210
Adaptation models, Uncertainty, Computer network reliability,
Training data, Object detection, Reliability engineering,
Self- semi- meta- unsupervised learning
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Shin, G.[Gyungin],
Albanie, S.[Samuel],
Xie, W.[Weidi],
Unsupervised Salient Object Detection with Spectral Cluster Voting,
L3D-IVU22(3970-3979)
IEEE DOI
2210
Conferences, Object detection, Object segmentation, Detectors,
Benchmark testing, Feature extraction, Pattern recognition
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Ding, S.H.[Shou-Hong],
Song, M.[Mofei],
Disentangled High Quality Salient Object Detection,
ICCV21(3560-3570)
IEEE DOI
2203
Training, Deep learning, Uncertainty, Semantics, Refining,
Graphics processing units,
Transfer/Low-shot/Semi/Unsupervised Learning
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Ouyang, S.X.[Sheng-Xiong],
Wang, X.L.[Xing-Lu],
Lyu, K.[Kejie],
Li, Y.M.[Ying-Ming],
Pseudo-Label Generation-Evaluation Framework for Cross Domain Weakly
Supervised Object Detection,
ICIP21(724-728)
IEEE DOI
2201
Annotations, Image processing, Detectors, Object detection,
Task analysis, Cross domain, Weakly supervised, Object detection, Evaluator
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Kosugi, S.,
Yamasaki, T.,
Aizawa, K.,
Object-Aware Instance Labeling for Weakly Supervised Object Detection,
ICCV19(6063-6071)
IEEE DOI
2004
image annotation, image classification, iterative methods,
learning (artificial intelligence), object detection, Focusing
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Inoue, N.,
Furuta, R.,
Yamasaki, T.,
Aizawa, K.,
Cross-Domain Weakly-Supervised Object Detection Through Progressive
Domain Adaptation,
CVPR18(5001-5009)
IEEE DOI
1812
Object detection, Task analysis, Detectors, Dogs, Search engines,
Feature extraction, Noise measurement
BibRef
Zhang, D.,
Han, J.,
Zhang, Y.,
Supervision by Fusion: Towards Unsupervised Learning of Deep Salient
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ICCV17(4068-4076)
IEEE DOI
1802
convolution, image fusion, neural nets, object detection,
unsupervised learning, DNNs, deep convolutional models,
Unsupervised learning
BibRef
López-Sastre, R.J.[Roberto J.],
Unsupervised Robust Feature-Based Partition Ensembling to Discover
Categories,
Robust16(1187-1195)
IEEE DOI
1612
Different features to aggregate different partitions to get objects.
BibRef
Cholakkal, H.[Hisham],
Johnson, J.[Jubin],
Rajan, D.[Deepu],
Backtracking ScSPM Image Classifier for Weakly Supervised Top-Down
Saliency,
CVPR16(5278-5287)
IEEE DOI
1612
BibRef
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Color, Multispectral, RGB, for Salient Regions .