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GOGGLES: Automatic Image Labeling with Affinity Coding
[article]
2020
arXiv
pre-print
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We propose affinity coding, a new domain-agnostic paradigm for automated training data labeling. ...
We compare GOGGLES with existing data programming systems on 5 image labeling tasks from diverse domains. ...
We also use the probabilistic labels generated by Snorkel, Snuba and GOGGLES to train downstream discriminative models following the similar approach taken in [19, 29] . ...
arXiv:1903.04552v2
fatcat:jsd7vl6xureh5ner3snid7kb4y
Icon scanning: Towards next generation QR codes
2012
2012 IEEE Conference on Computer Vision and Pattern Recognition
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Such a solution exists today for QR codes, which can be thought of as icons with a binary pattern. ...
In addition, our system should further deal with the challenges introduced by taking pictures of a screen. ...
First, for each original icon in our training set we generate K blurred versions using the gaussian kernels obtained in the previous stage. ...
doi:10.1109/cvpr.2012.6247793
dblp:conf/cvpr/FriedmanZ12
fatcat:lvumzvj3vvcprjstomz3afej6q
Training Deep Neural Networks to Detect Repeatable 2D Features Using Large Amounts of 3D World Capture Data
[article]
2019
arXiv
pre-print
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We further present an algorithm for automatically generating labels of repeatable 2D features, and present a fast, easy to use test algorithm for evaluating a detector in an 3D environment. ...
To this end, we generate labeled 2D images from a photo-realistic 3D dataset. These images are used for training a neural network based feature detector. ...
Proposed Technique
Training Set Generation To generate a large amount of 3D data for training our network, we utilize the Gibson simulator, which renders photo realistic viewpoints of scenes captured ...
arXiv:1912.04384v1
fatcat:dkvekwqhwneq7j77xu45mnbf4q
Intelligent Splicing Method of Virtual Reality Lingnan Cultural Heritage Panorama Based on Automatic Machine Learning
2021
Mobile Information Systems
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We use automatic machine learning models to train the visual feature set and use the bagging method to generate different training subsets. ...
With the increasing expansion of virtual reality application fields and the complexity of application content, the demand for real-time rendering of realistic graphics has increased sharply. ...
Use automatic machine learning models to train the visual feature sets and use the bagging method to generate different training subsets. ...
doi:10.1155/2021/8693436
fatcat:ylb6r6qewnbgpjvelj5qroooqq
Image search—from thousands to billions in 20 years
2013
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Starting with a retrospective review of three stages of image search in the history, the article highlights major breakthroughs around the year 2000 in image search features, indexing methods, and commercial ...
ACKNOWLEDGMENTS The authors gratefully acknowledge Wei-Ying Ma for his visionary long-term support and encouragement, and Xin-Jing Wang, Changhu Wang, Xirong Li, and Zhiwei Li for their years of collaboration with ...
The progress is particularly promising due to the help of large-scale training data. ...
doi:10.1145/2490823
fatcat:cor23f3c7nb7fimy4ixp32bdk4
Smooth object retrieval using a bag of boundaries
2011
2011 International Conference on Computer Vision
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We introduce a new dataset of 6K images containing sculptures by Moore and Rodin, and annotated with ground truth for the occurrence of twenty 3D sculptures. ...
There have been several large scale demonstrations [11, 21, 22] with Google Goggles as a commercial application. ...
We use the method and code from [4] which generates a hierarchy of regions based on the output of the gPb contour detector [15] This provides a partition of the image into a set of closed regions for ...
doi:10.1109/iccv.2011.6126265
dblp:conf/iccv/ArandjelovicZ11
fatcat:zpc6twt5fndcbk6cgqtcxvhuwu
Deep learning hashing for mobile visual search
2017
EURASIP Journal on Image and Video Processing
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Firstly, we present a comprehensive survey of the existed deep learning based hashing methods, which showcases their remarkable power of automatic learning highly robust and compact binary code representation ...
The proliferation of mobile devices is producing a new wave of mobile visual search applications that enable users to sense their surroundings with smart phones. ...
generates less effective hash codes. ...
doi:10.1186/s13640-017-0167-4
fatcat:vcdhjjbe6jai7hyigxstihcega
Supervising the Transfer of Reasoning Patterns in VQA
[article]
2021
arXiv
pre-print
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Methods for Visual Question Anwering (VQA) are notorious for leveraging dataset biases rather than performing reasoning, hindering generalization. ...
This provides evidence that deep neural networks can learn to reason when training conditions are favorable enough. ...
GQA is a dataset with question-answer pairs automatically generated from real images, and is particularly well suited for evaluating a large variety of reasoning skills. ...
arXiv:2106.05597v1
fatcat:naevbagtvvgy3ppxyyllanudia
IE-Vnet: Deep Learning-Based Segmentation of the Inner Ear's Total Fluid Space
2022
Frontiers in Neurology
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Code and pre-trained models are available free and open-source under https://github.com/pydsgz/IEVNet. ...
Its output works seamlessly with a previously published open-source pipeline for automatic ELS segmentation. ...
The dataset D1 was split into 90% training data (N = 161 subjects, 322 inner ears) and 10% validation data (N = 18 subjects, 36 inner ears). ...
doi:10.3389/fneur.2022.663200
pmid:35645963
pmcid:PMC9130477
fatcat:xp26uysrwfau7okpiswcsrnf64
On-the-fly learning for visual search of large-scale image and video datasets
2015
International Journal of Multimedia Information Retrieval
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The paradigm we explore is constructing visual models for such semantic entities on-the-fly, i.e. at run time, by using an image search engine to source visual training data for the text query. ...
We describe three classes of queries, each with its associated visual search method: object instances (using a bag of visual words approach for matching); object categories (using a discriminative classifier ...
Along with the fixed pool of pre-computed negative training data, these are used to train a linear SVM w, φ(I ) by fitting w to the available training data by minimizing an objective function balancing ...
doi:10.1007/s13735-015-0077-0
pmid:26191469
pmcid:PMC4498639
fatcat:prpxk47u4bdzxkhe5nfrssshpa
Comparative Study of Trust Modeling for Automatic Landmark Tagging
2013
IEEE Transactions on Information Forensics and Security
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We compare this socially-driven approach with other user trust models via experiments and subjective testing on an image database of various famous landmarks. ...
He also worked as a radio access network conceptual planning expert in Vip mobile, Serbia, focusing on the implementation of second-and third-generation radio access technologies. ...
In a real-life scenario, an image with unknown landmark will be automatically tagged with either one geotag or none, depending on the level of similarity with the known (trained) landmarks. ...
doi:10.1109/tifs.2013.2242889
fatcat:7a5lrf4kzbeqnjgmgizaeulgaq
Unbiased look at dataset bias
2011
CVPR 2011
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They have been the chief reason for the considerable progress in the field, not just as source of large amounts of training data, but also as means of measuring and comparing performance of competing algorithms ...
We present a comparison study using a set of popular datasets, evaluated based on a number of criteria including: relative data bias, cross-dataset generalization, effects of closed-world assumption, and ...
This work is part of a larger effort, joint with David Forsyth and Jay Yagnik, on understanding the benefits and pitfalls of using large data in vision. ...
doi:10.1109/cvpr.2011.5995347
dblp:conf/cvpr/TorralbaE11
fatcat:bm2of7hygjcgfck7vt6qzbsdz4
Visual Object Recognition
2011
Synthesis Lectures on Artificial Intelligence and Machine Learning
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We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. ...
connected constellations; pyramid match kernels; detection via sliding windows; Hough voting; Generalized distance transform; the Implicit Shape Model; the Deformable Part-based Model vii Contents ...
Both the part appearances and their location distributions are learned automatically from training data. ...
doi:10.2200/s00332ed1v01y201103aim011
fatcat:fhz7aokkfjav7fuauuorfstq4y
Spatially-Constrained Similarity Measurefor Large-Scale Object Retrieval
2014
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Furthermore, based on the retrieval and localization results of SCSM, we introduce a novel and robust re-ranking method with the k-nearest neighbors of the query for automatically refining the initial ...
One fundamental problem in object retrieval with the bag-of-words model is its lack of spatial information. ...
seconds without code optimization. ...
doi:10.1109/tpami.2013.237
pmid:26353283
fatcat:gup7cqh4lfd4jgtvo76mqh6ewi
Self-reported empathy and neural activity during action imitation and observation in schizophrenia
2014
NeuroImage: Clinical
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These findings suggest that patients show a disjunction between automatic neural responses to low level social cues and higher level, integrative social cognitive processes involved in self-perceived empathy ...
This study investigated neural activity during imitation and observation of finger movements and facial expressions in schizophrenia, and their correlates with self-reported empathy. ...
The authors wish to thank Amanda Bender, Michelle Dolinsky, Crystal Gibson, Cory Tripp, and Katherine Weiner for their assistance in data collection. ...
doi:10.1016/j.nicl.2014.06.006
pmid:25009771
pmcid:PMC4087183
fatcat:3qwue6sojbdrtio7j4ra6lnyvq
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