47 research outputs found
Seeing Behind the Camera: Identifying the Authorship of a Photograph
We introduce the novel problem of identifying the photographer behind a
photograph. To explore the feasibility of current computer vision techniques to
address this problem, we created a new dataset of over 180,000 images taken by
41 well-known photographers. Using this dataset, we examined the effectiveness
of a variety of features (low and high-level, including CNN features) at
identifying the photographer. We also trained a new deep convolutional neural
network for this task. Our results show that high-level features greatly
outperform low-level features. We provide qualitative results using these
learned models that give insight into our method's ability to distinguish
between photographers, and allow us to draw interesting conclusions about what
specific photographers shoot. We also demonstrate two applications of our
method.Comment: Dataset downloadable at http://www.cs.pitt.edu/~chris/photographer To
Appear in CVPR 201
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Efficiently identifying images, videos, songs or documents most relevant to the user using binary search trees on attributes for guiding relevance feedback
A method, system and computer program product for efficiently identifying images, videos, audio files or documents relevant to a user using binary search trees in attribute space for guiding relevance feedback. A binary tree is constructed for each relative attribute of interest. A “pivot exemplar” (at a node of the binary tree) is set for each relative attribute's binary tree as corresponding to the database image, video, audio file or document with a median relative attribute value among that subtree's child examples. A pivot exemplar out of the available current pivot exemplars that has the highest expected information gain is selected to be provided to the user. Comparative attribute feedback is then received from the user regarding whether a degree of the attribute in the user's target image, video, audio file or document is more, less or equal with the attribute displayed in the selected pivot exemplar.Board of Regents, University of Texas Syste
Asking Friendly Strangers: Non-Semantic Attribute Transfer
International audienc
VEIL: Vetting Extracted Image Labels from In-the-Wild Captions for Weakly-Supervised Object Detection
The use of large-scale vision-language datasets is limited for object
detection due to the negative impact of label noise on localization. Prior
methods have shown how such large-scale datasets can be used for pretraining,
which can provide initial signal for localization, but is insufficient without
clean bounding-box data for at least some categories. We propose a technique to
"vet" labels extracted from noisy captions, and use them for weakly-supervised
object detection (WSOD). We conduct analysis of the types of label noise in
captions, and train a classifier that predicts if an extracted label is
actually present in the image or not. Our classifier generalizes across dataset
boundaries and across categories. We compare the classifier to eleven baselines
on five datasets, and demonstrate that it can improve WSOD without label
vetting by 30% (31.2 to 40.5 mAP when evaluated on PASCAL VOC