845 research outputs found
Neighbourhood Consensus Networks
We address the problem of finding reliable dense correspondences between a
pair of images. This is a challenging task due to strong appearance differences
between the corresponding scene elements and ambiguities generated by
repetitive patterns. The contributions of this work are threefold. First,
inspired by the classic idea of disambiguating feature matches using semi-local
constraints, we develop an end-to-end trainable convolutional neural network
architecture that identifies sets of spatially consistent matches by analyzing
neighbourhood consensus patterns in the 4D space of all possible
correspondences between a pair of images without the need for a global
geometric model. Second, we demonstrate that the model can be trained
effectively from weak supervision in the form of matching and non-matching
image pairs without the need for costly manual annotation of point to point
correspondences. Third, we show the proposed neighbourhood consensus network
can be applied to a range of matching tasks including both category- and
instance-level matching, obtaining the state-of-the-art results on the PF
Pascal dataset and the InLoc indoor visual localization benchmark.Comment: In Proceedings of the 32nd Conference on Neural Information
Processing Systems (NeurIPS 2018
A CNN-RNN Framework for Image Annotation from Visual Cues and Social Network Metadata
Images represent a commonly used form of visual communication among people.
Nevertheless, image classification may be a challenging task when dealing with
unclear or non-common images needing more context to be correctly annotated.
Metadata accompanying images on social-media represent an ideal source of
additional information for retrieving proper neighborhoods easing image
annotation task. To this end, we blend visual features extracted from neighbors
and their metadata to jointly leverage context and visual cues. Our models use
multiple semantic embeddings to achieve the dual objective of being robust to
vocabulary changes between train and test sets and decoupling the architecture
from the low-level metadata representation. Convolutional and recurrent neural
networks (CNNs-RNNs) are jointly adopted to infer similarity among neighbors
and query images. We perform comprehensive experiments on the NUS-WIDE dataset
showing that our models outperform state-of-the-art architectures based on
images and metadata, and decrease both sensory and semantic gaps to better
annotate images
Love Thy Neighbors: Image Annotation by Exploiting Image Metadata
Some images that are difficult to recognize on their own may become more
clear in the context of a neighborhood of related images with similar
social-network metadata. We build on this intuition to improve multilabel image
annotation. Our model uses image metadata nonparametrically to generate
neighborhoods of related images using Jaccard similarities, then uses a deep
neural network to blend visual information from the image and its neighbors.
Prior work typically models image metadata parametrically, in contrast, our
nonparametric treatment allows our model to perform well even when the
vocabulary of metadata changes between training and testing. We perform
comprehensive experiments on the NUS-WIDE dataset, where we show that our model
outperforms state-of-the-art methods for multilabel image annotation even when
our model is forced to generalize to new types of metadata.Comment: Accepted to ICCV 201
Discovering visual concept structure with sparse and incomplete tags
This work was partially supported by the China Scholarship Council, Vision Semantics Limited, and Royal Society Newton Advanced Fellowship Programme (NA150459)
A Data-Driven Approach for Tag Refinement and Localization in Web Videos
Tagging of visual content is becoming more and more widespread as web-based
services and social networks have popularized tagging functionalities among
their users. These user-generated tags are used to ease browsing and
exploration of media collections, e.g. using tag clouds, or to retrieve
multimedia content. However, not all media are equally tagged by users. Using
the current systems is easy to tag a single photo, and even tagging a part of a
photo, like a face, has become common in sites like Flickr and Facebook. On the
other hand, tagging a video sequence is more complicated and time consuming, so
that users just tag the overall content of a video. In this paper we present a
method for automatic video annotation that increases the number of tags
originally provided by users, and localizes them temporally, associating tags
to keyframes. Our approach exploits collective knowledge embedded in
user-generated tags and web sources, and visual similarity of keyframes and
images uploaded to social sites like YouTube and Flickr, as well as web sources
like Google and Bing. Given a keyframe, our method is able to select on the fly
from these visual sources the training exemplars that should be the most
relevant for this test sample, and proceeds to transfer labels across similar
images. Compared to existing video tagging approaches that require training
classifiers for each tag, our system has few parameters, is easy to implement
and can deal with an open vocabulary scenario. We demonstrate the approach on
tag refinement and localization on DUT-WEBV, a large dataset of web videos, and
show state-of-the-art results.Comment: Preprint submitted to Computer Vision and Image Understanding (CVIU
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