480 research outputs found
End-to-end weakly-supervised semantic alignment
We tackle the task of semantic alignment where the goal is to compute dense
semantic correspondence aligning two images depicting objects of the same
category. This is a challenging task due to large intra-class variation,
changes in viewpoint and background clutter. We present the following three
principal contributions. First, we develop a convolutional neural network
architecture for semantic alignment that is trainable in an end-to-end manner
from weak image-level supervision in the form of matching image pairs. The
outcome is that parameters are learnt from rich appearance variation present in
different but semantically related images without the need for tedious manual
annotation of correspondences at training time. Second, the main component of
this architecture is a differentiable soft inlier scoring module, inspired by
the RANSAC inlier scoring procedure, that computes the quality of the alignment
based on only geometrically consistent correspondences thereby reducing the
effect of background clutter. Third, we demonstrate that the proposed approach
achieves state-of-the-art performance on multiple standard benchmarks for
semantic alignment.Comment: In 2018 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR 2018
Taking the bite out of automated naming of characters in TV video
We investigate the problem of automatically labelling appearances of characters in TV or film material
with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying
when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classifier which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series ââBuffy the Vampire Slayerâ
"'Who are you?' - Learning person specific classifiers from video"
We investigate the problem of automatically labelling
faces of characters in TV or movie material with their
names, using only weak supervision from automaticallyaligned
subtitle and script text. Our previous work (Everingham
et al. [8]) demonstrated promising results on the
task, but the coverage of the method (proportion of video
labelled) and generalization was limited by a restriction to
frontal faces and nearest neighbour classification.
In this paper we build on that method, extending the coverage
greatly by the detection and recognition of characters
in profile views. In addition, we make the following contributions:
(i) seamless tracking, integration and recognition
of profile and frontal detections, and (ii) a character specific
multiple kernel classifier which is able to learn the features
best able to discriminate between the characters.
We report results on seven episodes of the TV series
âBuffy the Vampire Slayerâ, demonstrating significantly increased
coverage and performance with respect to previous
methods on this material
Convolutional neural network architecture for geometric matching
We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline
transformation, and estimating its parameters. The contributions of this work
are three-fold. First, we propose a convolutional neural network architecture
for geometric matching. The architecture is based on three main components that
mimic the standard steps of feature extraction, matching and simultaneous
inlier detection and model parameter estimation, while being trainable
end-to-end. Second, we demonstrate that the network parameters can be trained
from synthetically generated imagery without the need for manual annotation and
that our matching layer significantly increases generalization capabilities to
never seen before images. Finally, we show that the same model can perform both
instance-level and category-level matching giving state-of-the-art results on
the challenging Proposal Flow dataset.Comment: In 2017 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR 2017
Antitrust Standing of Target Corporations to Enjoin Hostile Takeovers Under Section 16 of the Clayton Act
Joint understanding of video and language is an active research area with many applications. Prior work in this domain typically relies on learning text-video embeddings. One difficulty with this approach, however, is the lack of large-scale annotated video-caption datasets for training. To address this issue, we aim at learning text-video embeddings from heterogeneous data sources. To this end, we propose a Mixture-of-Embedding-Experts (MEE) model with ability to handle missing input modalities during training. As a result, our framework can learn improved text-video embeddings simultaneously from image and video datasets. We also show the generalization of MEE to other input modalities such as face descriptors. We evaluate our method on the task of video retrieval and report results for the MPII Movie Description and MSR-VTT datasets. The proposed MEE model demonstrates significant improvements and outperforms previously reported methods on both text-to-video and video-to-text retrieval tasks
Weakly-supervised learning of visual relations
This paper introduces a novel approach for modeling visual relations between
pairs of objects. We call relation a triplet of the form (subject, predicate,
object) where the predicate is typically a preposition (eg. 'under', 'in front
of') or a verb ('hold', 'ride') that links a pair of objects (subject, object).
Learning such relations is challenging as the objects have different spatial
configurations and appearances depending on the relation in which they occur.
Another major challenge comes from the difficulty to get annotations,
especially at box-level, for all possible triplets, which makes both learning
and evaluation difficult. The contributions of this paper are threefold. First,
we design strong yet flexible visual features that encode the appearance and
spatial configuration for pairs of objects. Second, we propose a
weakly-supervised discriminative clustering model to learn relations from
image-level labels only. Third we introduce a new challenging dataset of
unusual relations (UnRel) together with an exhaustive annotation, that enables
accurate evaluation of visual relation retrieval. We show experimentally that
our model results in state-of-the-art results on the visual relationship
dataset significantly improving performance on previously unseen relations
(zero-shot learning), and confirm this observation on our newly introduced
UnRel dataset
Occlusion resistant learning of intuitive physics from videos
To reach human performance on complex tasks, a key ability for artificial
systems is to understand physical interactions between objects, and predict
future outcomes of a situation. This ability, often referred to as intuitive
physics, has recently received attention and several methods were proposed to
learn these physical rules from video sequences. Yet, most of these methods are
restricted to the case where no, or only limited, occlusions occur. In this
work we propose a probabilistic formulation of learning intuitive physics in 3D
scenes with significant inter-object occlusions. In our formulation, object
positions are modeled as latent variables enabling the reconstruction of the
scene. We then propose a series of approximations that make this problem
tractable. Object proposals are linked across frames using a combination of a
recurrent interaction network, modeling the physics in object space, and a
compositional renderer, modeling the way in which objects project onto pixel
space. We demonstrate significant improvements over state-of-the-art in the
intuitive physics benchmark of IntPhys. We apply our method to a second dataset
with increasing levels of occlusions, showing it realistically predicts
segmentation masks up to 30 frames in the future. Finally, we also show results
on predicting motion of objects in real videos
Weakly-supervised learning of visual relations
This paper introduces a novel approach for modeling visual relations between
pairs of objects. We call relation a triplet of the form (subject, predicate,
object) where the predicate is typically a preposition (eg. 'under', 'in front
of') or a verb ('hold', 'ride') that links a pair of objects (subject, object).
Learning such relations is challenging as the objects have different spatial
configurations and appearances depending on the relation in which they occur.
Another major challenge comes from the difficulty to get annotations,
especially at box-level, for all possible triplets, which makes both learning
and evaluation difficult. The contributions of this paper are threefold. First,
we design strong yet flexible visual features that encode the appearance and
spatial configuration for pairs of objects. Second, we propose a
weakly-supervised discriminative clustering model to learn relations from
image-level labels only. Third we introduce a new challenging dataset of
unusual relations (UnRel) together with an exhaustive annotation, that enables
accurate evaluation of visual relation retrieval. We show experimentally that
our model results in state-of-the-art results on the visual relationship
dataset significantly improving performance on previously unseen relations
(zero-shot learning), and confirm this observation on our newly introduced
UnRel dataset
On Pairwise Costs for Network Flow Multi-Object Tracking
Multi-object tracking has been recently approached with the min-cost network
flow optimization techniques. Such methods simultaneously resolve multiple
object tracks in a video and enable modeling of dependencies among tracks.
Min-cost network flow methods also fit well within the "tracking-by-detection"
paradigm where object trajectories are obtained by connecting per-frame outputs
of an object detector. Object detectors, however, often fail due to occlusions
and clutter in the video. To cope with such situations, we propose to add
pairwise costs to the min-cost network flow framework. While integer solutions
to such a problem become NP-hard, we design a convex relaxation solution with
an efficient rounding heuristic which empirically gives certificates of small
suboptimality. We evaluate two particular types of pairwise costs and
demonstrate improvements over recent tracking methods in real-world video
sequences
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