8,719 research outputs found
Detecting Visual Relationships with Deep Relational Networks
Relationships among objects play a crucial role in image understanding.
Despite the great success of deep learning techniques in recognizing individual
objects, reasoning about the relationships among objects remains a challenging
task. Previous methods often treat this as a classification problem,
considering each type of relationship (e.g. "ride") or each distinct visual
phrase (e.g. "person-ride-horse") as a category. Such approaches are faced with
significant difficulties caused by the high diversity of visual appearance for
each kind of relationships or the large number of distinct visual phrases. We
propose an integrated framework to tackle this problem. At the heart of this
framework is the Deep Relational Network, a novel formulation designed
specifically for exploiting the statistical dependencies between objects and
their relationships. On two large datasets, the proposed method achieves
substantial improvement over state-of-the-art.Comment: To be appeared in CVPR 2017 as an oral pape
Object Referring in Videos with Language and Human Gaze
We investigate the problem of object referring (OR) i.e. to localize a target
object in a visual scene coming with a language description. Humans perceive
the world more as continued video snippets than as static images, and describe
objects not only by their appearance, but also by their spatio-temporal context
and motion features. Humans also gaze at the object when they issue a referring
expression. Existing works for OR mostly focus on static images only, which
fall short in providing many such cues. This paper addresses OR in videos with
language and human gaze. To that end, we present a new video dataset for OR,
with 30, 000 objects over 5, 000 stereo video sequences annotated for their
descriptions and gaze. We further propose a novel network model for OR in
videos, by integrating appearance, motion, gaze, and spatio-temporal context
into one network. Experimental results show that our method effectively
utilizes motion cues, human gaze, and spatio-temporal context. Our method
outperforms previousOR methods. For dataset and code, please refer
https://people.ee.ethz.ch/~arunv/ORGaze.html.Comment: Accepted to CVPR 2018, 10 pages, 6 figure
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
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
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