6,337 research outputs found
No Spare Parts: Sharing Part Detectors for Image Categorization
This work aims for image categorization using a representation of distinctive
parts. Different from existing part-based work, we argue that parts are
naturally shared between image categories and should be modeled as such. We
motivate our approach with a quantitative and qualitative analysis by
backtracking where selected parts come from. Our analysis shows that in
addition to the category parts defining the class, the parts coming from the
background context and parts from other image categories improve categorization
performance. Part selection should not be done separately for each category,
but instead be shared and optimized over all categories. To incorporate part
sharing between categories, we present an algorithm based on AdaBoost to
jointly optimize part sharing and selection, as well as fusion with the global
image representation. We achieve results competitive to the state-of-the-art on
object, scene, and action categories, further improving over deep convolutional
neural networks
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
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Every moment counts in action recognition. A comprehensive understanding of
human activity in video requires labeling every frame according to the actions
occurring, placing multiple labels densely over a video sequence. To study this
problem we extend the existing THUMOS dataset and introduce MultiTHUMOS, a new
dataset of dense labels over unconstrained internet videos. Modeling multiple,
dense labels benefits from temporal relations within and across classes. We
define a novel variant of long short-term memory (LSTM) deep networks for
modeling these temporal relations via multiple input and output connections. We
show that this model improves action labeling accuracy and further enables
deeper understanding tasks ranging from structured retrieval to action
prediction.Comment: To appear in IJC
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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Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.
Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches
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