658 research outputs found
Semantic bottleneck for computer vision tasks
This paper introduces a novel method for the representation of images that is
semantic by nature, addressing the question of computation intelligibility in
computer vision tasks. More specifically, our proposition is to introduce what
we call a semantic bottleneck in the processing pipeline, which is a crossing
point in which the representation of the image is entirely expressed with
natural language , while retaining the efficiency of numerical representations.
We show that our approach is able to generate semantic representations that
give state-of-the-art results on semantic content-based image retrieval and
also perform very well on image classification tasks. Intelligibility is
evaluated through user centered experiments for failure detection
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
Neural Motifs: Scene Graph Parsing with Global Context
We investigate the problem of producing structured graph representations of
visual scenes. Our work analyzes the role of motifs: regularly appearing
substructures in scene graphs. We present new quantitative insights on such
repeated structures in the Visual Genome dataset. Our analysis shows that
object labels are highly predictive of relation labels but not vice-versa. We
also find that there are recurring patterns even in larger subgraphs: more than
50% of graphs contain motifs involving at least two relations. Our analysis
motivates a new baseline: given object detections, predict the most frequent
relation between object pairs with the given labels, as seen in the training
set. This baseline improves on the previous state-of-the-art by an average of
3.6% relative improvement across evaluation settings. We then introduce Stacked
Motif Networks, a new architecture designed to capture higher order motifs in
scene graphs that further improves over our strong baseline by an average 7.1%
relative gain. Our code is available at github.com/rowanz/neural-motifs.Comment: CVPR 2018 camera read
Improving fairness in machine learning systems: What do industry practitioners need?
The potential for machine learning (ML) systems to amplify social inequities
and unfairness is receiving increasing popular and academic attention. A surge
of recent work has focused on the development of algorithmic tools to assess
and mitigate such unfairness. If these tools are to have a positive impact on
industry practice, however, it is crucial that their design be informed by an
understanding of real-world needs. Through 35 semi-structured interviews and an
anonymous survey of 267 ML practitioners, we conduct the first systematic
investigation of commercial product teams' challenges and needs for support in
developing fairer ML systems. We identify areas of alignment and disconnect
between the challenges faced by industry practitioners and solutions proposed
in the fair ML research literature. Based on these findings, we highlight
directions for future ML and HCI research that will better address industry
practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in
Computing Systems (CHI 2019
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