26,343 research outputs found
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data
Paucity of large curated hand-labeled training data for every
domain-of-interest forms a major bottleneck in the deployment of machine
learning models in computer vision and other fields. Recent work (Data
Programming) has shown how distant supervision signals in the form of labeling
functions can be used to obtain labels for given data in near-constant time. In
this work, we present Adversarial Data Programming (ADP), which presents an
adversarial methodology to generate data as well as a curated aggregated label
has given a set of weak labeling functions. We validated our method on the
MNIST, Fashion MNIST, CIFAR 10 and SVHN datasets, and it outperformed many
state-of-the-art models. We conducted extensive experiments to study its
usefulness, as well as showed how the proposed ADP framework can be used for
transfer learning as well as multi-task learning, where data from two domains
are generated simultaneously using the framework along with the label
information. Our future work will involve understanding the theoretical
implications of this new framework from a game-theoretic perspective, as well
as explore the performance of the method on more complex datasets.Comment: CVPR 2018 main conference pape
Activity Driven Weakly Supervised Object Detection
Weakly supervised object detection aims at reducing the amount of supervision
required to train detection models. Such models are traditionally learned from
images/videos labelled only with the object class and not the object bounding
box. In our work, we try to leverage not only the object class labels but also
the action labels associated with the data. We show that the action depicted in
the image/video can provide strong cues about the location of the associated
object. We learn a spatial prior for the object dependent on the action (e.g.
"ball" is closer to "leg of the person" in "kicking ball"), and incorporate
this prior to simultaneously train a joint object detection and action
classification model. We conducted experiments on both video datasets and image
datasets to evaluate the performance of our weakly supervised object detection
model. Our approach outperformed the current state-of-the-art (SOTA) method by
more than 6% in mAP on the Charades video dataset.Comment: CVPR'19 camera read
Exploiting saliency for object segmentation from image level labels
There have been remarkable improvements in the semantic labelling task in the
recent years. However, the state of the art methods rely on large-scale
pixel-level annotations. This paper studies the problem of training a
pixel-wise semantic labeller network from image-level annotations of the
present object classes. Recently, it has been shown that high quality seeds
indicating discriminative object regions can be obtained from image-level
labels. Without additional information, obtaining the full extent of the object
is an inherently ill-posed problem due to co-occurrences. We propose using a
saliency model as additional information and hereby exploit prior knowledge on
the object extent and image statistics. We show how to combine both information
sources in order to recover 80% of the fully supervised performance - which is
the new state of the art in weakly supervised training for pixel-wise semantic
labelling. The code is available at https://goo.gl/KygSeb.Comment: CVPR 201
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Multiple instance learning (MIL) is a form of weakly supervised learning
where training instances are arranged in sets, called bags, and a label is
provided for the entire bag. This formulation is gaining interest because it
naturally fits various problems and allows to leverage weakly labeled data.
Consequently, it has been used in diverse application fields such as computer
vision and document classification. However, learning from bags raises
important challenges that are unique to MIL. This paper provides a
comprehensive survey of the characteristics which define and differentiate the
types of MIL problems. Until now, these problem characteristics have not been
formally identified and described. As a result, the variations in performance
of MIL algorithms from one data set to another are difficult to explain. In
this paper, MIL problem characteristics are grouped into four broad categories:
the composition of the bags, the types of data distribution, the ambiguity of
instance labels, and the task to be performed. Methods specialized to address
each category are reviewed. Then, the extent to which these characteristics
manifest themselves in key MIL application areas are described. Finally,
experiments are conducted to compare the performance of 16 state-of-the-art MIL
methods on selected problem characteristics. This paper provides insight on how
the problem characteristics affect MIL algorithms, recommendations for future
benchmarking and promising avenues for research
- …