7 research outputs found
LSDA: Large Scale Detection Through Adaptation
A major challenge in scaling object detection is the difficulty of obtaining
labeled images for large numbers of categories. Recently, deep convolutional
neural networks (CNNs) have emerged as clear winners on object classification
benchmarks, in part due to training with 1.2M+ labeled classification images.
Unfortunately, only a small fraction of those labels are available for the
detection task. It is much cheaper and easier to collect large quantities of
image-level labels from search engines than it is to collect detection data and
label it with precise bounding boxes. In this paper, we propose Large Scale
Detection through Adaptation (LSDA), an algorithm which learns the difference
between the two tasks and transfers this knowledge to classifiers for
categories without bounding box annotated data, turning them into detectors.
Our method has the potential to enable detection for the tens of thousands of
categories that lack bounding box annotations, yet have plenty of
classification data. Evaluation on the ImageNet LSVRC-2013 detection challenge
demonstrates the efficacy of our approach. This algorithm enables us to produce
a >7.6K detector by using available classification data from leaf nodes in the
ImageNet tree. We additionally demonstrate how to modify our architecture to
produce a fast detector (running at 2fps for the 7.6K detector). Models and
software are available a
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning
We develop methods for detector learning which exploit joint training over
both weak and strong labels and which transfer learned perceptual
representations from strongly-labeled auxiliary tasks. Previous methods for
weak-label learning often learn detector models independently using latent
variable optimization, but fail to share deep representation knowledge across
classes and usually require strong initialization. Other previous methods
transfer deep representations from domains with strong labels to those with
only weak labels, but do not optimize over individual latent boxes, and thus
may miss specific salient structures for a particular category. We propose a
model that subsumes these previous approaches, and simultaneously trains a
representation and detectors for categories with either weak or strong labels
present. We provide a novel formulation of a joint multiple instance learning
method that includes examples from classification-style data when available,
and also performs domain transfer learning to improve the underlying detector
representation. Our model outperforms known methods on ImageNet-200 detection
with weak labels
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
Random clustering ferns for multimodal object recognition
The final publication is available at link.springer.comWe propose an efficient and robust method for the recognition of objects exhibiting multiple intra-class modes, where each one is associated with a particular object appearance. The proposed method, called random clustering ferns, combines synergically a single and real-time classifier, based on the boosted assembling of extremely randomized trees (ferns), with an unsupervised and probabilistic approach in order to recognize efficiently object instances in images and discover simultaneously the most prominent appearance modes of the object through tree-structured visual words. In particular, we use boosted random ferns and probabilistic latent semantic analysis to obtain a discriminative and multimodal classifier that automatically clusters the response of its randomized trees in function of the visual object appearance. The proposed method is validated extensively in synthetic and real experiments, showing that the method is capable of detecting objects with diverse and complex appearance distributions in real-time performance.Peer ReviewedPostprint (author's final draft