29 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
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
Autonomous driving has harsh requirements of small model size and energy
efficiency, in order to enable the embedded system to achieve real-time
on-board object detection. Recent deep convolutional neural network based
object detectors have achieved state-of-the-art accuracy. However, such models
are trained with numerous parameters and their high computational costs and
large storage prohibit the deployment to memory and computation resource
limited systems. Low-precision neural networks are popular techniques for
reducing the computation requirements and memory footprint. Among them, binary
weight neural network (BWN) is the extreme case which quantizes the float-point
into just bit. BWNs are difficult to train and suffer from accuracy
deprecation due to the extreme low-bit representation. To address this problem,
we propose a knowledge transfer (KT) method to aid the training of BWN using a
full-precision teacher network. We built DarkNet- and MobileNet-based binary
weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car,
pedestrian and cyclist detection. The experimental results show that the
proposed method maintains high detection accuracy while reducing the model size
of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB.Comment: Accepted by ICRA 201
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly