497 research outputs found
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
This paper proposes a new hybrid architecture that consists of a deep
Convolutional Network and a Markov Random Field. We show how this architecture
is successfully applied to the challenging problem of articulated human pose
estimation in monocular images. The architecture can exploit structural domain
constraints such as geometric relationships between body joint locations. We
show that joint training of these two model paradigms improves performance and
allows us to significantly outperform existing state-of-the-art techniques
Rethinking Object Detection in Retail Stores
The convention standard for object detection uses a bounding box to represent
each individual object instance. However, it is not practical in the
industry-relevant applications in the context of warehouses due to severe
occlusions among groups of instances of the same categories. In this paper, we
propose a new task, ie, simultaneously object localization and counting,
abbreviated as Locount, which requires algorithms to localize groups of objects
of interest with the number of instances. However, there does not exist a
dataset or benchmark designed for such a task. To this end, we collect a
large-scale object localization and counting dataset with rich annotations in
retail stores, which consists of 50,394 images with more than 1.9 million
object instances in 140 categories. Together with this dataset, we provide a
new evaluation protocol and divide the training and testing subsets to fairly
evaluate the performance of algorithms for Locount, developing a new benchmark
for the Locount task. Moreover, we present a cascaded localization and counting
network as a strong baseline, which gradually classifies and regresses the
bounding boxes of objects with the predicted numbers of instances enclosed in
the bounding boxes, trained in an end-to-end manner. Extensive experiments are
conducted on the proposed dataset to demonstrate its significance and the
analysis discussions on failure cases are provided to indicate future
directions. Dataset is available at
https://isrc.iscas.ac.cn/gitlab/research/locount-dataset.Comment: Information Erro
Towards efficient on-board deployment of DNNs on intelligent autonomous systems
With their unprecedented performance in major AI tasks, deep neural networks (DNNs) have emerged as a primary building block in modern autonomous systems. Intelligent systems such as drones, mobile robots and driverless cars largely base their perception, planning and application-specific tasks on DNN models. Nevertheless, due to the nature of these applications, such systems require on-board local processing in order to retain their autonomy and meet latency and throughput constraints. In this respect, the large computational and memory demands of DNN workloads pose a significant barrier on their deployment on the resource-and power-constrained compute platforms that are available on-board. This paper presents an overview of recent methods and hardware architectures that address the system-level challenges of modern DNN-enabled autonomous systems at both the algorithmic and hardware design level. Spanning from latency-driven approximate computing techniques to high-throughput mixed-precision cascaded classifiers, the presented set of works paves the way for the on-board deployment of sophisticated DNN models on robots and autonomous systems
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