510 research outputs found
Recurrent 3D Pose Sequence Machines
3D human articulated pose recovery from monocular image sequences is very
challenging due to the diverse appearances, viewpoints, occlusions, and also
the human 3D pose is inherently ambiguous from the monocular imagery. It is
thus critical to exploit rich spatial and temporal long-range dependencies
among body joints for accurate 3D pose sequence prediction. Existing approaches
usually manually design some elaborate prior terms and human body kinematic
constraints for capturing structures, which are often insufficient to exploit
all intrinsic structures and not scalable for all scenarios. In contrast, this
paper presents a Recurrent 3D Pose Sequence Machine(RPSM) to automatically
learn the image-dependent structural constraint and sequence-dependent temporal
context by using a multi-stage sequential refinement. At each stage, our RPSM
is composed of three modules to predict the 3D pose sequences based on the
previously learned 2D pose representations and 3D poses: (i) a 2D pose module
extracting the image-dependent pose representations, (ii) a 3D pose recurrent
module regressing 3D poses and (iii) a feature adaption module serving as a
bridge between module (i) and (ii) to enable the representation transformation
from 2D to 3D domain. These three modules are then assembled into a sequential
prediction framework to refine the predicted poses with multiple recurrent
stages. Extensive evaluations on the Human3.6M dataset and HumanEva-I dataset
show that our RPSM outperforms all state-of-the-art approaches for 3D pose
estimation.Comment: Published in CVPR 201
Recognizing Focal Liver Lesions in Contrast-Enhanced Ultrasound with Discriminatively Trained Spatio-Temporal Model
The aim of this study is to provide an automatic computational framework to
assist clinicians in diagnosing Focal Liver Lesions (FLLs) in
Contrast-Enhancement Ultrasound (CEUS). We represent FLLs in a CEUS video clip
as an ensemble of Region-of-Interests (ROIs), whose locations are modeled as
latent variables in a discriminative model. Different types of FLLs are
characterized by both spatial and temporal enhancement patterns of the ROIs.
The model is learned by iteratively inferring the optimal ROI locations and
optimizing the model parameters. To efficiently search the optimal spatial and
temporal locations of the ROIs, we propose a data-driven inference algorithm by
combining effective spatial and temporal pruning. The experiments show that our
method achieves promising results on the largest dataset in the literature (to
the best of our knowledge), which we have made publicly available.Comment: 5 pages, 1 figure
Study of the Game Theory Analysis and Incentive Mechanism of Inter-organizational Knowledge Sharing in Cooperative R&D
AbstractWith the technological development and social demand, an increasing number of multi-field complex products come into being and call for the research and development (R&D) of many enterprises. Since the R&D is related to a large amount of knowledge, it is of great significance to realize effective knowledge sharing and establish an incentive mechanism to promote the knowledge sharing. This study constructed an inter-organizational knowledge sharing model for cooperative R&D and a static inter-organizational knowledge sharing game theory model. Subsequently, it analyzed the decision matrix of cooperative organizations in detail. Finally, it proposed an incentive mechanism to realize knowledge sharing, which a feasible method for improving the effect of the inter-organizational knowledge sharing in cooperative R&D
Attention-Aware Face Hallucination via Deep Reinforcement Learning
Face hallucination is a domain-specific super-resolution problem with the
goal to generate high-resolution (HR) faces from low-resolution (LR) input
images. In contrast to existing methods that often learn a single
patch-to-patch mapping from LR to HR images and are regardless of the
contextual interdependency between patches, we propose a novel Attention-aware
Face Hallucination (Attention-FH) framework which resorts to deep reinforcement
learning for sequentially discovering attended patches and then performing the
facial part enhancement by fully exploiting the global interdependency of the
image. Specifically, in each time step, the recurrent policy network is
proposed to dynamically specify a new attended region by incorporating what
happened in the past. The state (i.e., face hallucination result for the whole
image) can thus be exploited and updated by the local enhancement network on
the selected region. The Attention-FH approach jointly learns the recurrent
policy network and local enhancement network through maximizing the long-term
reward that reflects the hallucination performance over the whole image.
Therefore, our proposed Attention-FH is capable of adaptively personalizing an
optimal searching path for each face image according to its own characteristic.
Extensive experiments show our approach significantly surpasses the
state-of-the-arts on in-the-wild faces with large pose and illumination
variations
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