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RMPE: Regional Multi-person Pose Estimation

This paper proposes a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes and can achieve 76:7 mAP on the MPII (multi person) dataset.
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Accurate depth map estimation from a lenslet light field camera

This paper introduces an algorithm that accurately estimates depth maps using a lenslet light field camera and estimates the multi-view stereo correspondences with sub-pixel accuracy using the cost volume using the phase shift theorem.
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Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector

A novel few-shot object detection network that aims at detecting objects of unseen categories with only a few annotated examples, which exploits the similarity between the few shot support set and query set to detect novel objects while suppressing false detection in the background.
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High quality depth map upsampling for 3D-TOF cameras

This paper describes an application framework to perform high quality upsampling on depth maps captured from a low-resolution and noisy 3D time-of-flight (3D-ToF) camera that has been coupled with a…
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Deep Saliency with Encoded Low Level Distance Map and High Level Features

It is demonstrated that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features.
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Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

This paper introduces network trimming which iteratively optimizes the network by pruning unimportant neurons based on analysis of their outputs on a large dataset, inspired by an observation that the outputs of a significant portion of neurons in a large network are mostly zero.
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Accurate Single Stage Detector Using Recurrent Rolling Convolution

A novel single stage end-to-end trainable object detection network is proposed by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are deep in context.
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Deep High Dynamic Range Imaging with Large Foreground Motions

This paper proposes the first non-flow-based deep framework for high dynamic range (HDR) imaging of dynamic scenes with large-scale foreground motions, and produces excellent results where color artifacts and geometric distortions are significantly reduced compared to existing state-of-the-art methods.
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Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications

Instead of minimizing the nuclear norm, this paper proposes to minimize the partial sum of singular values, which implicitly encourages the target rank constraint, and shows that its results outperform those obtained by the conventional nuclear norm rank minimization method.
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Salient Region Detection via High-Dimensional Color Transform

A novel technique to automatically detect salient regions of an image via high-dimensional color transform that can linearly separate the salient regions from the background by finding an optimal linear combination of color coefficients in the high- dimensional color space.
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