Search Results for author:
Found 21 papers, 9 papers with code
BLSM: A Bone-Level Skinned Model of the Human Mesh
We introduce BLSM, a bone-level skinned model of the human body mesh where bone scales are set prior to template synthesis, rather than the common, inverse practice.
MeshPose: Unifying DensePose and 3D Body Mesh reconstruction
DensePose provides a pixel-accurate association of images with 3D mesh coordinates, but does not provide a 3D mesh, while Human Mesh Reconstruction (HMR) systems have high 2D reprojection error, as measured by DensePose localization metrics.
Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning
The key insight is to leverage previously seen "calibration" images of a given user to extrapolate what should be rendered in a novel viewpoint from the data available in the sensor.
DeeperLab: Single-Shot Image Parser
We present a single-shot, bottom-up approach for whole image parsing.
Ranked #32 on
Panoptic Segmentation
on Cityscapes val
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.
Ranked #1 on
Human Part Segmentation
on PASCAL-Person-Part
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model.
Ranked #8 on
Multi-Person Pose Estimation
on COCO test-dev
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.
Ranked #1 on
Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
Within each region of interest, MaskLab performs foreground/background segmentation by combining semantic and direction prediction.
Ranked #86 on
Instance Segmentation
on COCO test-dev
(using extra training data)
Rethinking Atrous Convolution for Semantic Image Segmentation
To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates.
Ranked #3 on
Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
Towards Accurate Multi-person Pose Estimation in the Wild
Trained on COCO data alone, our final system achieves average precision of 0. 649 on the COCO test-dev set and the 0. 643 test-standard sets, outperforming the winner of the 2016 COCO keypoints challenge and other recent state-of-art.
Ranked #6 on
Keypoint Detection
on COCO test-challenge
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales.
Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.
Im2Calories: Towards an Automated Mobile Vision Food Diary
We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.
Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
Deep convolutional neural networks (CNNs) are the backbone of state-of-art semantic image segmentation systems.
Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection
Deep Convolutional Neural Networks (DCNNs) achieve invariance to domain transformations (deformations) by using multiple 'max-pooling' (MP) layers.
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation
Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
This is due to the very invariance properties that make DCNNs good for high level tasks.
Ranked #3 on
Scene Segmentation
on SUN-RGBD
Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection
Deep Convolutional Neural Networks (DCNNs) commonly use generic `max-pooling' (MP) layers to extract deformation-invariant features, but we argue in favor of a more refined treatment.
Deep Epitomic Convolutional Neural Networks
An epitomic convolution layer replaces a pair of consecutive convolution and max-pooling layers found in standard deep convolutional neural networks.
Modeling Image Patches with a Generic Dictionary of Mini-Epitomes
As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting.
Gaussian sampling by local perturbations
We present a technique for exact simulation of Gaussian Markov random fields (GMRFs), which can be interpreted as locally injecting noise to each Gaussian factor independently, followed by computing the mean/mode of the perturbed GMRF.
