TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Multi-Person Pose Estimation	COCO test-dev	HigherHRNet (ScaleNet_P4)	AP	71.6	# 4
Multi-Person Pose Estimation	COCO test-dev	HigherHRNet (ScaleNet_P4)	APL	77.2	# 1
Multi-Person Pose Estimation	COCO test-dev	HigherHRNet (ScaleNet_P4)	APM	67.5	# 1
Multi-Person Pose Estimation	COCO test-dev	HigherHRNet (ScaleNet_P4)	AP50	90.3	# 1
Multi-Person Pose Estimation	COCO test-dev	HigherHRNet (ScaleNet_P4)	AP75	78.2	# 1
Multi-Person Pose Estimation	COCO test-dev	HigherHRNet (ScaleNet_P4)	AR	76.0	# 1
Multi-Person Pose Estimation	COCO test-dev	HigherHRNet (ScaleNet_P4)	AR50	92.3	# 1
Multi-Person Pose Estimation	CrowdPose	HigherHRNet (ScaleNet_P4)	mAP @0.5:0.95	71.3	# 1

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ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition

30 Nov 2020 • Hsin-Pai Cheng • Feng Liang • Meng Li • Bowen Cheng • Feng Yan • Hai Li • Vikas Chandra • Yiran Chen

Scale variance among different sizes of body parts and objects is a challenging problem for visual recognition tasks. Existing works usually design dedicated backbone or apply Neural architecture Search(NAS) for each task to tackle this challenge... (read more)

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Tasks

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MULTI-PERSON POSE ESTIMATION

NEURAL ARCHITECTURE SEARCH

ONE-SHOT LEARNING

POSE ESTIMATION

SEMANTIC SEGMENTATION

Datasets

ImageNet

COCO

Cityscapes

MPII

CrowdPose

Results from the Paper

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Ranked #1 on Multi-Person Pose Estimation on CrowdPose (using extra training data)

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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	BENCHMARK
Multi-Person Pose Estimation	COCO test-dev	HigherHRNet (ScaleNet_P4)	AP	71.6	# 4	Compare
			APL	77.2	# 1	Compare
			APM	67.5	# 1	Compare
			AP50	90.3	# 1	Compare
			AP75	78.2	# 1	Compare
			AR	76.0	# 1	Compare
			AR50	92.3	# 1	Compare
Multi-Person Pose Estimation	CrowdPose	HigherHRNet (ScaleNet_P4)	mAP @0.5:0.95	71.3	# 1	Compare

Methods used in the Paper

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METHOD	TYPE
Residual Connection	Skip Connections
ReLU	Activation Functions
Softmax	Output Functions
Batch Normalization	Normalization
Average Pooling	Pooling Operations
Scale Aggregation Block	Image Model Blocks
1x1 Convolution	Convolutions
Global Average Pooling	Pooling Operations
Convolution	Convolutions
Bottleneck Residual Block	Skip Connection Blocks
Dense Connections	Feedforward Networks
Max Pooling	Pooling Operations
ScaleNet	Convolutional Neural Networks

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