About

Keypoint detection involves simultaneously detecting people and localizing their keypoints. Keypoints are the same thing as interest points. They are spatial locations, or points in the image that define what is interesting or what stand out in the image. They are invariant to image rotation, shrinkage, translation, distortion, and so on.

( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )

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Latest papers with code

Pose Recognition with Cascade Transformers

14 Apr 2021mlpc-ucsd/PRTR

Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches.

KEYPOINT DETECTION

44
14 Apr 2021

Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression

6 Apr 2021HRNet/DEKR

Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions.

KEYPOINT DETECTION

65
06 Apr 2021

Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping

6 Apr 2021jiahaoLjh/PoseGrouping

More specifically, we design a Geometry-aware Association GNN that utilizes spatial information of the keypoints and learns local affinity from the global context.

GRAPH PARTITIONING KEYPOINT DETECTION MULTI-PERSON POSE ESTIMATION

4
06 Apr 2021

Deep Dual Consecutive Network for Human Pose Estimation

12 Mar 2021Pose-Group/DCPose

Multi-frame human pose estimation in complicated situations is challenging.

KEYPOINT DETECTION

110
12 Mar 2021

Regressive Domain Adaptation for Unsupervised Keypoint Detection

10 Mar 2021thuml/Transfer-Learning-Library

First, based on our observation that the probability density of the output space is sparse, we introduce a spatial probability distribution to describe this sparsity and then use it to guide the learning of the adversarial regressor.

DOMAIN ADAPTATION KEYPOINT DETECTION

762
10 Mar 2021

OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association

3 Mar 2021vita-epfl/openpifpaf

We present a generic neural network architecture that uses Composite Fields to detect and construct a spatio-temporal pose which is a single, connected graph whose nodes are the semantic keypoints (e. g., a person's body joints) in multiple frames.

KEYPOINT DETECTION MULTI-PERSON POSE ESTIMATION SELF-DRIVING CARS

695
03 Mar 2021

Auto Learning Attention

NeurIPS 2020 btma48/AutoLA

Attention modules have been demonstrated effective in strengthening the representation ability of a neural network via reweighting spatial or channel features or stacking both operations sequentially.

IMAGE CLASSIFICATION KEYPOINT DETECTION OBJECT DETECTION

10
01 Dec 2020

Fast Fourier Convolution

NeurIPS 2020 pkumivision/FFC

FFC is a generic operator that can directly replace vanilla convolutions in a large body of existing networks, without any adjustments and with comparable complexity metrics (e. g., FLOPs).

ACTION RECOGNITION KEYPOINT DETECTION

1
01 Dec 2020