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Found 16 papers, 8 papers with code
Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware Association
This paper presents a new method to solve keypoint detection and instance association by using Transformer.
Ranked #9 on
Multi-Person Pose Estimation
on COCO
Guiding Query Position and Performing Similar Attention for Transformer-Based Detection Heads
After DETR was proposed, this novel transformer-based detection paradigm which performs several cross-attentions between object queries and feature maps for predictions has subsequently derived a series of transformer-based detection heads.
Adaptive Dilated Convolution For Human Pose Estimation
It can generate and fuse multi-scale features of the same spatial sizes by setting different dilation rates for different channels.
Is 2D Heatmap Representation Even Necessary for Human Pose Estimation?
The 2D heatmap representation has dominated human pose estimation for years due to its high performance.
Graph-MLP: Node Classification without Message Passing in Graph
Graph Neural Network (GNN) has been demonstrated its effectiveness in dealing with non-Euclidean structural data.
TokenPose: Learning Keypoint Tokens for Human Pose Estimation
Most existing CNN-based methods do well in visual representation, however, lacking in the ability to explicitly learn the constraint relationships between keypoints.
V2F-Net: Explicit Decomposition of Occluded Pedestrian Detection
V2F-Net consists of two sub-networks: Visible region Detection Network (VDN) and Full body Estimation Network (FEN).
General Instance Distillation for Object Detection
In recent years, knowledge distillation has been proved to be an effective solution for model compression.
Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation
However, for bottom-up methods, which need to handle a large variance of human scales and labeling ambiguities, the current practice seems unreasonable.
Efficient Human Pose Estimation by Learning Deeply Aggregated Representations
Instead, we focus on exploiting multi-scale information from layers with different receptive-field sizes and then making full of use this information by improving the fusion method.
DPGN: Distribution Propagation Graph Network for Few-shot Learning
To combine the distribution-level relations and instance-level relations for all examples, we construct a dual complete graph network which consists of a point graph and a distribution graph with each node standing for an example.
Ranked #1 on
Few-Shot Learning
on Mini-ImageNet - 1-Shot Learning
High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
When aligning two groups of local features from two images, we view it as a graph matching problem and propose a cross-graph embedded-alignment (CGEA) layer to jointly learn and embed topology information to local features, and straightly predict similarity score.
Learning Delicate Local Representations for Multi-Person Pose Estimation
To tackle this problem, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations.
GridFace: Face Rectification via Learning Local Homography Transformations
In this paper, we propose a method, called GridFace, to reduce facial geometric variations and improve the recognition performance.
Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression
Facial landmark localization plays an important role in face recognition and analysis applications.
Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
In this paper, we report our observations on how big data impacts the recognition performance.
