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Found 8 papers, 3 papers with code
Color Invariant Skin Segmentation
A primary motivation of the work has been to achieve results that are consistent across the full range of skin tones, even while using a training dataset that is significantly biased toward lighter skin tones.
Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation
Category-level object pose estimation aims to find 6D object poses of previously unseen object instances from known categories without access to object CAD models.
A Compositional Approach to Occlusion in Panoptic Segmentation
We also show extensive experimental results for the COCO and Cityscapes datasets.
Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds
To reduce the huge amount of pose annotations needed for category-level learning, we propose for the first time a self-supervised learning framework to estimate category-level 6D object pose from single 3D point clouds.
Adaptive Label Smoothing
We present a novel approach to classification that combines the ideas of objectness and label smoothing during training.
Category-Level Articulated Object Pose Estimation
We develop a deep network based on PointNet++ that predicts ANCSH from a single depth point cloud, including part segmentation, normalized coordinates, and joint parameters in the canonical object space.
Input Fast-Forwarding for Better Deep Learning
This scheme is substantially different from "deep supervision" in which the loss layer is re-introduced to earlier layers.
An Image Dataset of Text Patches in Everyday Scenes
Although much research has been devoted to text detection and recognition in scanned documents, relatively little attention has been given to text detection in other types of images, such as photographs that are posted on social-media sites.
