Human Instance Segmentation
3 papers with code • 1 benchmarks • 3 datasets
Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.
Image Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers
Benchmarks
These leaderboards are used to track progress in Human Instance Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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OCHuman
TikTok Dataset
PeopleSansPeople
Latest papers with no code
Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation
In this approach, we do not assume test-time access to the labeled source dataset.
Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder
In inference, it is possible to obtain instance segmentation results only from sound images.
Human Instance Segmentation and Tracking via Data Association and Single-stage Detector
To tracking the instance across the video, we have adopted data association strategy for matching the same instance in the video sequence, where we jointly learn target instance appearances and their affinities in a pair of video frames in an end-to-end fashion.
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation
Amodal recognition is the ability of the system to detect occluded objects.
