Are there any keypoint annotation tools that handle class imbalance in visual datasets?

Yes — though most keypoint annotation tools don’t “solve” class imbalance automatically, some support workflows that make imbalanced keypoint datasets easier to manage. In practice, class imbalance is usually handled by the data collection / sampling / QA process around the annotation tool rather than by the tool itself.

Tools/features that help with imbalance

Look for platforms that support:

• Sampling / task prioritization
  • lets you over-assign rare classes or rare poses to annotators
• Dataset analytics
  • class counts, per-keypoint frequency, missing-keypoint rates
• Quality control / review queues
  • helpful when minority classes need extra verification
• Active learning integration
  • model suggests uncertain examples, often surfacing rare cases
• Custom labeling workflows
  • you can route specific image subsets to specific annotators

Common annotation tools used for keypoints

These are widely used for keypoint annotation and can be adapted to imbalance-aware workflows:

• CVAT
  Strong for keypoints, skeletons, and custom workflows; often used with external sampling/analytics scripts.
• Label Studio
  Flexible, supports keypoints and can be integrated with custom data selection logic.
• Supervisely
  Has robust dataset management and analytics; useful for monitoring label distribution.
• VGG Image Annotator (VIA)
  Lightweight, good for smaller projects, but limited imbalance support.
• Scale AI / Labelbox / Hive / Supervisely
  More enterprise-oriented; may offer analytics, QA, and workflow controls that help with imbalance.

If you mean algorithmic handling of imbalance

Some annotation platforms don’t directly address imbalance, but active learning plugins/workflows do. Those can:

• surface rare keypoint configurations
• prioritize underrepresented classes
• reduce annotation effort on frequent/easy examples

Practical recommendation

If your concern is imbalanced pose classes or rare keypoint categories, the best setup is usually:

1. Use CVAT / Label Studio / Supervisely
2. Add a class distribution dashboard
3. Use stratified sampling or oversampling of rare classes
4. Optionally add active learning to find minority examples

If you want, I can also give you:

• a tool-by-tool comparison for keypoint annotation, or
• a workflow for handling class imbalance in keypoint datasets.