A new testing framework for VLN agents combines adaptive test case generation, capability oracles, and feedback to discover more failures and attribute them to specific capability deficiencies more accurately than baselines.
InProceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 14911–14920
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Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents
A new testing framework for VLN agents combines adaptive test case generation, capability oracles, and feedback to discover more failures and attribute them to specific capability deficiencies more accurately than baselines.