Dimensions overall score 7.0

GitHub Code Pulse

No public code linked for this paper yet.

• Standard evaluation metrics like mean Average Precision (mAP) fail to credit semantically valid but lexically different predictions in HOI detection.limitation 90%
• SHOE incorporates semantic similarity between predicted and ground-truth HOI labels by decomposing predictions into verb and object components.method 90%
• SHOE scores align more closely with human judgments than existing metrics, achieving 85.73% agreement with average human ratings.result 95%
• SHOE outperforms both LLM-based and embedding-based baselines in aligning with human judgments.result 85%
• SHOE enables flexible and scalable evaluation of both existing HOI detection methods and open-ended generative models.technical 80%
• SHOE can be applied to standard benchmarks such as HICO-DET for evaluating open-vocabulary predictions.technical 85%
• Open-vocabulary HOI detection is a step towards building scalable systems that generalize to unseen interactions in real-world scenarios.market 80%
• Semantically grounded HOI evaluation better mirrors human understanding of interactions than categorical label-based approaches.technical 85%

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Startup potential card