Physical Knot Classification Beyond Accuracy: A Benchmark and Diagnostic Study
Physical knot classification is a challenging fine-grained recognition task in which the intended discriminative cue is rope crossing structure; however, high closed-set accuracy may still arise from low-level appearance shortcuts rather than genuine topological understanding. In this work, we introduce dataset (1,440 images, 10 classes), which trains models on loosely tied knots and evaluates them on tightly dressed configurations to probe whether structure-guided training yields topology-specific gains. We demonstrate that topological distance successfully predicts residual inter-class confusion across multiple backbone architectures, validating the utility of our topology-aware evaluat...
| Authors | Shiheng Nie, Yunguang Yue |
|---|---|
| Primary category | cs.CV |
| Published | 24 March 2026 |
| Last updated at source | 22 April 2026 |
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