Activity Recognition on UCIHAR (test)
98.65Macro F1 ScoreAFVF
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| AFVF2023.12 | 98.65 | 98.61 | — | — | — | |
| Real time CNN2023.12 | 97.62 | 97.63 | — | — | — | |
| Layer-wise CNN2023.12 | 96.97 | 96.98 | — | — | — | |
| Contrastive Distillation2023.12 | 96.56 | 96.57 | — | — | — | |
| CNN and AOA2023.12 | 95.33 | 95.23 | — | — | — | |
| Finding Order in Chaos (Ours)Learning Paradigm=Self-Supervised2023.09 | 90.46 | 91.6 | — | — | — | |
| STAugLearning Paradigm=Self-Supervised2023.09 | 88.91 | 89.83 | — | — | — | |
| LLM-GuidedClassifier=Logistic Regression2025.12 | 88.79 | — | 19.99 | — | — | |
| PosETLearning Paradigm=Self-Supervised2023.09 | 87.35 | 88.13 | — | — | — | |
| GenRepLearning Paradigm=Self-Supervised2023.09 | 86.48 | 87.22 | — | — | — | |
| Traditional Augs.Learning Paradigm=Self-Supervised2023.09 | 86.13 | 87.05 | — | — | — | |
| LLM-GuidedClassifier=kNN2025.12 | 85.88 | — | 27.24 | — | — | |
| NNCLRLearning Paradigm=Self-Supervised2023.09 | 83.56 | 85.31 | — | — | — | |
| K-CenterClassifier=Logistic Regression2025.12 | 81.12 | — | — | — | — | |
| IDAALearning Paradigm=Self-Supervised2023.09 | 79.84 | 82.23 | — | — | — | |
| HerdingClassifier=Logistic Regression2025.12 | 79.64 | — | — | — | — | |
| LLM-GuidedClassifier=Random Forest2025.12 | 79.61 | — | 13.13 | — | — | |
| HerdingClassifier=Linear SVC2025.12 | 78.5 | — | — | — | — | |
| K-CenterClassifier=Random Forest2025.12 | 75.55 | — | — | — | — | |
| HerdingClassifier=kNN2025.12 | 74.97 | — | — | — | — | |
| Random SamplingClassifier=Linear SVC2025.12 | 74.45 | — | — | — | — | |
| HerdingClassifier=Random Forest2025.12 | 73.69 | — | — | — | — | |
| LLM-GuidedClassifier=Linear SVC2025.12 | 72.85 | — | -1.6 | — | — | |
| K-CenterClassifier=Linear SVC2025.12 | 71.23 | — | — | — | — | |
| Aug. BankLearning Paradigm=Self-Supervised2023.09 | 71.16 | 65.27 | — | — | — | |
| K-CenterClassifier=Gaussian Naïve Bayes2025.12 | 70.7 | — | — | — | — | |
| Random SamplingClassifier=Logistic Regression2025.12 | 68.79 | — | — | — | — | |
| LLM-GuidedClassifier=Mean2025.12 | 67.02 | — | 10.36 | — | — | |
| Random SamplingClassifier=Random Forest2025.12 | 66.48 | — | — | — | — | |
| DACLLearning Paradigm=Self-Supervised2023.09 | 66.28 | 73.12 | — | — | — | |
| K-CenterClassifier=Mean2025.12 | 62.72 | — | — | — | — | |
| HerdingClassifier=Mean2025.12 | 61.49 | — | — | — | — | |
| K-CenterClassifier=kNN2025.12 | 60.72 | — | — | — | — | |
| Random SamplingClassifier=kNN2025.12 | 58.64 | — | — | — | — | |
| LLM-GuidedClassifier=Gaussian Naïve Bayes2025.12 | 58.03 | — | 3.39 | — | — | |
| Random SamplingClassifier=Mean2025.12 | 56.66 | — | — | — | — | |
| Random SamplingClassifier=Gaussian Naïve Bayes2025.12 | 54.64 | — | — | — | — | |
| HerdingClassifier=Gaussian Naïve Bayes2025.12 | 45.15 | — | — | — | — | |
| InfoMinLearning Paradigm=Self-Supervised2023.09 | 30.66 | 38.07 | — | — | — | |
| Random SamplingClassifier=HistGradient Boosting2025.12 | 16.99 | — | — | — | — | |
| HerdingClassifier=HistGradient Boosting2025.12 | 16.99 | — | — | — | — | |
| K-CenterClassifier=HistGradient Boosting2025.12 | 16.99 | — | — | — | — | |
| LLM-GuidedClassifier=HistGradient Boosting2025.12 | 16.99 | — | 0 | — | — | |
| BiRNN2026.06 | — | 81.5 | — | — | 75.1 | |
| ChatTS# Parameters=8B, # Time Points=0.23B, Evaluation Protocol=linear-probing2026.03 | — | 49.55 | — | — | — | |
| Chronos2# Parameters=120M, # Time Points=242.8B, Evaluation Protocol=linear-probing2026.03 | — | 80.45 | — | — | — | |
| ChronosBase# Parameters=200M, # Time Points=12.8B, Evaluation Protocol=linear-probing2026.03 | — | 81.84 | — | — | — | |
| CODATSLearning Paradigm=Supervised2023.09 | — | 68.22 | — | — | — | |
| DCapsNet2023.12 | — | 98.43 | — | — | — | |
| DCLLearning Paradigm=Supervised2023.09 | — | 77.63 | — | — | — | |
| Ensem-HAR2023.12 | — | 95.05 | — | — | — | |
| Gaussian HMMNumber of states=62026.06 | — | 52.8 | — | — | 2 | |
| GILELearning Paradigm=Supervised2023.09 | — | 88.17 | — | — | — | |
| Normwear# Parameters=136M, # Time Points=0.31B, Evaluation Protocol=linear-probing2026.03 | — | 79.19 | — | — | — | |
| PatchTST (Supervised Learning)# Parameters=0.9M, Evaluation Protocol=Supervised Learning2026.03 | — | 84.49 | — | — | — | |
| RMRNNlambda=0.12026.06 | — | 81.8 | — | 54.46 | 66.3 | |
| RNN baselinelambda=02026.06 | — | 82.1 | — | — | 68 | |
| SimMTM# Parameters=0.5M, # Time Points=1.72M, Evaluation Protocol=linear-probing2026.03 | — | 71.75 | — | — | — | |
| SLIPBase# Parameters=120M, # Time Points=1.7B, Evaluation Protocol=linear-probing2026.03 | — | 86.25 | — | — | — | |
| Statistical MLEvaluation Protocol=linear-probing2026.03 | — | 84.24 | — | — | — | |
| SundialBase# Parameters=128M, # Time Points=1032B, Evaluation Protocol=linear-probing2026.03 | — | 58.89 | — | — | — | |
| TF-C# Parameters=12M, # Time Points=1.72M, Evaluation Protocol=linear-probing2026.03 | — | 63.81 | — | — | — |