Discriminative Ranking on Industrial Dataset
0.7184AUCMMQ
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| MMQ2025.08 | 0.7184 | 0.5529 | 7.956 | 1 | 0.6081 | |
| RQ-kmeansQuantization Strategy=Modality-Separated2025.08 | 0.7181 | 0.6848 | 7.5962 | 1 | 0.6076 | |
| OPQQuantization Strategy=Modality-Separated2025.08 | 0.7177 | 0.5678 | 5.2789 | 0.3 | 0.6071 | |
| RQ-VAEQuantization Strategy=Modality-Separated2025.08 | 0.7171 | 0.5547 | 6.3389 | 0.99 | 0.6067 | |
| RQ-kmeansQuantization Strategy=Modality-Aligned2025.08 | 0.717 | 0.6759 | 7.7933 | 1 | 0.6056 | |
| RQ-VAEQuantization Strategy=Modality-Aligned2025.08 | 0.7169 | 0.6165 | 7.1178 | 0.79 | 0.6064 | |
| OPQQuantization Strategy=Modality-Aligned2025.08 | 0.7149 | 0.673 | 5.3092 | 0.33 | 0.6046 | |
| PPNet2025.08 | 0.7144 | — | — | — | 0.6034 | |
| ADC-SIDBackbone=PPNet2025.10 | 0.7101 | 0.0031 | 5.0975 | 1 | 0.5846 | |
| MM-RQ-VAEBackbone=PPNet2025.10 | 0.7095 | 0.0055 | 4.2125 | 0.985 | 0.5843 | |
| DASBackbone=PPNet2025.10 | 0.7091 | 0.0051 | 4.3539 | 1 | 0.5845 | |
| RQ-KmeansBackbone=PPNet2025.10 | 0.7089 | 0.0065 | 4.7232 | 1 | 0.5832 | |
| LETTERBackbone=PPNet2025.10 | 0.7089 | 0.0054 | 4.2072 | 1 | 0.5828 | |
| OPQBackbone=PPNet2025.10 | 0.7088 | 0.0038 | 4.3984 | 1 | 0.583 | |
| RQ-VAEBackbone=PPNet2025.10 | 0.7071 | 0.0033 | 4.2481 | 1 | 0.5805 |