Toys
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Sequential Recommendation | Toys | Recall@56.01 | 42 | |
| Sequential Recommendation | Toys (test) | NDCG@105.55 | 36 | |
| Denoising | Toys 256 x 256 x 31 MSI (test) | PSNR37.34 | 35 | |
| Tensor Completion | Toys 256 x 256 x 31 | PSNR48.67 | 35 | |
| Sequential Recommendation | Toys (Overall) | Hit Rate @108.46 | 24 | |
| Generative Recommendation | Toys | Recall@100.0846 | 23 | |
| Recommendation | Toys | Hit Ratio@100.1101 | 21 | |
| Sequential Recommendation | Toys | Recall@108.02 | 20 | |
| 3D Asset Reconstruction | Toys4k | CD0.0083 | 18 | |
| Node Clustering | Toys | NMI54.66 | 17 | |
| Node Classification | Toys | Accuracy78.91 | 14 | |
| Node Classification | Toys MAGB | Accuracy80.92 | 13 | |
| Generative Recommendation | Toys | Ad Rate94.3 | 11 | |
| Sequential Recommendation | Toys | HR@57.83 | 11 | |
| Direct Recommendation | Toys | Hit Rate@558.93 | 9 | |
| Refinement of VFM-derived artifacts | Toys4k (synthetically corrupted) | mIoU0.858 | 8 | |
| Recommendation | Toys TIGER Backbone (Period 4) | Hit Rate @52.69 | 7 | |
| Recommendation | Toys TIGER Backbone (Period 3) | H@53.09 | 7 | |
| Recommendation | Toys TIGER Backbone (Period 2) | H@52 | 7 | |
| Recommendation | Toys TIGER Backbone (Period 1) | Hit Rate @52.08 | 7 | |
| Top-n Recommendation | Toys | HR@19.33 | 7 | |
| Explanation Generation | Toys (test) | BLEU-42.5319 | 7 | |
| Sequential Recommendation | Toys In-domain (test) | Recall@56.84 | 7 | |
| Low-shot object recognition | Toys4k Categ-SObj-PoseVar | Accuracy75.18 | 6 | |
| Low-shot object recognition | Toys4k Categ-SObj | Accuracy79.69 | 6 |
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