Learning to Cluster Faces via Hypergraph Convolution with Transformer on Large Graph
Bibliographic record
Abstract
Face clustering is a very useful method in image annotation, image retrieval and other real-world applications.The main challenge is that with the increasing data scale, the large graph constructed by KNN is difficult to train due to out of memory.At the same time, the image features of the previous face clustering methods are extracted through CNN, which is not conducive to obtaining the global features of the image.In this paper, we propose a method that uses transformer to extract image features.Then, we use the extracted image features to construct a large graph by KNN, and we randomly partition the large graph into several non-overlapping subgraphs through METIS.In addition, we use hypergraph convolution to learn deeper high-order graph structure data.Experiments on both MS-Celeb-1M and DeepFashion show that our method achieves state-of-the-art performance, eg.90.08% in pair-wise F-score on MS-Celeb-1M.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".