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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".