Attention-based Selection Strategy for Weakly Supervised Object Localization
Bibliographic record
Abstract
Weakly Supervised Object Localization (WSOL) task aims to recognize the object position by using only image-level labels. Some previous techniques remove the most discriminative parts for all input images or random images to capture the entire object location. However, these methods can not perform the correct operation on different images such as hiding the data or feature maps that should not be hidden. In this case, both classification and localization accuracy will be affected. Meanwhile, just erasing the most important regions tends to make the model learn the less discriminative parts from outside of the objects. To address these limitations, we propose an Attention-based Selection Strategy (ASS) method to choose images that do need to be erased. Moreover, we use different threshold self-attention maps to reduce the impact of unhelpful information in one of the branches of our selection strategy. Based on our experiments, the proposed method is simple but effective to improve the performance of WSOL. In particular, ASS achieves new state-of-the-art accuracy on CUB-200-2011 dataset and works very well on ILSVRC 2016 dataset.
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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 | 0.000 |
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".