From SAM to CAMs: Exploring Segment Anything Model for Weakly Supervised Semantic Segmentation
Why this work is in the frame
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Bibliographic record
Abstract
Weakly Supervised Semantic Segmentation (WSSS) aims to learn the concept of segmentation using image-level class labels. Recent WSSS works have shown promising results by using the Segment Anything Model (SAM), a foundation model for segmentation, during the inference phase. However, we observe that these methods can still be vulnerable to the noise of class activation maps (CAMs) serving as initial seeds. As a remedy, this paper introduces From-SAM-to-CAMs (S2C), a novel WSSS framework that directly transfers the knowledge of SAM to the classifier during the training process, enhancing the quality of CAMs it-self. S2C comprises SAM-segment Contrasting (SSC) and a CAM-based prompting module (CPM), which exploit SAM at the feature and logit levels, respectively. SSC performs prototype-based contrasting using SAM's automatic segmentation results. It constrains each feature to be close to the prototype of its segment and distant from prototypes of the others. Meanwhile, CPM extracts prompts from the CAM of each class and uses them to generate classspecific segmentation masks through SAM. The masks are aggregated into unified self-supervision based on the confidence score, designed to consider the reliability of both SAM and CAMs. S2C achieves a new state-of-the-art performance across all benchmarks, outperforming existing studies by significant margins. The code is available at https://github.com/sangrockEG/S2C.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it