Class Tokens Infusion for Weakly Supervised Semantic Segmentation
Bibliographic record
Abstract
Weakly Supervised Semantic Segmentation (WSSS) re-lies on Class Activation Maps (CAMs) to extract spatial information from image-level labels. With the success of Vision Transformer (ViT), the migration of ViT is actively conducted in WSSS. This work proposes a novel WSSS framework with Class Token Infusion (CTI). By infusing the class tokens from images, we guide class tokens to pos-sess class-specific distinct characteristics and global-local consistency. For this, we devise two kinds of token infusion: 1) Intra-image Class Token Infusion (I-CTI) and 2) Cross-image Class Token Infusion (C-CTI). In I-CTI, we infuse the class tokens from the same but differently augmented images and thus make CAMs consistent among var-ious deformations (i.e. view, color). In C-CTI, by infusing the class tokens from the other images and imposing the resulting CAMs to be similar, it learns class-specific distinct characteristics. Besides the CTI, we bring the background (BG) concept into ViT with the BG token to reduce the false positive activation of CAMs. We demonstrate the effectiveness of our method on PASCAL VOC 2012 and MS COCO 2014 datasets, achieving state-of-the-art results in weakly supervised semantic segmentation. The code is available at https://github.com/yoon307/CTI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".