F2CENet: Single-Image Object Counting Based on Block Co-Saliency Density Map Estimation
Bibliographic record
Abstract
This paper presents a novel single-image object counting method based on block co-saliency density map estimation, called free-to-count everything network (F2CENet). Image block co-saliency attention is introduced to promote density estimation adaptation, allowing to input any image with arbitrary size for accurate counting using the learned model without requiring manually labeled few shots. The proposed network also outperforms existing crowd counting methods based on geometry-adaptive kernels in complex scenes. A novel module generates multilevel & scale block correlation maps to guide the co-saliency density map estimation. Co-saliency attention maps are then fused for accurately locating block-wise salient objects under guidance of the initial cues. Hence, accurate density maps are generated via comprehensive learning of internal relations in block co-salient features and progressive optimization of local details with saliency-oriented scene understanding. Results from extensive experiments on existing density map estimation datasets with arbitrary challenges verify the effectiveness of the proposed F2CENet and show that it outperforms various state-of-the-art few-shot and crowd counting methods. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used as evaluation metrics to measure the accuracy which are commonly used metrics for counting task. The average predicted MAE and RMSE are 10.88% and 8.44% less compared with the state-of-the-art evaluated on dataset contains sufficiently large and diverse categories used for few-shot and crowd counting.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".