NF-Net: Crowd Counting Based on Near-Far Network and Dynamic Dual Attention Mechanism
Bibliographic record
Abstract
Crowd counting and precise localization in dense scenes are critical tasks in computer vision. Although the point-based prediction framework P2PNet eliminates complex post-processing steps through ensemble prediction, it still faces challenges such as insufficient multi-scale feature extraction and limited ability to perceive key information in complex scenes. To address these issues, this paper improves P2PNet by proposing a model based on a far-near network and a dynamic dual attention mechanism. Specifically, it introduces a far-near adaptive network (FN-Net) and a dynamic dual attention mechanism (DDAM). FN-Net explicitly models continuous scale variations caused by perspective effects by dividing the image into regions based on spatial position and assigning differentiated receptive fields. DDAM focuses on crowded areas through parallel spatial attention and channel attention sub-modules, selects discriminative features, and integrates a dynamic weighted fusion mechanism to adaptively combine the advantages of both attentions. Experiments show that our approach effectively enhances key features while suppressing background noise thus improves crowd counting accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".