SegAttnDetec: A Segmentation-Aware Attention-Based Object Detector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Object detection (OD) has emerged as a cornerstone of computer vision applications, with deep learning (DL) driving significant advancements. While modern OD algorithms excel in identifying objects, they often falter when confronted with small objects in intricate scenes. To address this challenge, we introduce SegAttnDetec, a novel framework that leverages semantic segmentation to enhance object detection performance. By fusing semantic segmentation-aware features with the backbone of an OD model and incorporating an attention-gating mechanism, SegAttnDetec enables the model to capture richer, more refined features, leading to substantial improvements in small object detection. Notably, our approach achieves a remarkable 28.5% increase in pedestrian hard category detection and a 38.8% improvement in cyclist hard category detection on the KITTI benchmark dataset; hence, reaching an overall mean average precision (mAP) of 83.5% in all the categories of the same dataset.
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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 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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