Efficient object detector via dynamic prior and dynamic feature fusion
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Sparse R-CNN is a new paradigm of object detection, which predicts objects in a sparse way. However, there are some limitations in Sparse R-CNN. One is the presence of weak prior information caused by fixed learnable proposal boxes and features across different images, necessitating excessive iterations for the model to refine its predictions; the other is the inadequate exploitation of multi-scale information, leading to the sub-optimal detection performance. Thus, building upon Sparse R-CNN, we propose an efficient detector that incorporates dynamic prior and dynamic feature fusion, called $D^{2}$-Det. In particular, for the dynamic prior part, a prior information generator module dynamically generates proposal features and boxes as the dynamic prior for different images to alleviate the inference-inefficient iterative refinement process of predictions, and we further propose the class scores decoupling method to reduce the computation overhead. Furthermore, for the dynamic feature fusion part, we develop a novel lightweight multi-scale feature fusion module, which dynamically aggregates features from all layers for each proposal box, enabling adaptive feature fusion and improving detection precision by nearly 2 AP. Experiments show that $D^{2}$-Det can achieve 46.6 AP on COCO 2017 with fewer computations for the backbone ResNet50, surpassing most of the state-of-the-art detectors.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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