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
Fusion techniques are frequently utilized in the realm of multimodal object detection tasks. While many current studies showcase their proficiency in generating visually pleasing fused images, only a limited number of them focused on the object detection performance. This study addresses the issue by presenting an end-to-end framework for object detection through the fusion of visible and infrared features (VIFF). Specifically, our approach involves the use of two distinct processing units that independently extract features from visible and infrared images, followed by the fusion of these features using a novel fusion strategy. While the visible feature processing unit preserves the direction of the gradient of visible images, the infrared feature processing unit focuses on extracting the contrast and semantic features of infrared images. Both features are aggregated by attention mechanisms and then fed into the backbone of the object detection networks. Our fusion network achieved superior object detection accuracy compared to existing state-of-the-art approaches on various datasets. We have also demonstrated that the proposed visible feature and infrared feature processing units are capable of enhancing the performance of various object detection models.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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