TFIENet: Transformer Fusion Information Enhancement Network for Multimodel 3-D Object Detection
Bibliographic record
Abstract
During feature-level data fusion in 3-D object detection, the correlation between different modal data is destroyed by the misalignment problem, which leads to inaccurate localization of small targets at long distances. For the problem, a transformer fusion information enhancement network (TFIENet) is proposed. First, the original point cloud and color images are taken as input. Besides, the standard backbone network of feature extraction is passed to obtain LiDAR point cloud features and image features, respectively. Second, a region proposal network of transformer dual-fusion features is designed, which uses a deformable transformer-decoder to double fuse the extracted LiDAR point cloud features and image features based on a deformed attention mechanism. Moreover, the dual-domain feature information of the LiDAR camera is aggregated to generate the initial candidate frames. Then, the enhancement module of feature information is used to further refine the frame, which predicts the dense depth feature information using a depth complementation mechanism. The corresponding dense depth information and feature semantic information are extracted to complete the box refinement. Finally, for aligning and fusing feature information from different modalities effectively, a multimodal feature cross-attention module (MFCAM) is designed. Moreover, a dynamic cross-attention mechanism is applied to obtain the correlation between different modalities. Experimental results on the KITTI, NuScenes, and Waymo datasets demonstrate the generality and effectiveness of the proposed TFIENet method. Extensive ablation experiments demonstrate the efficiency of each individual module. Experimental results on a real road dataset show that the TFIENet algorithm has strong robustness in complex real road environments.
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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 | 0.000 | 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) | 0.002 | 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".