Research map / Detection and segmentation / CNN features
"Looking at the Right Stuff" - Guided Semantic-Gaze for Autonomous Driving
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train DREYEVE on SAGE gt instead of gaze gt | DREYEVE · BDD-A | D_KL: 1.28$\pm$0.43 -> 0.73$\pm$0.38 | Helped |
| Train DREYEVE on SAGE gt instead of gaze gt | DREYEVE · BDD-A | F1 score: 0.1$\pm$0.06 -> 0.37$\pm$0.14 | Helped |
| Train DREYEVE on SAGE gt instead of gaze gt | DREYEVE · BDD-A | CC: 0.58$\pm$0.13 -> 0.75$\pm$0.13 | Helped |
| Train DREYEVE on SAGE gt instead of gaze gt | DREYEVE · BDD-A | MAE: 0.11$\pm$0.06 -> 0.08$\pm$0.05 | Helped |
| Train DREYEVE on SAGE gt instead of gaze gt | DREYEVE · JAAD pedestrian crossing at intersections | D_KL: 3.36$\pm$0.76 -> 1.56$\pm$0.62 | Helped |
| Train DREYEVE on SAGE gt instead of gaze gt | DREYEVE · JAAD pedestrian crossing at intersections | F1 score: 0.07$\pm$0.06 -> 0.21$\pm$0.09 | Helped |
| Train DREYEVE on SAGE gt instead of gaze gt | DREYEVE · Cars approaching ego-vehicle | D_KL: 3.87$\pm$0.79 -> 1.28$\pm$0.71 | Helped |
| Train DREYEVE on SAGE gt instead of gaze gt | DREYEVE · Cars approaching ego-vehicle | F1 score: 0.08$\pm$0.08 -> 0.33$\pm$0.16 | Helped |
| Train DREYEVE on SAGE gt instead of gaze gt | DREYEVE · Cars approaching ego-vehicle | CC: 0.18$\pm$0.11 -> 0.62$\pm$0.19 | Helped |
Closest papers by meaning
- SalM²: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention (AAAI 2025 · similarity 0.87)
- What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation (CVPR 2024 · similarity 0.87)
- Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction (ICLR 2025 · similarity 0.86)
- FIFA: Fine-grained Inter-frame Attention for Driver's Video Gaze Estimation (CVPR 2025 · similarity 0.86)
- Prune2Drive: A Plug-and-Play Framework for Accelerating Vision-Language Models in Autonomous Driving (CVPR 2026 · similarity 0.85)
- Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection (AAAI 2024 · similarity 0.85)
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