We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| yaorong0921/driver-gaze-yolov5 | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| bb_mapping | Ran | yaorong0921/driver-gaze-yolov5/gaze_prediction_and_evaluation.py code served (permissive licence) · get_code("6fe32119c78ca119") |
| validate | Ran | yaorong0921/driver-gaze-yolov5/gaze_prediction_and_evaluation.py code served (permissive licence) · get_code("45b4fbe687e167bd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Human drivers use their attentional mechanisms to focus on critical objects and make decisions while driving. As human attention can be revealed from gaze data, capturing and analyzing gaze information has emerged in recent years to benefit autonomous driving technology. Previous works in this context have primarily aimed at predicting "where" human drivers look at and lack knowledge of "what" objects drivers focus on. Our work bridges the gap between pixel-level and object-level attention prediction. Specifically, we propose to integrate an attention prediction module into a pretrained object detection framework and predict the attention in a grid-based style. Furthermore, critical objects are recognized based on predicted attended-to areas. We evaluate our proposed method on two driver attention datasets, BDD-A and DR(eye)VE. Our framework achieves competitive state-of-the-art performance in the attention prediction on both pixel-level and object-level but is far more efficient (75.3 GFLOPs less) in computation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2204.12150")
get_code_for_paper("2204.12150")
have("2204.12150")
Connect an agent — have() is free.