TransGaze-Object: Transformer Based Driver Gaze Object Prediction Framework in Real Driving
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
ObjectGuidedMap introduces a multitask architecture that jointly predicts bird's eye view (BEV) maps and 3D objects, enabling explicit information exchange between the two tasks. By adding an object detection branch and cross‑task feature fusion through deformable attention and cross‑attention, the model allows map instances to leverage object context while remaining robust to occlusions. Experiments on nuScenes and Argoverse 2 show consistent improvements in BEV map prediction accuracy, especially a 21% mAP gain in heavily occluded scenes.
Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexplored. In this work, we study the decoding of pedestrian crossing intentions from short egocentric video clips.
arXiv:2211.11062v2 Announce Type: replace Abstract: Gaze following aims to predict where a person is looking in a scene, by predicting the target location, or indicating that the target is located ou...
arXiv:2610.07783v1 Announce Type: new Abstract: Bird's-eye-view (BEV) representations have become a widely used interface between perception and planning in autonomous driving, but they encode what i...
arXiv:2608. 11367v1 Announce Type: cross Abstract: Estimating human gaze targets from images in-the-wild is an important and formidable task.
arXiv:2606. 09142v1 Announce Type: cross Abstract: Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexplored.