Abstract Large Language Models (LLMs) have achieved remarkable success with their
billion-level parameters, yet they incur high inference overheads. The emergence of …

Textual prompt tuning has demonstrated significant performance improvements in adapting
natural language processing models to a variety of downstream tasks by treating hand …

Due to the lack of depth cues in images, multi-frame inputs are important for the success of
vision-based perception, prediction, and planning in autonomous driving. Observations from …

Panoptic perception represents a forefront advancement in autonomous driving technology,
unifying multiple perception tasks into a singular, cohesive framework to facilitate a thorough …

Recent self-supervised clustering-based pre-training techniques like DINO and Cribo have
shown impressive results for downstream detection and segmentation tasks. However, real …

State-of-the-art 3D object detectors are often trained on massive labeled datasets. However,
annotating 3D bounding boxes remains prohibitively expensive and time-consuming …

State-of-the-art 3D object detectors are often trained on massive labeled datasets. However,
annotating 3D bounding boxes remains prohibitively expensive and time-consuming …

Unsupervised 3D representation learning via masked-and-reconstruction with differentiable
rendering is promising to reduce the labeling burden for fusion 3D perception. However …

Constructing large-scale labeled datasets for multi-modal perception model training in
autonomous driving presents significant challenges. This has motivated the development of …

LiDAR-based 3D object detectors have been largely utilized in various applications,
including autonomous vehicles or mobile robots. However, LiDAR-based detectors often fail …