4 papers
AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
Yanhao Wu, Haoyang Zhang, Fei He +6
Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes. While state-of-the-art (SOTA) methods u…
UniRefiner: Teaching Pre-trained ViTs to Self-Dispose Dross via Contrastive Register
Congpei Qiu, Zhaoyu Hu, Wei Ke +3
Representation learning with Vision Transformers (ViTs) has advanced rapidly, yet the utility of large-scale models in spatially sensitive tasks is hindered by spurious tokens. Pri…
Refining CLIP's Spatial Awareness: A Visual-Centric Perspective
Congpei Qiu, Yanhao Wu, Wei Ke +2
Contrastive Language-Image Pre-training (CLIP) excels in global alignment with language but exhibits limited sensitivity to spatial information, leading to strong performance in ze…
Generating Multimodal Driving Scenes via Next-Scene Prediction
Yanhao Wu, Haoyang Zhang, Tianwei Lin +6
Generative models in Autonomous Driving (AD) enable diverse scene creation, yet existing methods fall short by only capturing a limited range of modalities, restricting the capabil…