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Found 6 papers, 6 papers with code
End-to-End Driving with Online Trajectory Evaluation via BEV World Model
Therefore, we propose an end-to-end driving framework WoTE, which leverages a BEV World model to predict future BEV states for Trajectory Evaluation.
Enhancing End-to-End Autonomous Driving with Latent World Model
Specifically, our framework \textbf{LAW} uses a LAtent World model to predict future latent features based on the predicted ego actions and the latent feature of the current frame.
Fully Sparse Fusion for 3D Object Detection
In this paper, we study how to effectively leverage image modality in the emerging fully sparse architecture.
Densely Constrained Depth Estimator for Monocular 3D Object Detection
So these methods only use a small number of projection constraints and produce insufficient depth candidates, leading to inaccurate depth estimation.
B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrieval
The basic idea of PROP is to construct the \textit{representative words prediction} (ROP) task for pre-training inspired by the query likelihood model.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image Retrieval
on RUC-CAS-WenLan
