Guan Huang

TL;DR: The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative; results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years.

TL;DR: BEVDet4D with robust generalization performance reduces the velocity error and makes the vision-based methods, for the first time, become comparable with those relied on LiDAR or radar in this aspect.

TL;DR: It is shown that the multi-task BEVerse outperforms existing single-task methods on 3D object detection, semantic map construction, and motion prediction, and with extensive experiments on the nuScenes dataset, it also favors in significantly improved ef ficiency.

TL;DR: This paper proposes a novel scheme to Condense dataset by Aligning FEatures (CAFE), which explicitly attempts to preserve the real-feature distribution as well as the discriminant power of the resulting synthetic set, lending itself to strong generalization capability to various architectures.

TL;DR: MVSTER is presented, which leverages the proposed epipolar Transformer to learn both 2D semantics and 3D spatial associations efficiently and is built in a cascade structure, where entropy-regularized optimal transport is leveraged to propagate finer depth estimations in each stage.