Most existing RGB-based trackers target low frame rate benchmarks of aro...
Synthesizing novel views from a single view image is a highly ill-posed
...
HD map reconstruction is crucial for autonomous driving. LiDAR-based met...
Deep reinforcement learning has achieved great success in laser-based
co...
Crowd image is arguably one of the most laborious data to annotate. In t...
In this paper, we aim to forecast a future trajectory distribution of a
...
Conventional domain generalization aims to learn domain invariant
repres...
Ultra-high resolution image segmentation has raised increasing interests...
Deep reinforcement learning has achieved great success in laser-based
co...
Navigation in dense crowds is a well-known open problem in robotics with...
In the era of big data, a large number of text data generated by the Int...
We propose an unsupervised learning framework with the pretext task of
f...
Synthesizing high dynamic range (HDR) images from multiple low-dynamic r...
In recent years, with the progress of deep learning technologies, crowd
...
In recent years, vision-based crowd analysis has been studied extensivel...
Deep reinforcement learning has great potential to acquire complex, adap...
In this paper, we propose a novel iterative multi-task framework to comp...
Detection and segmentation of the hippocampal structures in volumetric b...
We aim to enable a mobile robot to navigate through environments with de...
The tracking-by-detection framework receives growing attentions through ...
In recent years, crowd analysis is important for applications such as sm...
In this paper, we present a decentralized sensor-level collision avoidan...
Developing a safe and efficient collision avoidance policy for multiple
...
High-speed, low-latency obstacle avoidance that is insensitive to sensor...
In this paper, we present a novel method to recognize the types of crowd...
We present a multiple-person tracking algorithm, based on combining part...