Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Zhong, Yiqi"'
Indoor robots rely on depth to perform tasks like navigation or obstacle detection, and single-image depth estimation is widely used to assist perception. Most indoor single-image depth prediction focuses less on model generalizability to unseen data
Externí odkaz:
http://arxiv.org/abs/2409.02486
Collaborative perception aims to mitigate the limitations of single-agent perception, such as occlusions, by facilitating data exchange among multiple agents. However, most current works consider a homogeneous scenario where all agents use identity s
Externí odkaz:
http://arxiv.org/abs/2401.13964
Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving. Current self-supervised methods mainly rely on point correspondences between point clouds, which may introdu
Externí odkaz:
http://arxiv.org/abs/2401.11499
Autor:
Ding, Tianyu, Chen, Tianyi, Zhu, Haidong, Jiang, Jiachen, Zhong, Yiqi, Zhou, Jinxin, Wang, Guangzhi, Zhu, Zhihui, Zharkov, Ilya, Liang, Luming
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains, reshaping the artificial general intelligence landscape. However, the increasing computational and memory demands of these models present subst
Externí odkaz:
http://arxiv.org/abs/2312.00678
Collaborative perception can substantially boost each agent's perception ability by facilitating communication among multiple agents. However, temporal asynchrony among agents is inevitable in the real world due to communication delays, interruptions
Externí odkaz:
http://arxiv.org/abs/2309.16940
A central challenge of video prediction lies where the system has to reason the objects' future motions from image frames while simultaneously maintaining the consistency of their appearances across frames. This work introduces an end-to-end trainabl
Externí odkaz:
http://arxiv.org/abs/2308.16154
Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series. However, the adversarial robustness of incremental learners has not been wi
Externí odkaz:
http://arxiv.org/abs/2305.18384
Model generalizability to unseen datasets, concerned with in-the-wild robustness, is less studied for indoor single-image depth prediction. We leverage gradient-based meta-learning for higher generalizability on zero-shot cross-dataset inference. Unl
Externí odkaz:
http://arxiv.org/abs/2305.07269
Vision-centric joint perception and prediction (PnP) has become an emerging trend in autonomous driving research. It predicts the future states of the traffic participants in the surrounding environment from raw RGB images. However, it is still a cri
Externí odkaz:
http://arxiv.org/abs/2303.09998
Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communication. It inevitably results in a fundamental trade-off between percepti
Externí odkaz:
http://arxiv.org/abs/2209.12836