Zobrazeno 1 - 10
of 1 434
pro vyhledávání: '"WU, Junfeng"'
Autor:
Wu, Junfeng, Jiang, Yi, Ma, Chuofan, Liu, Yuliang, Zhao, Hengshuang, Yuan, Zehuan, Bai, Song, Bai, Xiang
We present Liquid, an auto-regressive generation paradigm that seamlessly integrates visual comprehension and generation by tokenizing images into discrete codes and learning these code embeddings alongside text tokens within a shared feature space f
Externí odkaz:
http://arxiv.org/abs/2412.04332
This paper proposes an online environment poisoning algorithm tailored for reinforcement learning agents operating in a black-box setting, where an adversary deliberately manipulates training data to lead the agent toward a mischievous policy. In con
Externí odkaz:
http://arxiv.org/abs/2412.00797
Linear Convergence Analysis of Single-loop Algorithm for Bilevel Optimization via Small-gain Theorem
Bilevel optimization has gained considerable attention due to its broad applicability across various fields. While several studies have investigated the convergence rates in the strongly-convex-strongly-convex (SC-SC) setting, no prior work has prove
Externí odkaz:
http://arxiv.org/abs/2412.00659
Autor:
Sheng, Wenliang, Zhao, Hongxu, Chen, Lingpeng, Zeng, Guangyang, Shao, Yunling, Hong, Yuze, Yang, Chao, Hong, Ziyang, Wu, Junfeng
We consider the acoustic-n-point (AnP) problem, which estimates the pose of a 2D forward-looking sonar (FLS) according to n 3D-2D point correspondences. We explore the nature of the measured partial spherical coordinates and reveal their inherent rel
Externí odkaz:
http://arxiv.org/abs/2411.17521
The allyl radical (C3H5) is a well-characterized hydrocarbon radical, renowned for its pivotal role as an intermediate species in high-energy environments. Its core excited states can elucidate intricate details pertaining to its electronic and struc
Externí odkaz:
http://arxiv.org/abs/2409.15032
Cooperative localization and target tracking are essential for multi-robot systems to implement high-level tasks. To this end, we propose a distributed invariant Kalman filter based on covariance intersection for effective multi-robot pose estimation
Externí odkaz:
http://arxiv.org/abs/2409.09410
This paper presents a novel approach to distributed pose estimation in the multi-agent system based on an invariant Kalman filter with covariance intersection. Our method models uncertainties using Lie algebra and applies object-level observations wi
Externí odkaz:
http://arxiv.org/abs/2409.07933
Neural Radiance Fields (NeRF) have revolutionized 3D computer vision and graphics, facilitating novel view synthesis and influencing sectors like extended reality and e-commerce. However, NeRF's dependence on extensive data collection, including sens
Externí odkaz:
http://arxiv.org/abs/2409.01661
We present PartGLEE, a part-level foundation model for locating and identifying both objects and parts in images. Through a unified framework, PartGLEE accomplishes detection, segmentation, and grounding of instances at any granularity in the open wo
Externí odkaz:
http://arxiv.org/abs/2407.16696
Autor:
Zeng, Guangyang, Mu, Biqiang, Zeng, Qingcheng, Song, Yuchen, Dai, Chulin, Shi, Guodong, Wu, Junfeng
Camera pose estimation is a fundamental problem in robotics. This paper focuses on two issues of interest: First, point and line features have complementary advantages, and it is of great value to design a uniform algorithm that can fuse them effecti
Externí odkaz:
http://arxiv.org/abs/2407.16151