Zobrazeno 1 - 10
of 11 897
pro vyhledávání: '"Ma, Lin"'
Autor:
Huang, Yiyang, Hao, Yuhui, Yu, Bo, Yan, Feng, Yang, Yuxin, Min, Feng, Han, Yinhe, Ma, Lin, Liu, Shaoshan, Liu, Qiang, Gan, Yiming
Embodied AI robots have the potential to fundamentally improve the way human beings live and manufacture. Continued progress in the burgeoning field of using large language models to control robots depends critically on an efficient computing substra
Externí odkaz:
http://arxiv.org/abs/2407.04292
Multimodal Large Language Models (MLLM) have made significant progress in the field of document analysis. Despite this, existing benchmarks typically focus only on extracting text and simple layout information, neglecting the complex interactions bet
Externí odkaz:
http://arxiv.org/abs/2407.02842
Utilizing Vision-Language Models (VLMs) for robotic manipulation represents a novel paradigm, aiming to enhance the model's ability to generalize to new objects and instructions. However, due to variations in camera specifications and mounting positi
Externí odkaz:
http://arxiv.org/abs/2406.18977
Video representation is a long-standing problem that is crucial for various down-stream tasks, such as tracking,depth prediction,segmentation,view synthesis,and editing. However, current methods either struggle to model complex motions due to the abs
Externí odkaz:
http://arxiv.org/abs/2406.13870
Amidst the advancements in image-based Large Vision-Language Models (image-LVLM), the transition to video-based models (video-LVLM) is hindered by the limited availability of quality video data. This paper addresses the challenge by leveraging the vi
Externí odkaz:
http://arxiv.org/abs/2406.08024
Autor:
Zhang, Zhan, Zhang, Qin, Jiao, Yang, Lu, Lin, Ma, Lin, Liu, Aihua, Liu, Xiao, Zhao, Juan, Xue, Yajun, Wei, Bing, Zhang, Mingxia, Gao, Ru, Zhao, Hong, Lu, Jie, Li, Fan, Zhang, Yang, Wang, Yiming, Zhang, Lei, Tian, Fengwei, Hu, Jie, Gou, Xin
Publikováno v:
Artificaial Intelligence Review, (2024) 57:151
AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The recently developed Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, a
Externí odkaz:
http://arxiv.org/abs/2406.05746
Based on the slopes between DESI $g,r$ and IRAS 100 $\mu m$ intensities, specifically $k_{g}$ and $k_{r}$, we have constructed a substantial sample of Galactic cirri. This sample covers 561.25 deg$^2$ at high Galactic latitudes (|b| $\geq$ 30$^{\circ
Externí odkaz:
http://arxiv.org/abs/2406.03031
Powered by massive curated training data, Segment Anything Model (SAM) has demonstrated its impressive generalization capabilities in open-world scenarios with the guidance of prompts. However, the vanilla SAM is class agnostic and heavily relies on
Externí odkaz:
http://arxiv.org/abs/2406.00480
Publikováno v:
Journal of Student Research, Volume 10, Issue 1, 2021
This paper examines the association between police drug seizures and drug overdose deaths in Ohio from 2014 to 2018. We use linear regression, ARIMA models, and categorical data analysis to quantify the effect of drug seizure composition and weight o
Externí odkaz:
http://arxiv.org/abs/2405.19199
TIE: Revolutionizing Text-based Image Editing for Complex-Prompt Following and High-Fidelity Editing
As the field of image generation rapidly advances, traditional diffusion models and those integrated with multimodal large language models (LLMs) still encounter limitations in interpreting complex prompts and preserving image consistency pre and pos
Externí odkaz:
http://arxiv.org/abs/2405.16803