Zobrazeno 1 - 10
of 206 170
pro vyhledávání: '"A P, Worth"'
Recent advances in Retrieval-Augmented Generation (RAG) systems have popularized semantic chunking, which aims to improve retrieval performance by dividing documents into semantically coherent segments. Despite its growing adoption, the actual benefi
Externí odkaz:
http://arxiv.org/abs/2410.13070
Autor:
Abdulaal, Ahmed, Fry, Hugo, Montaña-Brown, Nina, Ijishakin, Ayodeji, Gao, Jack, Hyland, Stephanie, Alexander, Daniel C., Castro, Daniel C.
Radiological services are experiencing unprecedented demand, leading to increased interest in automating radiology report generation. Existing Vision-Language Models (VLMs) suffer from hallucinations, lack interpretability, and require expensive fine
Externí odkaz:
http://arxiv.org/abs/2410.03334
Accurate traffic prediction faces significant challenges, necessitating a deep understanding of both temporal and spatial cues and their complex interactions across multiple variables. Recent advancements in traffic prediction systems are primarily d
Externí odkaz:
http://arxiv.org/abs/2409.17440
Merging datasets across institutions is a lengthy and costly procedure, especially when it involves private information. Data hosts may therefore want to prospectively gauge which datasets are most beneficial to merge with, without revealing sensitiv
Externí odkaz:
http://arxiv.org/abs/2409.07215
The performance of deep neural networks scales with dataset size and label quality, rendering the efficient mitigation of low-quality data annotations crucial for building robust and cost-effective systems. Existing strategies to address label noise
Externí odkaz:
http://arxiv.org/abs/2408.14358
Material characterization using electron micrographs is a crucial but challenging task with applications in various fields, such as semiconductors, quantum materials, batteries, etc. The challenges in categorizing electron micrographs include but are
Externí odkaz:
http://arxiv.org/abs/2408.11351
Autor:
Liu, Jinming, Wei, Yuntao, Lin, Junyan, Zhao, Shengyang, Sun, Heming, Chen, Zhibo, Zeng, Wenjun, Jin, Xin
We present a new image compression paradigm to achieve ``intelligently coding for machine'' by cleverly leveraging the common sense of Large Multimodal Models (LMMs). We are motivated by the evidence that large language/multimodal models are powerful
Externí odkaz:
http://arxiv.org/abs/2408.08575
This work addresses the challenge of quantifying originality in text-to-image (T2I) generative diffusion models, with a focus on copyright originality. We begin by evaluating T2I models' ability to innovate and generalize through controlled experimen
Externí odkaz:
http://arxiv.org/abs/2408.08184
Autor:
Zhao, Penghai, Xing, Qinghua, Dou, Kairan, Tian, Jinyu, Tai, Ying, Yang, Jian, Cheng, Ming-Ming, Li, Xiang
As the academic landscape expands, the challenge of efficiently identifying potentially high-impact articles among the vast number of newly published works becomes critical. This paper introduces a promising approach, leveraging the capabilities of f
Externí odkaz:
http://arxiv.org/abs/2408.03934
We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively convert
Externí odkaz:
http://arxiv.org/abs/2408.03178