Zobrazeno 1 - 10
of 2 416
pro vyhledávání: '"Dual-Encoder"'
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder caused by upper airway blockage, leading to oxygen deprivation and disrupted sleep. Traditional diagnosis using polysomnography (PSG) is expensive, time-consuming, and uncom
Externí odkaz:
http://arxiv.org/abs/2412.18919
Autor:
Ki, Juhyeong1 (AUTHOR), Lee, Jung Mok2 (AUTHOR), Lee, Wonjin3 (AUTHOR), Kim, Jin Ho4 (AUTHOR), Jin, Hyeongmin4 (AUTHOR), Jung, Seongmoon4,5 (AUTHOR) smjung@kriss.re.kr, Lee, Jimin1,6 (AUTHOR) jiminlee@unist.ac.kr
Publikováno v:
Scientific Reports. 11/14/2024, Vol. 12 Issue 1, p1-11. 11p.
In this study, we introduce CIKMar, an efficient approach to educational dialogue systems powered by the Gemma Language model. By leveraging a Dual-Encoder ranking system that incorporates both BERT and SBERT model, we have designed CIKMar to deliver
Externí odkaz:
http://arxiv.org/abs/2408.08805
3D GAN inversion aims to project a single image into the latent space of a 3D Generative Adversarial Network (GAN), thereby achieving 3D geometry reconstruction. While there exist encoders that achieve good results in 3D GAN inversion, they are predo
Externí odkaz:
http://arxiv.org/abs/2409.20530
Legal Judgment Prediction (LJP) aims to form legal judgments based on the criminal fact description. However, researchers struggle to classify confusing criminal cases, such as robbery and theft, which requires LJP models to distinguish the nuances b
Externí odkaz:
http://arxiv.org/abs/2408.09717
Accurate behavior prediction for vehicles is essential but challenging for autonomous driving. Most existing studies show satisfying performance under regular scenarios, but most neglected safety-critical scenarios. In this study, a spatio-temporal d
Externí odkaz:
http://arxiv.org/abs/2408.01774
Advancements in deep learning and voice-activated technologies have driven the development of human-vehicle interaction. Distributed microphone arrays are widely used in in-car scenarios because they can accurately capture the voices of passengers fr
Externí odkaz:
http://arxiv.org/abs/2409.08610
Autor:
Cheng, Jiacheng, Shin, Hijung Valentina, Vasconcelos, Nuno, Russell, Bryan, Heilbron, Fabian Caba
In the recent years, the dual-encoder vision-language models (\eg CLIP) have achieved remarkable text-to-image retrieval performance. However, we discover that these models usually results in very different retrievals for a pair of paraphrased querie
Externí odkaz:
http://arxiv.org/abs/2405.03190
Autor:
Kharbanda, Siddhant, Gupta, Devaansh, K, Gururaj, Malhotra, Pankaj, Hsieh, Cho-Jui, Babbar, Rohit
Extreme Multi-label Classification (XMC) involves predicting a subset of relevant labels from an extremely large label space, given an input query and labels with textual features. Models developed for this problem have conventionally used modular ap
Externí odkaz:
http://arxiv.org/abs/2405.03714
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