Zobrazeno 1 - 10
of 337
pro vyhledávání: '"Li, Xinzhe"'
Autor:
Yang, Zhaotong, Jiang, Zicheng, Li, Xinzhe, Zhou, Huiyu, Dong, Junyu, Zhang, Huaidong, Du, Yong
In this paper, we introduce D$^4$-VTON, an innovative solution for image-based virtual try-on. We address challenges from previous studies, such as semantic inconsistencies before and after garment warping, and reliance on static, annotation-driven c
Externí odkaz:
http://arxiv.org/abs/2407.15111
Autor:
Li, Xinzhe
Tool use, planning, and feedback learning are currently three prominent paradigms for developing Large Language Model (LLM)-based agents across various tasks. Although numerous frameworks have been devised for each paradigm, their intricate workflows
Externí odkaz:
http://arxiv.org/abs/2406.05804
Autor:
Li, Xinzhe, Liu, Ming
Over the last decade, a wide range of training and deployment strategies for Large Language Models (LLMs) have emerged. Among these, the prompting paradigms of Auto-regressive LLMs (AR-LLMs) have catalyzed a significant surge in Artificial Intelligen
Externí odkaz:
http://arxiv.org/abs/2405.10474
Retrieval-Augmented Generation (RAG) systems are widely used across various industries for querying closed-domain and in-house knowledge bases. However, evaluating these systems presents significant challenges due to the private nature of closed-doma
Externí odkaz:
http://arxiv.org/abs/2404.19232
Autor:
Du, Yong, Zhan, Jiahui, He, Shengfeng, Li, Xinzhe, Dong, Junyu, Chen, Sheng, Yang, Ming-Hsuan
In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences. The main idea behind our approach is
Externí odkaz:
http://arxiv.org/abs/2310.14222
Publikováno v:
ACL2023 Third Workshop on Trustworthy Natural Language Processing
This paper addresses the ethical concerns arising from the use of unauthorized public data in deep learning models and proposes a novel solution. Specifically, building on the work of Huang et al. (2021), we extend their bi-level optimization approac
Externí odkaz:
http://arxiv.org/abs/2307.00456
Publikováno v:
SEM2023 Co-located with ACL 2023
For Pretrained Language Models (PLMs), their susceptibility to noise has recently been linked to subword segmentation. However, it is unclear which aspects of segmentation affect their understanding. This study assesses the robustness of PLMs against
Externí odkaz:
http://arxiv.org/abs/2306.15268
Publikováno v:
IJCAI-2023
Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. However, a comprehensive, integrated discussion of the three research lines
Externí odkaz:
http://arxiv.org/abs/2306.15261
Publikováno v:
Journal of Hospitality and Tourism Technology, 2024, Vol. 15, Issue 4, pp. 534-550.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JHTT-09-2023-0255
Autor:
Fang, Hanyan, Mahalingam, Harshitra, Li, Xinzhe, Han, Xu, Qiu, Zhizhan, Han, Yixuan, Noori, Keian, Dulal, Dikshant, Chen, Hongfei, Lyu, Pin, Yang, Tianhao, Li, Jing, Su, Chenliang, Chen, Wei, Cai, Yongqing, Neto, Antonio Castro H., Novoselov, Kostya S., Rodin, Aleksandr, Lu, Jiong
Patterning antidots ("voids") into well-defined antidot lattices creates an intriguing class of artificial structures for the periodic modulation of 2D electron systems, leading to anomalous transport properties and exotic quantum phenomena as well a
Externí odkaz:
http://arxiv.org/abs/2305.04088