Zobrazeno 1 - 10
of 46 297
pro vyhledávání: '"CHEN, Hao"'
Autor:
Wang, Wen, Wang, Qiuyu, Zheng, Kecheng, Ouyang, Hao, Chen, Zhekai, Gong, Biao, Chen, Hao, Shen, Yujun, Shen, Chunhua
We propose Framer for interactive frame interpolation, which targets producing smoothly transitioning frames between two images as per user creativity. Concretely, besides taking the start and end frames as inputs, our approach supports customizing t
Externí odkaz:
http://arxiv.org/abs/2410.18978
Models based on human-understandable concepts have received extensive attention to improve model interpretability for trustworthy artificial intelligence in the field of medical image analysis. These methods can provide convincing explanations for mo
Externí odkaz:
http://arxiv.org/abs/2410.15446
Deep hashing, due to its low cost and efficient retrieval advantages, is widely valued in cross-modal retrieval. However, existing cross-modal hashing methods either explore the relationships between data points, which inevitably leads to intra-class
Externí odkaz:
http://arxiv.org/abs/2410.15387
Autor:
Chen, Hao, Waheed, Abdul, Li, Xiang, Wang, Yidong, Wang, Jindong, Raj, Bhiksha, Abdin, Marah I.
The rise of Large Language Models (LLMs) has accentuated the need for diverse, high-quality pre-training data. Synthetic data emerges as a viable solution to the challenges of data scarcity and inaccessibility. While previous literature has focused p
Externí odkaz:
http://arxiv.org/abs/2410.15226
The demand for structured light with a reconfigurable spatial and polarization distribution has been increasing across a wide range of fundamental and advanced photonics applications, including microscopy, imaging, sensing, communications, and quantu
Externí odkaz:
http://arxiv.org/abs/2410.15172
The cold start problem in recommender systems remains a critical challenge. Current solutions often train hybrid models on auxiliary data for both cold and warm users/items, potentially degrading the experience for the latter. This drawback limits th
Externí odkaz:
http://arxiv.org/abs/2410.14241
Autor:
Li, Xinze, Mei, Sen, Liu, Zhenghao, Yan, Yukun, Wang, Shuo, Yu, Shi, Zeng, Zheni, Chen, Hao, Yu, Ge, Liu, Zhiyuan, Sun, Maosong, Xiong, Chenyan
Retrieval-Augmented Generation (RAG) has proven its effectiveness in mitigating hallucinations in Large Language Models (LLMs) by retrieving knowledge from external resources. To adapt LLMs for RAG pipelines, current approaches use instruction tuning
Externí odkaz:
http://arxiv.org/abs/2410.13509
Autor:
Chen, Hao Mark, Tan, Fuwen, Kouris, Alexandros, Lee, Royson, Fan, Hongxiang, Venieris, Stylianos I.
In spite of the great potential of large language models (LLMs) across various tasks, their deployment on resource-constrained devices remains challenging due to their excessive computational and memory demands. Quantization has emerged as an effecti
Externí odkaz:
http://arxiv.org/abs/2410.13461
Autor:
Zhou, Donghao, Huang, Jiancheng, Bai, Jinbin, Wang, Jiaze, Chen, Hao, Chen, Guangyong, Hu, Xiaowei, Heng, Pheng-Ann
Recent advancements in text-to-image (T2I) diffusion models have enabled the creation of high-quality images from text prompts, but they still struggle to generate images with precise control over specific visual concepts. Existing approaches can rep
Externí odkaz:
http://arxiv.org/abs/2410.13370
Autor:
Li, Chenglin, Chen, Qianglong, Li, Zhi, Tao, Feng, Li, Yicheng, Chen, Hao, Yu, Fei, Zhang, Yin
Instruction tuning is a crucial technique for aligning language models with humans' actual goals in the real world. Extensive research has highlighted the quality of instruction data is essential for the success of this alignment. However, creating h
Externí odkaz:
http://arxiv.org/abs/2410.10392