Zobrazeno 1 - 10
of 684
pro vyhledávání: '"Liu, Xianglong"'
Autor:
Gong, Ruihao, Ding, Yifu, Wang, Zining, Lv, Chengtao, Zheng, Xingyu, Du, Jinyang, Qin, Haotong, Guo, Jinyang, Magno, Michele, Liu, Xianglong
Large language models (LLMs) have achieved remarkable advancements in natural language processing, showcasing exceptional performance across various tasks. However, the expensive memory and computational requirements present significant challenges fo
Externí odkaz:
http://arxiv.org/abs/2409.16694
Autor:
Zhang, Tianyuan, Wang, Lu, Kang, Jiaqi, Zhang, Xinwei, Liang, Siyuan, Chen, Yuwei, Liu, Aishan, Liu, Xianglong
Recent advances in deep learning have markedly improved autonomous driving (AD) models, particularly end-to-end systems that integrate perception, prediction, and planning stages, achieving state-of-the-art performance. However, these models remain v
Externí odkaz:
http://arxiv.org/abs/2409.07321
Autor:
Xue, Yanni, Hao, Haojie, Wang, Jiakai, Sheng, Qiang, Tao, Renshuai, Liang, Yu, Feng, Pu, Liu, Xianglong
While neural machine translation (NMT) models achieve success in our daily lives, they show vulnerability to adversarial attacks. Despite being harmful, these attacks also offer benefits for interpreting and enhancing NMT models, thus drawing increas
Externí odkaz:
http://arxiv.org/abs/2409.05021
Modern Cardinality Estimators struggle with data updates. This research tackles this challenge within single-table. We introduce ICE, an Index-based Cardinality Estimator, the first data-driven estimator that enables instant, tuple-leveled updates. I
Externí odkaz:
http://arxiv.org/abs/2408.17209
Autor:
Liu, Aishan, Zhou, Yuguang, Liu, Xianglong, Zhang, Tianyuan, Liang, Siyuan, Wang, Jiakai, Pu, Yanjun, Li, Tianlin, Zhang, Junqi, Zhou, Wenbo, Guo, Qing, Tao, Dacheng
Large language models (LLMs) have transformed the development of embodied intelligence. By providing a few contextual demonstrations, developers can utilize the extensive internal knowledge of LLMs to effortlessly translate complex tasks described in
Externí odkaz:
http://arxiv.org/abs/2408.02882
The Diffusion models, widely used for image generation, face significant challenges related to their broad applicability due to prolonged inference times and high memory demands. Efficient Post-Training Quantization (PTQ) is crucial to address these
Externí odkaz:
http://arxiv.org/abs/2407.19547
Autor:
Tian, Shilong, Chen, Hong, Lv, Chengtao, Liu, Yu, Guo, Jinyang, Liu, Xianglong, Li, Shengxi, Yang, Hao, Xie, Tao
Recently, video diffusion models (VDMs) have garnered significant attention due to their notable advancements in generating coherent and realistic video content. However, processing multiple frame features concurrently, coupled with the considerable
Externí odkaz:
http://arxiv.org/abs/2407.11585
Autor:
Xiao, Yisong, Liu, Aishan, Cheng, QianJia, Yin, Zhenfei, Liang, Siyuan, Li, Jiapeng, Shao, Jing, Liu, Xianglong, Tao, Dacheng
Large Vision-Language Models (LVLMs) have been widely adopted in various applications; however, they exhibit significant gender biases. Existing benchmarks primarily evaluate gender bias at the demographic group level, neglecting individual fairness,
Externí odkaz:
http://arxiv.org/abs/2407.00600
The recent release of GPT-4o has garnered widespread attention due to its powerful general capabilities. While its impressive performance is widely acknowledged, its safety aspects have not been sufficiently explored. Given the potential societal imp
Externí odkaz:
http://arxiv.org/abs/2406.06302
Autor:
Ying, Zonghao, Liu, Aishan, Zhang, Tianyuan, Yu, Zhengmin, Liang, Siyuan, Liu, Xianglong, Tao, Dacheng
In the realm of large vision language models (LVLMs), jailbreak attacks serve as a red-teaming approach to bypass guardrails and uncover safety implications. Existing jailbreaks predominantly focus on the visual modality, perturbing solely visual inp
Externí odkaz:
http://arxiv.org/abs/2406.04031