Zobrazeno 1 - 10
of 4 832
pro vyhledávání: '"Zhang, Yiming"'
Despite much progress in large 3D datasets there are currently few interactive 3D object datasets, and their scale is limited due to the manual effort required in their construction. We introduce the static to openable (S2O) task which creates intera
Externí odkaz:
http://arxiv.org/abs/2409.18896
Autor:
Zhang, Yiming, Chi, Jianfeng, Nguyen, Hailey, Upasani, Kartikeya, Bikel, Daniel M., Weston, Jason, Smith, Eric Michael
Text generation has a fundamental limitation almost by definition: there is no taking back tokens that have been generated, even when they are clearly problematic. In the context of language model safety, when a partial unsafe generation is produced,
Externí odkaz:
http://arxiv.org/abs/2409.14586
Autor:
Zhang, Chuqi, Zeng, Jun, Zhang, Yiming, Ahmad, Adil, Zhang, Fengwei, Jin, Hai, Liang, Zhenkai
Protecting system observability records (logs) from compromised OSs has gained significant traction in recent times, with several note-worthy approaches proposed. Unfortunately, none of the proposed approaches achieve high performance with tiny log p
Externí odkaz:
http://arxiv.org/abs/2409.04484
Due to high accuracy, BERT-like models have been widely adopted by discriminative text mining and web searching. However, large BERT-like models suffer from inefficient online inference, as they face the following two problems on GPUs. First, they re
Externí odkaz:
http://arxiv.org/abs/2408.12526
Cracking Elements Method (CEM) is a numerical tool to simulate quasi-brittle fractures, which does not need remeshing, nodal enrichment, or complicated crack tracking strategy. The cracking elements used in the CEM can be considered as a special type
Externí odkaz:
http://arxiv.org/abs/2407.17104
Autor:
Zhang, Yiming, Gu, Yicheng, Zeng, Yanhong, Xing, Zhening, Wang, Yuancheng, Wu, Zhizheng, Chen, Kai
We study Neural Foley, the automatic generation of high-quality sound effects synchronizing with videos, enabling an immersive audio-visual experience. Despite its wide range of applications, existing approaches encounter limitations when it comes to
Externí odkaz:
http://arxiv.org/abs/2407.01494
We introduce Duoduo CLIP, a model for 3D representation learning that learns shape encodings from multi-view images instead of point-clouds. The choice of multi-view images allows us to leverage 2D priors from off-the-shelf CLIP models to facilitate
Externí odkaz:
http://arxiv.org/abs/2406.11579
Autor:
Zhang, Yiming, Xu, Xuenan, Du, Ruoyi, Liu, Haohe, Dong, Yuan, Tan, Zheng-Hua, Wang, Wenwu, Ma, Zhanyu
In traditional audio captioning methods, a model is usually trained in a fully supervised manner using a human-annotated dataset containing audio-text pairs and then evaluated on the test sets from the same dataset. Such methods have two limitations.
Externí odkaz:
http://arxiv.org/abs/2406.06295
Prompt recovery in large language models (LLMs) is crucial for understanding how LLMs work and addressing concerns regarding privacy, copyright, etc. The trend towards inference-only APIs complicates this task by restricting access to essential outpu
Externí odkaz:
http://arxiv.org/abs/2405.20657
Human-Lead Cooperative Adaptive Cruise Control (HL-CACC) is regarded as a promising vehicle platooning technology in real-world implementation. By utilizing a Human-driven Vehicle (HV) as the platoon leader, HL-CACC reduces the cost and enhances the
Externí odkaz:
http://arxiv.org/abs/2405.07556