Zobrazeno 1 - 10
of 2 217
pro vyhledávání: '"Ross, David P."'
Autor:
Gerblich, Lasse, Dasanjh, Tamanna, Wong, Horatio Q. X., Ross, David, Novo, Leonardo, Chancellor, Nicholas, Kendon, Viv
Methods to find the solution state for optimization problems encoded into Ising Hamiltonians are a very active area of current research. In this work we compare the quantum approximate optimization algorithm (QAOA) with multi-stage quantum walks (MSQ
Externí odkaz:
http://arxiv.org/abs/2407.06663
Autor:
Ross, David A.
Short nonstandard proofs are given for some results about infinite systems of equations in infinitely many variables.
Externí odkaz:
http://arxiv.org/abs/2405.04552
Autor:
Nathanson, Melvyn B., Ross, David A.
This paper describes infinite sets of polynomial equations in infinitely many variables with the property that the existence of a solution or even an approximate solution for every finite subset of the equations implies the existence of a solution fo
Externí odkaz:
http://arxiv.org/abs/2405.01766
Autor:
Chowdhury, Townim Faisal, Liao, Kewen, Phan, Vu Minh Hieu, To, Minh-Son, Xie, Yutong, Hung, Kevin, Ross, David, Hengel, Anton van den, Verjans, Johan W., Liao, Zhibin
Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability. Class activation maps (CAMs) and recent variants provide ways t
Externí odkaz:
http://arxiv.org/abs/2404.02388
Autor:
Hu, Ziniu, Iscen, Ahmet, Jain, Aashi, Kipf, Thomas, Yue, Yisong, Ross, David A., Schmid, Cordelia, Fathi, Alireza
This paper introduces SceneCraft, a Large Language Model (LLM) Agent converting text descriptions into Blender-executable Python scripts which render complex scenes with up to a hundred 3D assets. This process requires complex spatial planning and ar
Externí odkaz:
http://arxiv.org/abs/2403.01248
Autor:
Zhao, Long, Gundavarapu, Nitesh B., Yuan, Liangzhe, Zhou, Hao, Yan, Shen, Sun, Jennifer J., Friedman, Luke, Qian, Rui, Weyand, Tobias, Zhao, Yue, Hornung, Rachel, Schroff, Florian, Yang, Ming-Hsuan, Ross, David A., Wang, Huisheng, Adam, Hartwig, Sirotenko, Mikhail, Liu, Ting, Gong, Boqing
We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model. We pretrain VideoPrism on a heterogeneous corpus containing 36M high-quality video-caption pairs and 582M video clips
Externí odkaz:
http://arxiv.org/abs/2402.13217
Autor:
Kondratyuk, Dan, Yu, Lijun, Gu, Xiuye, Lezama, José, Huang, Jonathan, Schindler, Grant, Hornung, Rachel, Birodkar, Vighnesh, Yan, Jimmy, Chiu, Ming-Chang, Somandepalli, Krishna, Akbari, Hassan, Alon, Yair, Cheng, Yong, Dillon, Josh, Gupta, Agrim, Hahn, Meera, Hauth, Anja, Hendon, David, Martinez, Alonso, Minnen, David, Sirotenko, Mikhail, Sohn, Kihyuk, Yang, Xuan, Adam, Hartwig, Yang, Ming-Hsuan, Essa, Irfan, Wang, Huisheng, Ross, David A., Seybold, Bryan, Jiang, Lu
We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder-only transformer architecture that processes multimodal inputs -- includ
Externí odkaz:
http://arxiv.org/abs/2312.14125
Autor:
Yu, Lijun, Lezama, José, Gundavarapu, Nitesh B., Versari, Luca, Sohn, Kihyuk, Minnen, David, Cheng, Yong, Birodkar, Vighnesh, Gupta, Agrim, Gu, Xiuye, Hauptmann, Alexander G., Gong, Boqing, Yang, Ming-Hsuan, Essa, Irfan, Ross, David A., Jiang, Lu
While Large Language Models (LLMs) are the dominant models for generative tasks in language, they do not perform as well as diffusion models on image and video generation. To effectively use LLMs for visual generation, one crucial component is the vi
Externí odkaz:
http://arxiv.org/abs/2310.05737
Autor:
Yan, Shen, Xiong, Xuehan, Nagrani, Arsha, Arnab, Anurag, Wang, Zhonghao, Ge, Weina, Ross, David, Schmid, Cordelia
While large-scale image-text pretrained models such as CLIP have been used for multiple video-level tasks on trimmed videos, their use for temporal localization in untrimmed videos is still a relatively unexplored task. We design a new approach for t
Externí odkaz:
http://arxiv.org/abs/2308.11062
Autor:
Yu, Lijun, Cheng, Yong, Wang, Zhiruo, Kumar, Vivek, Macherey, Wolfgang, Huang, Yanping, Ross, David A., Essa, Irfan, Bisk, Yonatan, Yang, Ming-Hsuan, Murphy, Kevin, Hauptmann, Alexander G., Jiang, Lu
In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such as images or videos. SPAE converts between raw pixels and interpretabl
Externí odkaz:
http://arxiv.org/abs/2306.17842