Zobrazeno 1 - 10
of 6 247
pro vyhledávání: '"LUO, Yang"'
In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect th
Externí odkaz:
http://arxiv.org/abs/2408.05831
The surging demand for cloud computing resources, driven by the rapid growth of sophisticated large-scale models and data centers, underscores the critical importance of efficient and adaptive resource allocation. As major tech enterprises deploy mas
Externí odkaz:
http://arxiv.org/abs/2408.01000
Autor:
Yenamandra, Sriram, Ramachandran, Arun, Khanna, Mukul, Yadav, Karmesh, Vakil, Jay, Melnik, Andrew, Büttner, Michael, Harz, Leon, Brown, Lyon, Nandi, Gora Chand, PS, Arjun, Yadav, Gaurav Kumar, Kala, Rahul, Haschke, Robert, Luo, Yang, Zhu, Jinxin, Han, Yansen, Lu, Bingyi, Gu, Xuan, Liu, Qinyuan, Zhao, Yaping, Ye, Qiting, Dou, Chenxiao, Chua, Yansong, Kuzma, Volodymyr, Humennyy, Vladyslav, Partsey, Ruslan, Francis, Jonathan, Chaplot, Devendra Singh, Chhablani, Gunjan, Clegg, Alexander, Gervet, Theophile, Jain, Vidhi, Ramrakhya, Ram, Szot, Andrew, Wang, Austin, Yang, Tsung-Yen, Edsinger, Aaron, Kemp, Charlie, Shah, Binit, Kira, Zsolt, Batra, Dhruv, Mottaghi, Roozbeh, Bisk, Yonatan, Paxton, Chris
In order to develop robots that can effectively serve as versatile and capable home assistants, it is crucial for them to reliably perceive and interact with a wide variety of objects across diverse environments. To this end, we proposed Open Vocabul
Externí odkaz:
http://arxiv.org/abs/2407.06939
Multimodal learning robust to missing modality has attracted increasing attention due to its practicality. Existing methods tend to address it by learning a common subspace representation for different modality combinations. However, we reveal that t
Externí odkaz:
http://arxiv.org/abs/2407.04458
Autor:
Peng, Shuting, Han, Yulei, Li, Yongkai, Shen, Jianchang, Miao, Yu, Luo, Yang, Huai, Linwei, Ou, Zhipeng, Li, Hongyu, Xiang, Ziji, Liu, Zhengtai, Shen, Dawei, Hashimoto, Makoto, Lu, Donghui, Yao, Yugui, Qiao, Zhenhua, Wang, Zhiwei, He, Junfeng
Kagome metal CsV$_3$Sb$_5$ has attracted much recent attention due to the coexistence of multiple exotic orders and the associated proposals to mimic unconventional high temperature superconductors. Nevertheless, magnetism and strong electronic corre
Externí odkaz:
http://arxiv.org/abs/2406.17769
Deep State Space Models (SSMs) have proven effective in numerous task scenarios but face significant security challenges due to Adversarial Perturbations (APs) in real-world deployments. Adversarial Training (AT) is a mainstream approach to enhancing
Externí odkaz:
http://arxiv.org/abs/2406.05532
The increase in parameter size of multimodal large language models (MLLMs) introduces significant capabilities, particularly in-context learning, where MLLMs enhance task performance without updating pre-trained parameters. This effectiveness, howeve
Externí odkaz:
http://arxiv.org/abs/2404.12866
Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However, current leading methods rely on sup
Externí odkaz:
http://arxiv.org/abs/2404.00680
Due to the complementary nature of visible light and thermal infrared modalities, object tracking based on the fusion of visible light images and thermal images (referred to as RGB-T tracking) has received increasing attention from researchers in rec
Externí odkaz:
http://arxiv.org/abs/2403.16834
Logit knowledge distillation attracts increasing attention due to its practicality in recent studies. However, it often suffers inferior performance compared to the feature knowledge distillation. In this paper, we argue that existing logit-based met
Externí odkaz:
http://arxiv.org/abs/2403.13512