Zobrazeno 1 - 10
of 4 751
pro vyhledávání: '"Naeem, Muhammad A."'
Autor:
Fan, Yue, Xian, Yongqin, Zhai, Xiaohua, Kolesnikov, Alexander, Naeem, Muhammad Ferjad, Schiele, Bernt, Tombari, Federico
Building generalized models that can solve many computer vision tasks simultaneously is an intriguing direction. Recent works have shown image itself can be used as a natural interface for general-purpose visual perception and demonstrated inspiring
Externí odkaz:
http://arxiv.org/abs/2407.00503
Autor:
Khattak, Muhammad Uzair, Naeem, Muhammad Ferjad, Hassan, Jameel, Naseer, Muzammal, Tombari, Federico, Khan, Fahad Shahbaz, Khan, Salman
Recent advancements in Large Language Models (LLMs) have led to the development of Video Large Multi-modal Models (Video-LMMs) that can handle a wide range of video understanding tasks. These models have the potential to be deployed in real-world app
Externí odkaz:
http://arxiv.org/abs/2405.03690
Autor:
Wang, Haiyang, Tang, Hao, Jiang, Li, Shi, Shaoshuai, Naeem, Muhammad Ferjad, Li, Hongsheng, Schiele, Bernt, Wang, Liwei
This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a vanilla ViT. Motivated by the universality of the Multi-layer Transformer architecture (e.g, GPT) widely used in large l
Externí odkaz:
http://arxiv.org/abs/2403.09394
Autor:
Khan, Muhammad Saif Ullah, Naeem, Muhammad Ferjad, Tombari, Federico, Van Gool, Luc, Stricker, Didier, Afzal, Muhammad Zeshan
We propose FocusCLIP, integrating subject-level guidance--a specialized mechanism for target-specific supervision--into the CLIP framework for improved zero-shot transfer on human-centric tasks. Our novel contributions enhance CLIP on both the vision
Externí odkaz:
http://arxiv.org/abs/2403.06904
Autor:
Khattak, Muhammad Uzair, Naeem, Muhammad Ferjad, Naseer, Muzammal, Van Gool, Luc, Tombari, Federico
Foundational vision-language models such as CLIP are becoming a new paradigm in vision, due to their excellent generalization abilities. However, adapting these models for downstream tasks while maintaining their generalization remains a challenge. I
Externí odkaz:
http://arxiv.org/abs/2401.02418
Performance of ordinary least squares(OLS) method for the \emph{estimation of high dimensional stable state transition matrix} $A$(i.e., spectral radius $\rho(A)<1$) from a single noisy observed trajectory of the linear time invariant(LTI)\footnote{L
Externí odkaz:
http://arxiv.org/abs/2312.05794
Autor:
Naeem, Muhammad Ferjad, Xian, Yongqin, Zhai, Xiaohua, Hoyer, Lukas, Van Gool, Luc, Tombari, Federico
Image-Text pretraining on web-scale image caption datasets has become the default recipe for open vocabulary classification and retrieval models thanks to the success of CLIP and its variants. Several works have also used CLIP features for dense pred
Externí odkaz:
http://arxiv.org/abs/2310.13355
High dimensional random dynamical systems are ubiquitous, including -- but not limited to -- cyber-physical systems, daily return on different stocks of S&P 1500 and velocity profile of interacting particle systems around McKeanVlasov limit. Mathemat
Externí odkaz:
http://arxiv.org/abs/2310.10523
Publikováno v:
International Journal of Emerging Markets, 2022, Vol. 19, Issue 8, pp. 2222-2240.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJOEM-05-2022-0782
Autor:
Khan, Muhammad Gul Zain Ali, Naeem, Muhammad Ferjad, Van Gool, Luc, Stricker, Didier, Tombari, Federico, Afzal, Muhammad Zeshan
Continual Learning aims to learn a single model on a sequence of tasks without having access to data from previous tasks. The biggest challenge in the domain still remains catastrophic forgetting: a loss in performance on seen classes of earlier task
Externí odkaz:
http://arxiv.org/abs/2308.15827