Zobrazeno 1 - 10
of 771
pro vyhledávání: '"A, Sclaroff"'
In this work, we first propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently. Given a frame pair, we estimate multiple bidirectional flows to directly forward warp the pixels to the desired time step
Externí odkaz:
http://arxiv.org/abs/2310.18946
Multi-label image recognition in the low-label regime is a task of great challenge and practical significance. Previous works have focused on learning the alignment between textual and visual spaces to compensate for limited image labels, yet may suf
Externí odkaz:
http://arxiv.org/abs/2308.01890
Autor:
Lee, Ariel N., Bargal, Sarah Adel, Kasera, Janavi, Sclaroff, Stan, Saenko, Kate, Ruiz, Nataniel
Vision transformers (ViTs) have significantly changed the computer vision landscape and have periodically exhibited superior performance in vision tasks compared to convolutional neural networks (CNNs). Although the jury is still out on which model t
Externí odkaz:
http://arxiv.org/abs/2306.17848
Autor:
Naphade, Milind, Wang, Shuo, Anastasiu, David C., Tang, Zheng, Chang, Ming-Ching, Yao, Yue, Zheng, Liang, Rahman, Mohammed Shaiqur, Arya, Meenakshi S., Sharma, Anuj, Feng, Qi, Ablavsky, Vitaly, Sclaroff, Stan, Chakraborty, Pranamesh, Prajapati, Sanjita, Li, Alice, Li, Shangru, Kunadharaju, Krishna, Jiang, Shenxin, Chellappa, Rama
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential. The 2023 challenge
Externí odkaz:
http://arxiv.org/abs/2304.07500
Modern deep neural networks tend to be evaluated on static test sets. One shortcoming of this is the fact that these deep neural networks cannot be easily evaluated for robustness issues with respect to specific scene variations. For example, it is h
Externí odkaz:
http://arxiv.org/abs/2211.16499
In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature. We first propose a new approach to quantify the temporal relationships between frames captured by CNN-bas
Externí odkaz:
http://arxiv.org/abs/2204.11929
Autor:
Naphade, Milind, Wang, Shuo, Anastasiu, David C., Tang, Zheng, Chang, Ming-Ching, Yao, Yue, Zheng, Liang, Rahman, Mohammed Shaiqur, Venkatachalapathy, Archana, Sharma, Anuj, Feng, Qi, Ablavsky, Vitaly, Sclaroff, Stan, Chakraborty, Pranamesh, Li, Alice, Li, Shangru, Chellappa, Rama
The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and artificial intelligence: Intelligent Traffic Systems (ITS), and brick and
Externí odkaz:
http://arxiv.org/abs/2204.10380
Motion-based video frame interpolation commonly relies on optical flow to warp pixels from the inputs to the desired interpolation instant. Yet due to the inherent challenges of motion estimation (e.g. occlusions and discontinuities), most state-of-t
Externí odkaz:
http://arxiv.org/abs/2204.03513
While pose estimation is an important computer vision task, it requires expensive annotation and suffers from domain shift. In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a syntheti
Externí odkaz:
http://arxiv.org/abs/2204.00172
Deep models must learn robust and transferable representations in order to perform well on new domains. While domain transfer methods (e.g., domain adaptation, domain generalization) have been proposed to learn transferable representations across dom
Externí odkaz:
http://arxiv.org/abs/2203.11819