Zobrazeno 1 - 10
of 209
pro vyhledávání: '"Kasaei, Shohreh"'
The performance of Video Instance Segmentation (VIS) methods has improved significantly with the advent of transformer networks. However, these networks often face challenges in training due to the high annotation cost. To address this, unsupervised
Externí odkaz:
http://arxiv.org/abs/2408.16661
Deep neural networks have made significant advancements in accurately estimating scene flow using point clouds, which is vital for many applications like video analysis, action recognition, and navigation. The robustness of these techniques, however,
Externí odkaz:
http://arxiv.org/abs/2404.13621
Autor:
Somers, Vladimir, Joos, Victor, Cioppa, Anthony, Giancola, Silvio, Ghasemzadeh, Seyed Abolfazl, Magera, Floriane, Standaert, Baptiste, Mansourian, Amir Mohammad, Zhou, Xin, Kasaei, Shohreh, Ghanem, Bernard, Alahi, Alexandre, Van Droogenbroeck, Marc, De Vleeschouwer, Christophe
Publikováno v:
2024 IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Work. (CVPRW)
Tracking and identifying athletes on the pitch holds a central role in collecting essential insights from the game, such as estimating the total distance covered by players or understanding team tactics. This tracking and identification process is cr
Externí odkaz:
http://arxiv.org/abs/2404.11335
In contrast to existing complex methodologies commonly employed for distilling knowledge from a teacher to a student, this paper showcases the efficacy of a simple yet powerful method for utilizing refined feature maps to transfer attention. The prop
Externí odkaz:
http://arxiv.org/abs/2403.05451
Deep spectral methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing the Laplacian of the affinity matrix to obtain eigensegments. However, instance segmentatio
Externí odkaz:
http://arxiv.org/abs/2402.02474
Autor:
Ahmadi, Rozhan, Kasaei, Shohreh
In recent years, weakly supervised semantic segmentation using image-level labels as supervision has received significant attention in the field of computer vision. Most existing methods have addressed the challenges arising from the lack of spatial
Externí odkaz:
http://arxiv.org/abs/2401.17828
Despite significant progress in deep learning-based optical flow methods, accurately estimating large displacements and repetitive patterns remains a challenge. The limitations of local features and similarity search patterns used in these algorithms
Externí odkaz:
http://arxiv.org/abs/2401.00833
In recent years, deep neural networks have achieved remarkable accuracy in computer vision tasks. With inference time being a crucial factor, particularly in dense prediction tasks such as semantic segmentation, knowledge distillation has emerged as
Externí odkaz:
http://arxiv.org/abs/2308.04243
In recent years, research on super-resolution has primarily focused on the development of unsupervised models, blind networks, and the use of optimization methods in non-blind models. But, limited research has discussed the loss function in the super
Externí odkaz:
http://arxiv.org/abs/2301.10575
Adversarial attacks pose serious challenges for deep neural network (DNN)-based analysis of various input signals. In the case of 3D point clouds, methods have been developed to identify points that play a key role in network decision, and these beco
Externí odkaz:
http://arxiv.org/abs/2210.14164