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pro vyhledávání: '"Sharir, Gilad"'
While efficient architectures and a plethora of augmentations for end-to-end image classification tasks have been suggested and heavily investigated, state-of-the-art techniques for audio classifications still rely on numerous representations of the
Externí odkaz:
http://arxiv.org/abs/2204.11479
In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average pooling. By redesigning the de
Externí odkaz:
http://arxiv.org/abs/2111.12933
Autor:
Glaser, Tamar, Ben-Baruch, Emanuel, Sharir, Gilad, Zamir, Nadav, Noy, Asaf, Zelnik-Manor, Lihi
In recent years the amounts of personal photos captured increased significantly, giving rise to new challenges in multi-image understanding and high-level image understanding. Event recognition in personal photo albums presents one challenging scenar
Externí odkaz:
http://arxiv.org/abs/2109.12499
Leading methods in the domain of action recognition try to distill information from both the spatial and temporal dimensions of an input video. Methods that reach State of the Art (SotA) accuracy, usually make use of 3D convolution layers as a way to
Externí odkaz:
http://arxiv.org/abs/2103.13915
Many deep learning models, developed in recent years, reach higher ImageNet accuracy than ResNet50, with fewer or comparable FLOPS count. While FLOPs are often seen as a proxy for network efficiency, when measuring actual GPU training and inference t
Externí odkaz:
http://arxiv.org/abs/2003.13630
We propose a new method for anomaly detection of human actions. Our method works directly on human pose graphs that can be computed from an input video sequence. This makes the analysis independent of nuisance parameters such as viewpoint or illumina
Externí odkaz:
http://arxiv.org/abs/1912.11850
We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 challenge. Our approach combines category-based object detection, category-independent object appearance segmentation and temporal object tracking.
Externí odkaz:
http://arxiv.org/abs/1707.06545
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average pooling. By redesigning the de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f79ac5852de991dc71da822ae6253e8c
https://doi.org/10.1109/wacv56688.2023.00012
https://doi.org/10.1109/wacv56688.2023.00012
Autor:
Sharir, Gilad, Tuytelaars, Tinne
Publikováno v:
IEEE Winter Conference on Applications of Computer Vision; 2014, p610-617, 8p
Autor:
Sharir, Gilad, Tuytelaars, Tinne
Publikováno v:
2012 IEEE Computer Society Conference on Computer Vision & Pattern Recognition Workshops; 1/ 1/2012, p9-14, 6p