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Ali Diba
Researcher at Katholieke Universiteit Leuven
Publications - 37
Citations - 1987
Ali Diba is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Convolutional neural network & Object detection. The author has an hindex of 17, co-authored 37 publications receiving 1505 citations. Previous affiliations of Ali Diba include Sharif University of Technology.
Papers
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Proceedings ArticleDOI
Deep Temporal Linear Encoding Networks
TL;DR: Temporal linear encoding (TLE) as discussed by the authors is proposed to encode the entire video into a compact feature representation, learning the semantics and a discriminative feature space, which is applicable to all kinds of networks like 2D and 3D CNNs.
Proceedings ArticleDOI
Weakly Supervised Cascaded Convolutional Networks
TL;DR: In this article, a new architecture of cascaded networks is proposed to learn a convolutional neural network (CNN) under such conditions, with either two cascade stages or three which are trained in an end-to-end pipeline.
Posted Content
Weakly Supervised Cascaded Convolutional Networks
TL;DR: This work introduces two new architecture of cascaded networks, with either two cascade stages or three which are trained in an end-to-end pipeline to learn a convolutional neural network (CNN) under such conditions.
Book ChapterDOI
Spatio-temporal Channel Correlation Networks for Action Classification
Ali Diba,Mohsen Fayyaz,Vivek Sharma,Mohammad Mahdi Arzani,Rahman Yousefzadeh,Juergen Gall,Luc Van Gool +6 more
TL;DR: By fine-tuning this network, this work beats the performance of generic and recent methods in 3D CNNs, which were trained on large video datasets, and fine- Tuned on the target datasets, e.g. HMDB51/UCF101 and Kinetics.
Posted Content
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification.
Ali Diba,Mohsen Fayyaz,Vivek Sharma,Amir Hossein Karami,Mohammad Mahdi Arzani,Rahman Yousefzadeh,Luc Van Gool +6 more
TL;DR: By finetuning this network, the proposed video convolutional network T3D outperforms the performance of generic and recent methods in 3D CNNs, which were trained on large video datasets, and finetuned on the target datasets, e.g. HMDB51/UCF101.