187 citations · 261 across the 8 of their papers we have counts for
14 papers
Self-Supervised Ranking for Representation Learning
Ali Varamesh, Ali Diba, Tinne Tuytelaars +1
We present a new framework for self-supervised representation learning by formulating it as a ranking problem in an image retrieval context on a large number of random views (augme…
3D CNNs with Adaptive Temporal Feature Resolutions
Mohsen Fayyaz, Emad Bahrami, Ali Diba +4
While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFL…
DynamoNet: Dynamic Action and Motion Network
Ali Diba, Vivek Sharma, Luc Van Gool +1
In this paper, we are interested in self-supervised learning the motion cues in videos using dynamic motion filters for a better motion representation to finally boost human action…
Large Scale Holistic Video Understanding
Ali Diba, Mohsen Fayyaz, Vivek Sharma +4
Video recognition has been advanced in recent years by benchmarks with rich annotations. However, research is still mainly limited to human action or sports recognition - focusing…
Spatio-Temporal Channel Correlation Networks for Action Classification
Ali Diba, Mohsen Fayyaz, Vivek Sharma +4
The work in this paper is driven by the question if spatio-temporal correlations are enough for 3D convolutional neural networks (CNN)? Most of the traditional 3D networks use loca…
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
Ali Diba, Mohsen Fayyaz, Vivek Sharma +4
The work in this paper is driven by the question how to exploit the temporal cues available in videos for their accurate classification, and for human action recognition in particu…
Weakly Supervised Object Discovery by Generative Adversarial & Ranking Networks
Ali Diba, Vivek Sharma, Rainer Stiefelhagen +1
The deep generative adversarial networks (GAN) recently have been shown to be promising for different computer vision applications, like image edit- ing, synthesizing high resoluti…
Classification Driven Dynamic Image Enhancement
Vivek Sharma, Ali Diba, Davy Neven +3
Convolutional neural networks rely on image texture and structure to serve as discriminative features to classify the image content. Image enhancement techniques can be used as pre…
Weakly Supervised Cascaded Convolutional Networks
Ali Diba, Vivek Sharma, Ali Pazandeh +2
Object detection is a challenging task in visual understanding domain, and even more so if the supervision is to be weak. Recently, few efforts to handle the task without expensive…
Deep Temporal Linear Encoding Networks
Ali Diba, Vivek Sharma, Luc Van Gool
The CNN-encoding of features from entire videos for the representation of human actions has rarely been addressed. Instead, CNN work has focused on approaches to fuse spatial and t…
Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification
Ali Diba, Ali Mohammad Pazandeh, Luc Van Gool
The video and action classification have extremely evolved by deep neural networks specially with two stream CNN using RGB and optical flow as inputs and they present outstanding p…
DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns
Ali Diba, Ali Mohammad Pazandeh, Hamed Pirsiavash +1
The recognition of human actions and the determination of human attributes are two tasks that call for fine-grained classification. Indeed, often rather small and inconspicuous obj…
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers
Amir Ghodrati, Ali Diba, Marco Pedersoli +2
In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained…
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
Amir Ghodrati, Ali Diba, Marco Pedersoli +2
In this paper we evaluate the quality of the activation layers of a convolutional neural network (CNN) for the gen- eration of object proposals. We generate hypotheses in a sliding…