76 citations · 179 across the 9 of their papers we have counts for
13 papers · 1 filter
AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling
Alexandros Stergiou, Ronald Poppe
Pooling layers are essential building blocks of convolutional neural networks (CNNs), to reduce computational overhead and increase the receptive fields of proceeding convolutional…
Incremental Few-Shot Instance Segmentation
Dan Andrei Ganea, Bas Boom, Ronald Poppe
Few-shot instance segmentation methods are promising when labeled training data for novel classes is scarce. However, current approaches do not facilitate flexible addition of nove…
Refining activation downsampling with SoftPool
Alexandros Stergiou, Ronald Poppe, Grigorios Kalliatakis
Convolutional Neural Networks (CNNs) use pooling to decrease the size of activation maps. This process is crucial to increase the receptive fields and to reduce computational requi…
Multi-Temporal Convolutions for Human Action Recognition in Videos
Alexandros Stergiou, Ronald Poppe
Effective extraction of temporal patterns is crucial for the recognition of temporally varying actions in video. We argue that the fixed-sized spatio-temporal convolution kernels u…
Learn to cycle: Time-consistent feature discovery for action recognition
Alexandros Stergiou, Ronald Poppe
Generalizing over temporal variations is a prerequisite for effective action recognition in videos. Despite significant advances in deep neural networks, it remains a challenge to…
Learning Class Regularized Features for Action Recognition
Alexandros Stergiou, Ronald Poppe, Remco C. Veltkamp
Training Deep Convolutional Neural Networks (CNNs) is based on the notion of using multiple kernels and non-linearities in their subsequent activations to extract useful features.…
Spatio-Temporal FAST 3D Convolutions for Human Action Recognition
Alexandros Stergiou, Ronald Poppe
Effective processing of video input is essential for the recognition of temporally varying events such as human actions. Motivated by the often distinctive temporal characteristics…
Class Feature Pyramids for Video Explanation
Alexandros Stergiou, Georgios Kapidis, Grigorios Kalliatakis +3
Deep convolutional networks are widely used in video action recognition. 3D convolutions are one prominent approach to deal with the additional time dimension. While 3D convolution…
Multitask Learning to Improve Egocentric Action Recognition
Georgios Kapidis, Ronald Poppe, Elsbeth van Dam +2
In this work we employ multitask learning to capitalize on the structure that exists in related supervised tasks to train complex neural networks. It allows training a network for…
Light Field Saliency Detection with Deep Convolutional Networks
Jun Zhang, Yamei Liu, Shengping Zhang +2
Light field imaging presents an attractive alternative to RGB imaging because of the recording of the direction of the incoming light. The detection of salient regions in a light f…
Egocentric Hand Track and Object-based Human Action Recognition
Georgios Kapidis, Ronald Poppe, Elsbeth van Dam +2
Egocentric vision is an emerging field of computer vision that is characterized by the acquisition of images and video from the first person perspective. In this paper we address t…
Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions
Alexandros Stergiou, Georgios Kapidis, Grigorios Kalliatakis +3
Deep learning approaches have been established as the main methodology for video classification and recognition. Recently, 3-dimensional convolutions have been used to achieve stat…
Analyzing Human-Human Interactions: A Survey
Alexandros Stergiou, Ronald Poppe
Many videos depict people, and it is their interactions that inform us of their activities, relation to one another and the cultural and social setting. With advances in human acti…