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Found 12 papers, 7 papers with code
Refining activation downsampling with SoftPool
Convolutional Neural Networks (CNNs) use pooling to decrease the size of activation maps.
Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions
Deep learning approaches have been established as the main methodology for video classification and recognition.
Class Feature Pyramids for Video Explanation
We demonstrate the method on six state-of-the-art 3D convolution neural networks (CNNs) on three action recognition (Kinetics-400, UCF-101, and HMDB-51) and two egocentric action recognition datasets (EPIC-Kitchens and EGTEA Gaze+).
Exploring object-centric and scene-centric CNN features and their complementarity for human rights violations recognition in images
With this, we show that HRA database poses a challenge at a higher level for the well studied representation learning methods, and provide a benchmark in the task of human rights violations recognition in visual context.
MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip
Intelligent Transportation Systems (ITS) have become an important pillar in modern "smart city" framework which demands intelligent involvement of machines.
DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level
Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters.
Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?
We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data.
A Paradigm Shift: Detecting Human Rights Violations Through Web Images
The growing presence of devices carrying digital cameras, such as mobile phones and tablets, combined with ever improving internet networks have enabled ordinary citizens, victims of human rights abuse, and participants in armed conflicts, protests, and disaster situations to capture and share via social media networks images and videos of specific events.
Detection of Human Rights Violations in Images: Can Convolutional Neural Networks help?
We conduct a rigorous evaluation on a common ground by combining this dataset with different state-of-the-art deep convolutional architectures in order to achieve recognition of human rights violations.
Evaluating Deep Convolutional Neural Networks for Material Classification
Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention.
Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data
Despite significant recent advances in the field of head pose estimation and facial expression recognition, raising the cognitive level when analysing human activity presents serious challenges to current concepts.
GET-AID: Visual Recognition of Human Rights Abuses via Global Emotional Traits
Our hypothesis is that the emotional state of a person -- how positive or pleasant an emotion is, and the control level of the situation by the person -- are powerful cues for perceiving potential human rights violations.
