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Found 6 papers, 2 papers with code
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds.
SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition
Current state of the art object recognition architectures achieve impressive performance but are typically specialized for a single depictive style (e. g. photos only, sketches only).
Saliency Unified: A Deep Architecture for Simultaneous Eye Fixation Prediction and Salient Object Segmentation
Human eye fixations often correlate with locations of salient objects in the scene.
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective.
DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations
Understanding and predicting the human visual attentional mechanism is an active area of research in the fields of neuroscience and computer vision.
Crowd Flow Segmentation in Compressed Domain using CRF
This work in compressed domain can be easily extended to pixel domain by substituting motion vectors with motion based features like optical flow.
