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Found 10 papers, 1 papers with code
Dilated Continuous Random Field for Semantic Segmentation
Mean field approximation methodology has laid the foundation of modern Continuous Random Field (CRF) based solutions for the refinement of semantic segmentation.
Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network
The paper focuses on improving the recent plug-and-play patch rescaling module (PRM) based approaches for crowd counting.
Parallel Scale-wise Attention Network for Effective Scene Text Recognition
To address these issues, we propose a new multi-scale and encoder-based attention network for text recognition that performs the multi-scale FE and VA in parallel.
Stereo Frustums: A Siamese Pipeline for 3D Object Detection
The paper proposes a light-weighted stereo frustums matching module for 3D objection detection.
Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting
The state-of-the-art patch rescaling module (PRM) based approaches prove to be very effective in improving the crowd counting performance.
Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks
The paper proposes to employ deep convolutional neural networks (CNNs) to classify noncoding RNA (ncRNA) sequences.
ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images
This module also provides a count for each label, which is then analyzed via a specifically devised novel decision module to decide whether the image belongs to any of the two extreme cases (very low or very high density) or a normal case.
Plug-and-Play Rescaling Based Crowd Counting in Static Images
Crowd counting is a challenging problem especially in the presence of huge crowd diversity across images and complex cluttered crowd-like background regions, where most previous approaches do not generalize well and consequently produce either huge crowd underestimation or overestimation.
Object Detection with Convolutional Neural Networks
In this chapter, we present a brief overview of the recent development in object detection using convolutional neural networks (CNN).
