DeepAI
Log In Sign Up

Using Visual Saliency to Improve Human Detection with Convolutional Networks

02/21/2018
by   Vandit Gajjar, et al.
0

In this paper, we demonstrate an approach based on visual saliency for detection of humans. Using Deep Multi-Layer Network [1], we find the saliency maps of an image having humans, multiply with the input image and fed to Convolutional Neural Network (CNN). For detection purpose, we train DetectNet on prepared two challenging datasets - Penn-Fudan Dataset and TudBrussels Benchmark. After training, the network learns the mid and high-level features of a human body. We show the effectiveness of our approach on both the tasks and report state-of-the-art performance on PennFudan Dataset with the detection accuracy of 91.4 Benchmark.

READ FULL TEXT

page 2

page 3

page 6

09/07/2016

Visual Saliency Detection Based on Multiscale Deep CNN Features

Visual saliency is a fundamental problem in both cognitive and computati...
08/04/2020

Implicit Saliency in Deep Neural Networks

In this paper, we show that existing recognition and localization deep a...
06/01/2022

Deepfake Caricatures: Amplifying attention to artifacts increases deepfake detection by humans and machines

Deepfakes pose a serious threat to our digital society by fueling the sp...
02/04/2016

NeRD: a Neural Response Divergence Approach to Visual Salience Detection

In this paper, a novel approach to visual salience detection via Neural ...
10/17/2019

Context-Aware Saliency Detection for Image Retargeting Using Convolutional Neural Networks

Image retargeting is the task of making images capable of being displaye...
06/01/2016

OpenSalicon: An Open Source Implementation of the Salicon Saliency Model

In this technical report, we present our publicly downloadable implement...
05/22/2018

Global-and-local attention networks for visual recognition

State-of-the-art deep convolutional networks (DCNs) such as squeeze-and-...