Pytorch implementation of convolutional neural network visualization techniques
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Updated
Oct 10, 2022 - Python
Pytorch implementation of convolutional neural network visualization techniques
Code for our CVPR 2019 paper "A Simple Pooling-Based Design for Real-Time Salient Object Detection"
Official implementation of Score-CAM in PyTorch
Neural network visualization toolkit for tf.keras
CVPR2020, Multi-scale Interactive Network for Salient Object Detection
Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model. IEEE Transactions on Image Processing (2018)
(TPAMI2022) Salient Object Detection via Integrity Learning.
PySODEvalToolkit: A Python-based Evaluation Toolbox for Salient Object Detection and Camouflaged Object Detection
Unified Image and Video Saliency Modeling (ECCV 2020)
PySODMetrics: A Simple and Efficient Implementation of Grayscale/Binary Segmentation Metrcis
Video Salient Object Detection via Fully Convolutional Networks (TIP18)
A Deep Multi-Level Network for Saliency Prediction. ICPR 2016
(ECCV 2020) Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection
As part of the Explainable AI Toolkit (XAITK), XAITK-Saliency is an open source, explainable AI framework for visual saliency algorithm interfaces and implementations, built for analytics and autonomy applications.
ViNet Pushing the limits of Visual Modality for Audio Visual Saliency Prediction
Code for our IEEE TIP 2020 paper "Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton"
Gcam is an easy to use Pytorch library that makes model predictions more interpretable for humans. It allows the generation of attention maps with multiple methods like Guided Backpropagation, Grad-Cam, Guided Grad-Cam and Grad-Cam++.
(IEEE TIP 2021) Regularized Densely-connected Pyramid Network for Salient Instance Segmentation
Boundary Aware PoolNet = PoolNet + BASNet : Deeply supervised PoolNet using the hybrid loss in BASNet for Salient Object Detection
Exploiting Saliency for Object Segmentation from Image Level Labels, CVPR'17
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