Deep convolutional network for saliency prediction, implemented by PyTorch

Explainability and Robustness in Metric Space

Code for evaluating saliency maps with classification metrics.

This project uses Deep Learning to extract Salient text from an image using State-of-the-Art Vision Transformer Architecture.

Saliency Detection library (models, loss, utils) with PyTorch

Neural network visualization tool after an optional model compression with parameter pruning: (integrated) gradients, guided/visual backpropagation, activation maps for the cao model on the IndianPines dataset

reference implementation of Real-time Salient Object Detection based on Division of Gaussians [Katramados/Breckon, 2011]

Code and data for the ACL 2023 NLReasoning Workshop paper "Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods" (Feldhus et al., 2023)

The official PyTorch implementation of IEEE Transactions on Image Processing 2021 paper "Rethinking the U-shape Structure for Salient Object Detection"

Codes for the AAAI 2021 paper "Locate Globally, Segment locally: A Progressive Architecture With Knowledge Review Network for Salient Object Detection"

Official Repository for ECCV 2020 paper "AiR: Attention with Reasoning Capability"

PySODMetrics: A Simple and Efficient Implementation of Grayscale/Binary Segmentation Metrcis

Contextual Encoder-Decoder Network for Visual Saliency Prediction [Neural Networks 2020]

CVPR2020, Multi-scale Interactive Network for Salient Object Detection

Neural network visualization toolkit for tf.keras