Context Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark

Deep Visual Attention Prediction (TIP18)

Keras implementation of paper 'Deep Visual Attention Prediction' which predicts human eye fixation on view-free scenes.

A Deep Learning Framework for Visual Attention Prediction and Analysis of News Interfaces | 2025 IEEE Conference on Artificial Intelligence (CAI)

The source code for the paper: "A deep convolutional symmetric encoder-decoder neural networks to predict students’ visual attention".

It includes Data Preparation & preprocessing, segmentation using Attention U-Net, ROI extraction, and classification with Hybrid Classification Model. Handcrafted and deep features are fused and used by a Random Forest model for survival prediction. Guided Grad-CAM provides visual explanations, making the system accurate and interpretable.

Python implementation of Deep Visual Attention prediction

Deep learning model for visual saliency prediction trained on the SALICON dataset. The model predicts human attention (saliency maps) for input images using a GAN-based architecture.

Comparative analysis of state-of-the-art deep learning models for visual saliency prediction. This project explores how model design, training modality, and scene complexity affect alignment with human visual attention using metrics like AUC-Judd, NSS, and CC.

Attention-guided deep learning framework for multi-class brain tumor classification using MRI scans. Combines EfficientNetB3 with CBAM for high accuracy (99.29%) and integrates Explainable AI (HiResCAM) for visual interpretability of predictions.

A deep learning model for age prediction using facial images, combining CBAM and self-attention with separable convolutions and residual connections. Designed for efficient and accurate regression using visual and contextual features in a modular architecture.

MM-CrowdNet is a multimodal deep learning system that predicts crowd risk levels by combining visual features from Hajj surveillance videos with sentiment scores from Reddit. It uses CNNs, LSTM, and attention to process video frames and adjusts predictions using social media sentiment.

AI-Trust Forensic Suite is an end-to-end deep learning + explainable AI web app that detects whether an image is Authentic vs. AI-generated and attributes the likely AI generator. It addresses the “forensic transparency gap” by pairing predictions with visual evidence attention heatmaps in a simple drag-and-drop Streamlit dashboard.