Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing

<div>Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification.- Distortion Estimation Through Explicit Modeling of the Refractive Surface.- Eye Movement-based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation.- IBDNet: Lightweight Network for On-orbit Image Blind Denoising.- Aggregating Rich Deep Semantic Features for Fine-Grained Place Classification.- Improving Reliability of Object Detection for Lunar Craters using Monte Carlo Dropout.- An improved convolutional neural network for steganalysis in the scenario of reuse of the stego-key.- A New Learning-based One Shot Detection Framework For Natural Images.- Dense Receptive Field Network: A Backbone Network for Object Detection.- Referring Expression Comprehension via Co-attention and Visual Context.- Comparison between U-Net and U-ReNet models in OCR tasks.- Severe Convective Weather Classification in Remote Sensing Images by Semantic Segmentation.- Action Recognition Based on Divide-and-conquer.- An Adaptive Feature Channel Weighting Scheme for Correlation Tracking.- In-silico staining from bright-field and fluorescent images using deep learning.- A lightweight neural network for hard exudate segmentation of fundus image.- Attentional Residual Dense Factorized Network for Real-time Semantic Segmentation.- Random drop loss for tiny object segmentation: Application to lesion segmentation in fundus images.- Flow2Seg: Motion-Aided Semantic Segmentation.- COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation.- Learning Deep Structured Multi-Scale Features for crisp and occlusion edge detection.- Graph-Boosted Attentive Network for Semantic Body Parsing.- A Global-Local Architecture Constrained by Multiple Attributes for Person Re-identification.- Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders.- Learning Relational-Structural Networks for Robust Face Alignment.- An Efficient 3D-NAS Method for Video-based Gesture Recognition.- Robustness of deep LSTM networks in freehand gesture recognition.- Delving into the Impact of Saliency Detector: A GeminiNet for Accurate Saliency Detection.- FCN Salient Object Detection Using Region Cropping.- Object-Level Salience Detection By Progressively Enhanced Network.- Action unit assisted Facial Expression Recognition.- Discriminative Feature Learning using Two-stage Training Strategy for Facial Expression Recognition.- Action Units Classification using ClusWiSARD.- Automatic Estimation of Dog Age: The DogAge Dataset and Challenge.- Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency&nbsp; Compensation in Distributed Virtual Reality.- Variational Deep Embedding with Regularized Student-t Mixture Model.- A mixture-of-experts model for vehicle prediction using an online learning approach.- An Application of Convolutional Neural Networks for Analyzing Dogs' Sleep Patterns.- On the Inability of Markov Models to Capture Criticality in Human Mobility.- LSTM with Uniqueness Attention for Human Activity Recognition.- Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training.- Generative Creativity: Adversarial Learning for Bionic Design.- Self-attention StarGAN for Multi-domain Image-to-image Translation.- Generative Adversarial Networks for Operational Scenario Planing of Renewable Energy Farms: A Study on Wind and Photovoltaic.- Constraint-Based Visual Generation.- Text to Image Synthesis based on Multiple Discrimination.- Disentangling Latent Factors of Variational Auto-Encoder with Whitening.- Training Discriminative Models to Evaluate Generative Ones.- Scene Graph Generation via Convolutional Message Passing and Class-aware Memory Embeddings.- Change Detection in Satellite Images using Reconstruction Errors of Joint Autoencoders.- Physical Adversarial Attacks by Projecting Perturbations.- Improved Forward-backward Propagation to Generate Adversarial Examples.- Incremental Learning of GAN for Detecting Multiple Adversarial Attacks.- Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples.- DCT:Differential Combination Testing of Deep Learning Systems.- Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection.- HLR: Generating Adversarial Examples by High-Level Representations.</div><div><br></div>