Artificial Neural Networks and Machine Learning - ICANN 2021

Multi-Target Stance Detection.- Hierarchical Ensemble for Multi-view Clustering.- Structure-Aware Multi-Scale Hierarchical Graph Convolutional Network for Skeleton Action Recognition.- Learning Hierarchical Reasoning for Text-based Visual Question Answering.- Hierarchical Deep Gaussian Processes Latent Variable Model via Expectation Propagation.- Adaptive Consensus-Based Ensemble for Improved Deep Learning Inference Cost.- Human pose estimation.- Multi-Branch Network for Small Human Pose Estimation.- PNO: Personalized Network Optimization for Human Pose and Shape  Reconstruction.- JointPose: Jointly Optimizing Evolutionary Data Augmentation and Prediction Neural Network for 3D Human Pose Estimation.- DeepRehab: Real Time Pose Estimation on the Edge for Knee Injury Rehabilitation.- Image processing.- Subspace constraint for Single Image Super-Resolution.- Towards Fine-Grained Control over Latent Space for Unpaired Image-to-Image Translation.- FMSNet: Underwater Image Restoration by Learning from a Synthesized Dataset.- Towards Measuring Bias in Image Classification.- Towards Image Retrieval with Noisy Labels via Non-deterministic Features.- Image segmentation.- Improving Visual Question Answering by Semantic Segmentation.- Weakly Supervised Semantic Segmentation with Patch-Based Metric Learning Enhancement.- ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation.- Depth Mapping Hybrid Deep Learning Method for Optic Disc and Cup Segmentation on Stereoscopic Ocular Fundus.- RATS: Robust Automated Tracking and Segmentation of Similar Instances.- Knowledge distillation.- Data Diversification Revisited: Why Does It Work?.- A Generalized Meta-Loss Function for Distillation Based Learning Using Privileged Information for Classification and Regression.- Empirical Study of Data-Free Iterative Knowledge Distillation.- Adversarial Variational Knowledge Distillation.- Extract then Distill: Efficient and Effective Task-Agnostic BERT Distillation.- Medical image processing.- Semi-supervised Learning based Right Ventricle Segmentation Using Deep Convolutional Boltzmann Machine Shape Model.- Improved U-Net for Plaque Segmentation of Intracoronary Optical Coherence Tomography Images.- Approximated Masked Global Context Network for Skin Lesion Segmentation.- DSNet: Dynamic Selection Network for Biomedical Image Segmentation.- Computational Approach to Identifying Contrast-Driven Retinal Ganglion Cells.- Radiological Identification of Hip Joint Centers from X-ray Images Using Fast Deep Stacked Network and Dynamic Registration Graph.- A Two-Branch Neural Network for Non-Small-Cell Lung Cancer Classification and Segmentation.- Uncertainty Quantification and Estimation in Medical Image Classification.- Labeling Chest X-Ray Reports Using Deep Learning.