Machine Learning and Optimization
Sublabel-Accurate Multilabeling Meets Product Label Spaces
InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization
Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise
Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data
Revisiting Consistency Regularization for Semi-Supervised Learning
Learning Robust Models Using the Principle of Independent Causal Mechanisms
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks
Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators
End-to-end Learning of Fisher Vector Encodings for Part Features in Fine-grained Recognition
Investigating the Consistency of Uncertainty Sampling in Deep Active Learning
ScaleNet: An Unsupervised Representation Learning Method for Limited Information
Actions, Events, and Segmentation
A New Split for Evaluating True Zero-Shot Action Recognition
Video Instance Segmentation with Recurrent Graph Neural Networks
Distractor-Aware Video Object Segmentation
(SP)^2Net for Generalized Zero-Label Semantic Segmentation
Contrastive Representation Learning for Hand Shape Estimation
Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks
FIFA: Fast Inference Approximation for Action Segmentation
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision
A Comparative Study of PnP and Learning Approaches to Super-Resolution in a Real-World Setting
Merging-ISP: Multi-Exposure High Dynamic Range Image Signal Processing
Spatiotemporal Outdoor Lighting Aggregation on Image Sequences
Generative Models and Multimodal Data
AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style
Learning Conditional Invariance through Cycle Consistency
CAGAN: Text-To-Image Generation with Combined Attention Generative Adversarial Networks
TxT: Crossmodal End-to-End Learning with Transformers
Diverse Image Captioning with Grounded Style
Labeling and Self-Supervised Learning
Leveraging Group Annotations in Object Detection Using Graph-Based Pseudo-Labeling
Quantifying Uncertainty of Image Labelings Using Assignment Flows
Implicit and Explicit Attention for Zero-Shot Learning
Self-Supervised Learning for Object Detection in Autonomous Driving
Assignment Flows and Nonlocal PDEs on Graphs
Applications
Viewpoint-Tolerant Semantic Segmentation for Aerial Logistics
T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression
TetraPackNet: Four-Corner-Based Object Detection in Logistics Use-Cases
Detecting Slag Formations with Deep Convolutional Neural Networks
Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture
Weakly Supervised Segmentation Pre-training for Plant Cover Prediction
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?
3D Modeling and Reconstruction
Clustering Persistent Scatterer Points Based on a Hybrid Distance Metric
CATEGORISE: An Automated Framework for Utilizing the Workforce of the Crowd for Semantic Segmentation of 3D Point Clouds
Zero-Shot remote sensing image super resolution based on image continuity and self-tessellations
A Comparative Survey of Geometric Light Source Calibration Methods
Quantifying point cloud realism through adversarially learned latent representations
Full-Glow: Fully conditional Glow for more realistic image generation
Multidirectional Conjugate Gradients for Scalable Bundle Adjustment. .