← Learning Types
Deep Learning › Learning Types ›

Representation Learning

4516 directly classified papers

Papers per year

Papers

NeRF Director: Revisiting View Selection in Neural Volume Rendering CVPR 2024
Orthogonal Dictionary Guided Shape Completion Network for Point Cloud AAAI 2024
Social Physics Informed Diffusion Model for Crowd Simulation AAAI 2024
Knowledge-Aware Neuron Interpretation for Scene Classification AAAI 2024
Forecasting Bimanual Object Manipulation Sequences from Unimanual Observations AAAI 2024
NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs CVPR 2024
MFP: Making Full Use of Probability Maps for Interactive Image Segmentation CVPR 2024
FADES: Fair Disentanglement with Sensitive Relevance CVPR 2024
Boosting Image Restoration via Priors from Pre-trained Models CVPR 2024
Rethinking Inductive Biases for Surface Normal Estimation CVPR 2024
ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering CVPR 2024
Activity-Biometrics: Person Identification from Daily Activities CVPR 2024
Adaptive Slot Attention: Object Discovery with Dynamic Slot Number CVPR 2024
A New Benchmark and Model for Challenging Image Manipulation Detection AAAI 2024
Self-Supervised Representation Learning with Meta Comprehensive Regularization AAAI 2024
LISR: Learning Linear 3D Implicit Surface Representation Using Compactly Supported Radial Basis Functions AAAI 2024
NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic Radiographs AAAI 2024
Test-Time Linear Out-of-Distribution Detection CVPR 2024
Probing the 3D Awareness of Visual Foundation Models CVPR 2024
Mind Artist: Creating Artistic Snapshots with Human Thought CVPR 2024
RMem: Restricted Memory Banks Improve Video Object Segmentation CVPR 2024
Learning Adaptive Spatial Coherent Correlations for Speech-Preserving Facial Expression Manipulation CVPR 2024
Individuation in Neural Models with and without Visual Grounding EMNLP 2024
Measuring Per-Unit Interpretability at Scale Without Humans NIPS 2024
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control NIPS 2024