CVPR 2024 accepted papers on Probabilistic and scientific modeling
28 of the 2,716 papers accepted at CVPR 2024 (1.0%) are on Probabilistic and scientific modeling, compared with 1.2% at CVPR 2023. They fall into 6 topics.
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Topics and papers
Molecules and proteins · 13 papers
- DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting (68 citations)
- Neural Markov Random Field for Stereo Matching (44 citations)
- CaDeT: a Causal Disentanglement Approach for Robust Trajectory Prediction in Autonomous Driving (26 citations)
- Uncertainty-aware Action Decoupling Transformer for Action Anticipation (14 citations)
- Clustering for Protein Representation Learning (11 citations)
- SketchINR: A First Look into Sketches as Implicit Neural Representations (11 citations)
- Finsler-Laplace-Beltrami Operators with Application to Shape Analysis (9 citations)
- Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling (7 citations)
- Human Motion Prediction Under Unexpected Perturbation (6 citations)
- Learned Trajectory Embedding for Subspace Clustering (5 citations)
- Smart Help: Strategic Opponent Modeling for Proactive and Adaptive Robot Assistance in Households (5 citations)
- The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes (3 citations)
and 1 more in the interactive map.
Variational and generative sampling · 5 papers
- Differentiable Information Bottleneck for Deterministic Multi-view Clustering (27 citations)
- Latent Modulated Function for Computational Optimal Continuous Image Representation (16 citations)
- Learned Lossless Image Compression based on Bit Plane Slicing (11 citations)
- Accelerating Neural Field Training via Soft Mining (7 citations)
- Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose Modeling (2 citations)
Uncertainty and Gaussian processes · 5 papers
- Producing and Leveraging Online Map Uncertainty in Trajectory Prediction (37 citations)
- Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models (16 citations)
- Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture (8 citations)
- Uncertainty Visualization via Low-Dimensional Posterior Projections (1 citation)
- Epistemic Uncertainty Quantification For Pre-Trained Neural Networks
Dynamical systems · 2 papers
Causal discovery · 2 papers
- Robust Emotion Recognition in Context Debiasing (29 citations)
- Bi-Causal: Group Activity Recognition via Bidirectional Causality (10 citations)