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Found 4 papers, 3 papers with code
Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos
As there is 2D-to-3D ambiguity problem in the viewing direction when extracting 3D flow features from 2D video frames, we consider the volume densities as opacity priors that describe the contributions of flow features to the semantics on the frames.
Learning a Category-level Object Pose Estimator without Pose Annotations
Instead of using manually annotated images, we leverage diffusion models (e. g., Zero-1-to-3) to generate a set of images under controlled pose differences and propose to learn our object pose estimator with those images.
Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams
In this paper, we present an analysis of the most effective architectures for the fusion of deep learning models for the prediction of company credit rating classes, by using structured and unstructured datasets of different types.
MonoNeRF: Learning a Generalizable Dynamic Radiance Field from Monocular Videos
More specifically, we learn an implicit velocity field to estimate point trajectory from temporal features with Neural ODE, which is followed by a flow-based feature aggregation module to obtain spatial features along the point trajectory.
