Search Results for author:
Found 8 papers, 4 papers with code
Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method
We explore the oscillatory behavior observed in inversion methods applied to large-scale text-to-image diffusion models, with a focus on the "Flux" model.
PPLNs: Parametric Piecewise Linear Networks for Event-Based Temporal Modeling and Beyond
We present Parametric Piecewise Linear Networks (PPLNs) for temporal vision inference.
A Condition Number for Joint Optimization of Cycle-Consistent Networks
A recent trend in optimizing maps such as dense correspondences between objects or neural networks between pairs of domains is to optimize them jointly.
Learning Transformation Synchronization
Our approach alternates between transformation synchronization using weighted relative transformations and predicting new weights of the input relative transformations using a neural network.
Path-Invariant Map Networks
Optimizing a network of maps among a collection of objects/domains (or map synchronization) is a central problem across computer vision and many other relevant fields.
Joint Map and Symmetry Synchronization
Most existing techniques in map computation (e. g., in the form of feature or dense correspondences) assume that the underlying map between an object pair is unique.
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
We introduce a principled approach for simultaneous mapping and clustering (SMAC) for establishing consistent maps across heterogeneous object collections (e. g., 2D images or 3D shapes).
Translation Synchronization via Truncated Least Squares
In this paper, we introduce a robust algorithm, \textsl{TranSync}, for the 1D translation synchronization problem, in which the aim is to recover the global coordinates of a set of nodes from noisy measurements of relative coordinates along an observation graph.
