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Found 10 papers, 2 papers with code
Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior
Monocular depth estimation from a single image is an ill-posed problem for computer vision due to insufficient reliable cues as the prior knowledge.
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers
In most solvers, variable activities are preserved across restart boundaries, resulting in solvers continuing to search parts of the assignment tree that are not far from the one immediately prior to a restart.
Layered and Staged Monte Carlo Tree Search for SMT Strategy Synthesis
Our method treats strategy synthesis as a sequential decision-making process, whose search tree corresponds to the strategy space, and employs MCTS to navigate this vast search space.
AlphaMapleSAT: An MCTS-based Cube-and-Conquer SAT Solver for Hard Combinatorial Problems
We perform an extensive comparison of AlphaMapleSAT against the March CnC solver on challenging combinatorial problems such as the minimum Kochen-Specker and Ramsey problems.
City Scene Super-Resolution via Geometric Error Minimization
Super-resolution techniques are crucial in improving image granularity, particularly in complex urban scenes, where preserving geometric structures is vital for data-informed cultural heritage applications.
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects
In this work, we construct a joint inter-frame-supervised depth and optical flow estimation framework, which predicts depths in various motions by minimizing pixel wrap errors in bilateral photometric re-projections and optical vectors.
Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth Estimation
In this work, a Pyramid Frequency Network(PFN) with Spatial Attention Residual Refinement Module(SARRM) is proposed to deal with the weak robustness of existing deep-learning methods.
Delta family approach for the stochastic control problems of utility maximization
We obtain an explicit series representation of the value function, whose coefficients are expressed through integration of the value function at a later time point against a chosen basis function.
Dense U-net for super-resolution with shuffle pooling layer
By doing so, we effectively replace the handcrafted filter in the SISR pipeline with more lossy down-sampling filters specifically trained for each feature map, whilst also reducing the information loss of the overall SISR operation.
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss
To solve this problem, the mixed gradient error, which is composed by MSE and a weighted mean gradient error, is proposed in this work and applied to a modified U-net network as the loss function.
