Welsch Based Multiview Disparity Estimation

• URL: http://arxiv.org/abs/2110.00803v1
• Date: Sat, 2 Oct 2021 13:44:49 GMT
• Title: Welsch Based Multiview Disparity Estimation
• Authors: James L. Gray, Aous T. Naman, David S. Taubman
• Abstract summary: We experimentally identify occlusions as a key challenge for disparity estimation for applications with high numbers of views. We propose the use of a Welsch loss function for the data term in a global variational framework for disparity estimation.
• Score: 0.8594140167290096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we explore disparity estimation from a high number of views. We experimentally identify occlusions as a key challenge for disparity estimation for applications with high numbers of views. In particular, occlusions can actually result in a degradation in accuracy as more views are added to a dataset. We propose the use of a Welsch loss function for the data term in a global variational framework for disparity estimation. We also propose a disciplined warping strategy and a progressive inclusion of views strategy that can reduce the need for coarse to fine strategies that discard high spatial frequency components from the early iterations. Experimental results demonstrate that the proposed approach produces superior and/or more robust estimates than other conventional variational approaches.

Related papers

• Regularized Contrastive Partial Multi-view Outlier Detection [76.77036536484114]
  We propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD) In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-02T14:34:27Z)
• Spectral Representation for Causal Estimation with Hidden Confounders [33.148766692274215]
  We address the problem of causal effect estimation where hidden confounders are present. Our approach uses a singular value decomposition of a conditional expectation operator, followed by a saddle-point optimization problem.
  arXiv  Detail & Related papers  (2024-07-15T05:39:56Z)
• Hierarchical Uncertainty Exploration via Feedforward Posterior Trees [25.965665666173038]
  We introduce a new approach for visualizing posteriors across multiple levels of granularity using tree-valued predictions. Our method predicts a tree-valued hierarchical summarization of the posterior distribution for any input measurement, in a single forward pass of a neural network.
  arXiv  Detail & Related papers  (2024-05-24T17:06:51Z)
• Latent Embedding Clustering for Occlusion Robust Head Pose Estimation [7.620379605206596]
  Head pose estimation has become a crucial area of research in computer vision given its usefulness in a wide range of applications. One of the most difficult challenges in this field is managing head occlusions that frequently take place in real-world scenarios. We propose a novel and efficient framework that is robust in real world head occlusion scenarios.
  arXiv  Detail & Related papers  (2024-03-29T15:57:38Z)
• Loss Shaping Constraints for Long-Term Time Series Forecasting [79.3533114027664]
  We present a Constrained Learning approach for long-term time series forecasting that respects a user-defined upper bound on the loss at each time-step. We propose a practical Primal-Dual algorithm to tackle it, and aims to demonstrate that it exhibits competitive average performance in time series benchmarks, while shaping the errors across the predicted window.
  arXiv  Detail & Related papers  (2024-02-14T18:20:44Z)
• Bayesian Graph Contrastive Learning [55.36652660268726]
  We propose a novel perspective of graph contrastive learning methods showing random augmentations leads to encoders. Our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. We show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-15T01:45:32Z)
• Revisiting Consistency Regularization for Semi-Supervised Learning [80.28461584135967]
  We propose an improved consistency regularization framework by a simple yet effective technique, FeatDistLoss. Experimental results show that our model defines a new state of the art for various datasets and settings.
  arXiv  Detail & Related papers  (2021-12-10T20:46:13Z)
• Monte Carlo Dropout Ensembles for Robust Illumination Estimation [94.14796147340041]
  Computational color constancy is a preprocessing step used in many camera systems. We propose to aggregate different deep learning methods according to their output uncertainty. The proposed framework leads to state-of-the-art performance on INTEL-TAU dataset.
  arXiv  Detail & Related papers  (2020-07-20T13:56:14Z)
• Learning Overlapping Representations for the Estimation of Individualized Treatment Effects [97.42686600929211]
  Estimating the likely outcome of alternatives from observational data is a challenging problem. We show that algorithms that learn domain-invariant representations of inputs are often inappropriate. We develop a deep kernel regression algorithm and posterior regularization framework that substantially outperforms the state-of-the-art on a variety of benchmarks data sets.
  arXiv  Detail & Related papers  (2020-01-14T12:56:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.