Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining (CVPR 2024) - cschenxiang/NeRD-Rain
Preconditioners for the Stochastic Training of Implicit Neural Representations https://arxiv.org/abs/2402.08784 Shin-Fang Chng, Hemanth Saratchandran, Simon Lucey University of Adelaide 隐式神经表示已成为一种强大的技术,用于将复杂的连续多维信号编码为神经网络,从而在计算机视觉、机器人和几何学中得到广泛的...
For an overview ofNeRFs, checkout the Survey (Neural Volume Rendering: NeRF And BeyondandNeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review), Blog post (NeRF Explosion 2020) and Collection (awesome-NeRF). For an overview of 3D Gaussian Splatting papers, checkout the Repository ...
To finish the study, participants lastly were redirected to Qualtrics (Qualtrics,2020) or SoSci Survey (Leiner,2024) to respond to some questions about the experiment. They were asked to report technical issues, their ideas on the purpose of the study or if they noticed anything, if and why...
This formulation allows the use of convenient action spaces like the 6-DoF task space of a robot, facilitating the incorporation of advanced scene representations such as Neural Radiance Fields (NeRFs, [3], [4]), as demonstrated by Sóti et al. [5]. These integrations lead to improved ...
Shao, Z., Zhang, X., Fang, M., Lin, Z., Wang, J., Ding, C.: Learning granularity-unified representations for text-to-image person re-identification, in: MM 2022: proceedings of the 30th ACM international conference on multimedia. Lisboa, Portugal, pp. 5566–5574 (2022) Van Den Oord...
Therefore, using this causality as prior knowledge for AI models can encourage models to learn representations that are relatively independent to sensitive attributes, thereby enabling unbiased classification. To evaluate the FairerOPTH, we collected a large and diverse fundus dataset from over 8,405 ...
Problem 1, there is a shift in the distribution of the source and target data representations. The size of a domain shift is often measured by the distance between the source and target subspace representations. Solution 1, learning a feature space transformation to align the source and target ...
15862 3. Methodology In this section, we first briefly review deep implicit shape representations. We then introduce our model, the corre- sponding architecture, and our training approach. 3.1. Background: Deep Implicit Surfaces Implicit shape representations [8, 38...
Additionally, Neural ODEs are used to model the continuous dynamic changes in attention weights, thereby improving the model’s ability to capture long-range emotional dependencies. Simultaneously, we leverage Bidirectional Encoder Representations from Transformers (BERT) embeddings to model the global ...