Convolutions are core to deep learning recent success, especially in computer vision. This interactive visualization help to grasp a better understanding of the step-by-step processing. User can select different kernels and input signals among the predefined functions. Another option drag the dots to the wanted level.

2024年6月6日 · While images typically require 2D non-causal modeling, texts utilize 1D causal modeling. This distinction poses significant challenges in constructing unified multi-modal models. This paper explores the feasibility of representing images using 1D causal modeling.

2021年4月1日 · Detailed computational complexity analysis of compact and adaptive 1D CNNs are reported. The benchmark datasets and the principal 1D CNN software are also publicly shared. During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations.

2019年1月15日 · I am currently using a 1D convolutional neural network to classify multivariate time series in Keras. In particular, each instance is represented by 9, equal-length time series (300 points each).

Extensive experiments demonstrate the effectiveness of our De-focus Attention Networks for 1D causal visual representation learning. It achieves comparable or even superior performance to 2D non-causal ViTs across various tasks, including image …

2024年6月6日 · De-focus Attention Networks are causal models equipped with de-focus mechanisms, enabling 1D causal visual representation learning. We first identify an "over-focus" issue observed in causal vision models:

2024年3月1日 · In this paper we introduce a novel deep learning-based algorithm for visual pursuit detection on raw video data which uses a 1-dimensional Convolutional Neural Network (1D-CNN) to detect the pursuit patterns.

This paper explores the feasibility of representing images using 1D causal modeling. We identify an "over-focus" issue in existing 1D causal vision models, where attention overly concentrates on a small proportion of visual tokens.

通过使用可学习的带通滤波器创建多样化的注意模式以及引入大规模且有计划的 drop path 率和全局池化特征的辅助损失来解决现有 1D 因果视觉模型中的“过度聚焦”问题，从而提高模型对多模态任务的性能。

2022年1月10日 · This LibGuide collects resources and tutorials related to data visualization. It is a companion to the visualization services provided by Data and Visualization Services at Duke University Libraries. This taxonomy is based on a data taxonomy from: Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations.