WebFlowFormer [huang2024flowformer] introduces a transformer architecture into optical flow estimation and achieves state-of-the-art performance. The core component of FlowFormer is the transformer-based cost-volume encoder. Inspired by the recent success of masked autoencoding (MAE) pretraining in unleashing transformers’ capacity of encoding visual … Web与以前的工作相比，我们的 FlowFormer 的主要特点是调整转换器架构以有效地处理成本量，这是在光流估计社区中广泛探索的紧凑但丰富的表示，用于估计准确的光流。. 我们提出了一种新颖的基于Transformer 的神经网络架构 FlowFormer，用于光流估计，它实现了最先进 ...

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FlowFormer: A Transformer Architecture for Optical Flow - GitHub Pages

http://export.arxiv.org/abs/2303.01237v1 WebFeb 13, 2024 · In this paper, we linearize Transformers free from specific inductive biases based on the flow network theory. We cast attention as the information flow aggregated from the sources (values) to the sinks (results) through the learned flow capacities (attentions). Within this framework, we apply the property of flow conservation with attention ... WebComputer Vision – ECCV 2024: 17th European Conference, Tel Aviv, Israel, October 23–27, 2024, ... a transformer-based neural network architecture for learning optical flow. FlowFormer tokenizes the 4D cost volume built from an image pair, encodes the cost tokens into a cost memory with alternate-group transformer (AGT) layers in a novel ... chuck neeley listings