顶会论文种子 Local Vision Transformers for Efficient and Accurate Spherical Data Processing

Title: Local Vision Transformers for Efficient and Accurate Spherical Data Processing

Abstract: Spherical data processing poses unique challenges due to the distortions introduced by projecting the sphere onto a plane. Existing methods often rely on global spatial mixing, which can be computationally expensive. In this paper, we propose a novel approach that combines the strengths of spherical representations, Vision Transformers, and local spatial mixing. Our method directly processes data on the sphere using a HEALPix grid and employs a Vision Transformer with a restricted attention mechanism to focus on local neighborhoods. This reduces computational complexity while preserving spatial accuracy. To capture global context, we introduce a hierarchical aggregation scheme that gradually integrates local features into global representations. We evaluate our method on various tasks, including semantic segmentation, depth estimation, and object detection in omnidirectional images. Our results demonstrate that our approach achieves state-of-the-art accuracy while being significantly more efficient than existing global mixing methods.

Keywords: spherical data processing, Vision Transformers, local attention, HEALPix, computational efficiency, omnidirectional images, semantic segmentation, depth estimation, object detection

TOC

1. Introduction

1.1 Motivation: Challenges in spherical data processing and the need for efficient and accurate methods.

1.2 Contribution: Introducing our novel approach combining spherical representation, Vision Transformers, and local attention.

1.3 Outline: Overview of the paper's structure.

2. Related Work

2.1 Spherical CNNs: Review of methods adapting CNNs to spherical data. 

2.2 Spherical Transformers: Discussion of recent advances in applying transformers to spherical data. 

2.3 Attention Mechanisms: Overview of different attention mechanisms and their relevance to spherical data. 

Methodology

3.1 Spherical Representation with HEALPix

3.1.1 HEALPix Grid Structure: Explanation of the HEALPix grid and its properties. [Figure 1, Algorithm 1]

3.1.2 Data Representation on HEALPix: How spherical data is mapped onto the HEALPix grid.

3.2 Local Vision Transformer

3.2.1 Vision Transformer Architecture: Description of the chosen Vision Transformer variant. \cite{Liu_2021_ICCV, Liu_2022_CVPR}

3.2.2 Local Attention Mechanism: Details of our local attention mechanism and its implementation. [Figure 2, Algorithm 2]

3.3 Hierarchical Aggregation

3.3.1 Motivation for Hierarchy: Why hierarchical aggregation is important for capturing global context.

3.3.2 Aggregation Schemes: Exploration of different aggregation strategies. [Figure 3, Algorithm 3]

4. Experiments

4.1 Datasets and Tasks

4.1.1 Omnidirectional Image Datasets: Description of the used datasets (e.g., Woodscape).  [Table 1, Figure 4]

4.1.2 Semantic Segmentation Task: Definition and evaluation metrics for semantic segmentation. 

4.1.3 Depth Estimation Task: Definition and evaluation metrics for depth estimation.

4.1.4 Object Detection Task: Definition and evaluation metrics for object detection.

4.2 Implementation Details

4.2.1 Network Architecture and Hyperparameters: Specifics of the implemented model.

4.2.2 Training Procedure: Details of the training process and optimization techniques. (Optional: Algorithm 4)

4.3 Results and Analysis

4.3.1 Quantitative Results: Presentation of accuracy and efficiency metrics. [Tables 2, 3, 4]

4.3.2 Qualitative Results: Visualization of model outputs and comparison to baselines. [Figures 5, 6, 7]

4.3.3 Ablation Studies: Analysis of the impact of different components of our method. [Table 5]

5. Discussion

5.1 Strengths and Limitations: Analysis of the advantages and disadvantages of our approach.

5.2 Comparison to Existing Methods: Discussion of how our method compares to previous work. [Figure 8]

5.3 Future Work: Potential extensions and improvements to our method.

6. Conclusion

6.1 Summary of Findings: Concise overview of the key results.

6.2 Impact and Implications: Discussion of the broader implications of our work.

Figures:

Figure 1: Illustration of the HEALPix grid structure, highlighting its hierarchical and equal-area properties.

Figure 2: Visualization of the local attention mechanism applied to spherical data on the HEALPix grid.

Figure 3: Schematic diagram of different hierarchical aggregation schemes.

Figure 4: Example images from the omnidirectional image datasets used in the experiments.

Figure 5: Qualitative results for semantic segmentation, showing the model's predictions on example images.

Figure 6: Qualitative results for depth estimation, visualizing the predicted depth maps.

Figure 7: Qualitative results for object detection, showing the detected objects in omnidirectional images.

Figure 8: Plots comparing the accuracy of our method to baseline methods on different tasks.

Figure 9: Graphs illustrating the computational efficiency of our method compared to global mixing approaches.

Tables:

Table 1: Summary of the omnidirectional image datasets used in the experiments, including their size and characteristics.

Table 2: Quantitative results for semantic segmentation, presenting mIoU scores for different methods and datasets.

Table 3: Quantitative results for depth estimation, showing ARD values for different methods and datasets.

Table 4: Quantitative results for object detection, presenting AP scores for different methods and datasets.

Table 5: Ablation study results, analyzing the impact of different components of our method on accuracy and efficiency.

Algorithms

Algorithm 1: Pseudo-code for mapping spherical data onto the HEALPix grid.

Algorithm 2: Pseudo-code for the local attention mechanism within the Vision Transformer.

Algorithm 3: Pseudo-code for the hierarchical aggregation scheme.

Algorithm 4: (Optional) If a specific training or optimization algorithm is used, include its pseudo-code here.