indsigt - Multi-modal perception - # Modality-Incomplete Scene Segmentation (MISS)

Robust Multi-Modal Semantic Segmentation with Modality-Incomplete Scenarios

Q: How can the proposed methods be extended to handle more diverse modalities beyond the ones considered in this work, such as thermal, polarization, or radar data

The proposed methods can be extended to handle more diverse modalities by adapting the Missing-aware Modal Switch (MMS) strategy and Fourier Prompt Tuning (FPT) approach to accommodate additional sensor inputs. For instance, when incorporating thermal data, the MMS strategy can be modified to include binary switches for thermal modalities, similar to how it handles RGB, Depth, LiDAR, and Event modalities. This would allow the model to adapt to scenarios where thermal data is missing or corrupted. In the case of Fourier Prompt Tuning, the approach can be enhanced to incorporate spectral information specific to thermal, polarization, or radar data. By extracting relevant spectral features unique to each modality, the prompts can be tuned to capture the distinctive characteristics of these sensor inputs. This would involve customizing the FFT process to extract and integrate the spectral information from the new modalities into the prompt tokens, enabling the model to effectively utilize the diverse data sources for improved segmentation performance.

Q: What are the potential challenges and limitations of the Fourier Prompt Tuning approach in terms of its ability to capture and represent spatial information compared to traditional prompt tuning methods

One potential challenge and limitation of the Fourier Prompt Tuning approach in capturing spatial information compared to traditional prompt tuning methods is the trade-off between spectral and spatial representation. While FFT is effective in extracting global spectral information, it may not capture fine-grained spatial details as effectively as traditional spatial token representations. This could result in a loss of spatial context and intricacies in the segmentation task, especially in scenarios where spatial features play a crucial role in accurate predictions. To address this limitation, a hybrid approach combining both spectral and spatial representations in the prompt tokens could be explored. By integrating spatial information directly into the prompt tokens alongside the spectral features extracted through FFT, the model can maintain a balance between capturing global spectral patterns and local spatial details. This hybrid approach would require careful optimization and tuning to ensure that the model effectively leverages both types of information for robust segmentation performance.

Q: Given the focus on parameter efficiency, how could the proposed techniques be further optimized to reduce the computational and memory footprint for real-world deployment on resource-constrained platforms

To further optimize the proposed techniques for parameter efficiency and reduce the computational and memory footprint for real-world deployment on resource-constrained platforms, several strategies can be implemented: Quantization and Pruning: Implement quantization techniques to reduce the precision of model parameters and prune unnecessary connections to decrease the overall model size and computational requirements. Knowledge Distillation: Utilize knowledge distillation methods to transfer knowledge from a larger, more complex model to a smaller, more efficient model, maintaining performance while reducing the number of parameters. Sparse Attention Mechanisms: Explore sparse attention mechanisms to focus computational resources on relevant parts of the input data, reducing the overall computational cost of processing multi-modal inputs. Hardware Acceleration: Utilize specialized hardware accelerators, such as GPUs or TPUs, optimized for deep learning tasks to improve the efficiency of model inference on resource-constrained platforms. By incorporating these optimization strategies, the proposed methods can be further refined to ensure efficient deployment in real-world applications with limited computational resources.

Kernekoncepter

A comprehensive task, Modality-Incomplete Scene Segmentation (MISS), is studied to cover both system-level modality missing and sensor-level modality errors in multi-modal semantic segmentation. A Missing-aware Modal Switch (MMS) training strategy and a Fourier Prompt Tuning (FPT) method are proposed to address these challenges, enabling efficient and robust multi-modal perception.

Resumé

The paper introduces the Modality-Incomplete Scene Segmentation (MISS) task, which encompasses both system-level modality absence and sensor-level modality errors in multi-modal semantic segmentation. To mitigate the reliance on predominant modalities and differentiate the utilization of dense and sparse modalities, the authors devise the Missing-aware Modal Switch (MMS) training strategy.

Furthermore, the paper proposes Fourier Prompt Tuning (FPT), a novel approach that leverages Fast Fourier Transformation (FFT) to extract global spectral information and incorporate it into a limited number of learnable prompts. This enables efficient fine-tuning for MISS scenarios while maintaining robustness against all modality-incomplete conditions.

Extensive experiments on the DeLiVER and Cityscapes datasets demonstrate the efficacy of the proposed methods. The MMS training strategy leads to over 20% mIoU enhancement in scenarios lacking the predominant modality, while maintaining performance with full modalities. The FPT model achieves a 5.84% mIoU improvement over the prior state-of-the-art parameter-efficient methods in modality missing, and surpasses baselines in all sensor failure cases.

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Statistik

The paper reports the following key metrics:

On the DeLiVER dataset, the proposed FPT model achieves a 5.84% mIoU improvement over the prior state-of-the-art parameter-efficient methods in modality missing scenarios.
On the Cityscapes dataset, the FPT model trained with the MMS strategy boosts the performance by up to 47.5% in mIoU when the predominant RGB modality is absent.

Citater

"Integrating information from multiple modalities enhances the robustness of scene perception systems in autonomous vehicles, providing a more comprehensive and reliable sensory framework."
"Modality-Incomplete Scene Segmentation (MISS) expands upon our previous work, DeLiVER [2], which addressed only sensor-level failures."
"Our observations, illustrated with arbitrary missing modalities in Fig. 1b, indicate a significant fragility in the performance of multi-modal networks for semantic segmentation when a predominant dense modality (e.g., RGB or Depth) is missing."

Vigtigste indsigter udtrukket fra

Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation

by Ruiping Liu,... kl. arxiv.org 04-12-2024

https://arxiv.org/pdf/2401.16923.pdf

Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation

Dybere Forespørgsler

How can the proposed methods be extended to handle more diverse modalities beyond the ones considered in this work, such as thermal, polarization, or radar data

The proposed methods can be extended to handle more diverse modalities by adapting the Missing-aware Modal Switch (MMS) strategy and Fourier Prompt Tuning (FPT) approach to accommodate additional sensor inputs. For instance, when incorporating thermal data, the MMS strategy can be modified to include binary switches for thermal modalities, similar to how it handles RGB, Depth, LiDAR, and Event modalities. This would allow the model to adapt to scenarios where thermal data is missing or corrupted.
In the case of Fourier Prompt Tuning, the approach can be enhanced to incorporate spectral information specific to thermal, polarization, or radar data. By extracting relevant spectral features unique to each modality, the prompts can be tuned to capture the distinctive characteristics of these sensor inputs. This would involve customizing the FFT process to extract and integrate the spectral information from the new modalities into the prompt tokens, enabling the model to effectively utilize the diverse data sources for improved segmentation performance.

What are the potential challenges and limitations of the Fourier Prompt Tuning approach in terms of its ability to capture and represent spatial information compared to traditional prompt tuning methods

One potential challenge and limitation of the Fourier Prompt Tuning approach in capturing spatial information compared to traditional prompt tuning methods is the trade-off between spectral and spatial representation. While FFT is effective in extracting global spectral information, it may not capture fine-grained spatial details as effectively as traditional spatial token representations. This could result in a loss of spatial context and intricacies in the segmentation task, especially in scenarios where spatial features play a crucial role in accurate predictions.
To address this limitation, a hybrid approach combining both spectral and spatial representations in the prompt tokens could be explored. By integrating spatial information directly into the prompt tokens alongside the spectral features extracted through FFT, the model can maintain a balance between capturing global spectral patterns and local spatial details. This hybrid approach would require careful optimization and tuning to ensure that the model effectively leverages both types of information for robust segmentation performance.

Given the focus on parameter efficiency, how could the proposed techniques be further optimized to reduce the computational and memory footprint for real-world deployment on resource-constrained platforms

To further optimize the proposed techniques for parameter efficiency and reduce the computational and memory footprint for real-world deployment on resource-constrained platforms, several strategies can be implemented:

Quantization and Pruning: Implement quantization techniques to reduce the precision of model parameters and prune unnecessary connections to decrease the overall model size and computational requirements.

Knowledge Distillation: Utilize knowledge distillation methods to transfer knowledge from a larger, more complex model to a smaller, more efficient model, maintaining performance while reducing the number of parameters.

Sparse Attention Mechanisms: Explore sparse attention mechanisms to focus computational resources on relevant parts of the input data, reducing the overall computational cost of processing multi-modal inputs.

Hardware Acceleration: Utilize specialized hardware accelerators, such as GPUs or TPUs, optimized for deep learning tasks to improve the efficiency of model inference on resource-constrained platforms.

By incorporating these optimization strategies, the proposed methods can be further refined to ensure efficient deployment in real-world applications with limited computational resources.