Efficient Post-Training Quantization for Referring Image Segmentation Models

核心概念

An effective and efficient post-training quantization framework, PTQ4RIS, is proposed to enable the deployment of large multi-modal referring image segmentation models on resource-constrained edge devices.

要約

The paper presents an effective and efficient post-training quantization (PTQ) framework, termed PTQ4RIS, for Referring Image Segmentation (RIS) models. RIS aims to segment the object referred to by a given sentence in an image by understanding both visual and linguistic information. However, existing RIS methods often focus on achieving top performance, overlooking practical considerations for deployment on resource-limited edge devices.

The authors first conduct an in-depth analysis of the root causes of performance degradation in RIS model quantization. They identify two key challenges: 1) the non-normal distribution of post-Softmax and post-GeLU activations in the visual encoder, and 2) the presence of significant outliers in the text encoder activations.

To address these issues, the authors propose two novel quantization techniques:

1. Dual-Region Quantization (DRQ) for the visual encoder, which quantizes the activation values in two separate regions to better capture the bimodal distribution.
2. Reorder-based Outlier-Retained Quantization (RORQ) for the text encoder, which iteratively partitions the activation values into groups and dynamically quantizes each group using distinct scale factors to handle the outliers.

For the quantization-friendly feature fusion and decoder modules, the authors apply a simple uniform quantization approach.

Extensive experiments on three RIS benchmark datasets with different bit-width settings (from 8 to 4 bits) demonstrate the superior performance of the proposed PTQ4RIS framework. Notably, the PTQ INT8 model's accuracy is almost on par with the full-precision (FP32) model on some datasets, and the performance degradation is minimal even in the W6A6 and W4A8 settings.

統計

The paper does not provide any specific numerical data or statistics. The key results are presented in the form of quantitative performance metrics, such as MIoU and OIoU, across different bit-width settings and benchmark datasets.

引用

"Referring Image Segmentation (RIS), aims to segment the object referred by a given sentence in an image by understanding both visual and linguistic information."
"Existing RIS methods tend to explore top-performance models, disregarding considerations for practical applications on resources-limited edge devices."
"We unveil the root causes of performance collapse in the quantization of the RIS model, revealing that challenges primarily arise from the unique activation distributions of post-Softmax and post-GeLU in visual encoder, along with activation outliers in text encoder."

抽出されたキーインサイト

PTQ4RIS: Post-Training Quantization for Referring Image Segmentation

by Xiaoyan Jian... 場所 arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.17020.pdf

深掘り質問

How can the proposed PTQ4RIS framework be extended to handle more complex or diverse referring expressions, such as those involving multiple objects or spatial relationships?

The PTQ4RIS framework can be extended to accommodate more complex referring expressions by incorporating several enhancements. Firstly, the model could be adapted to support multi-object segmentation by integrating a mechanism for handling multiple queries simultaneously. This could involve modifying the text encoder to process a list of referring expressions, allowing it to generate distinct segmentation masks for each object mentioned. Additionally, the framework could leverage attention mechanisms to capture spatial relationships between objects, enabling the model to understand context better. For instance, incorporating spatial reasoning modules that analyze the relative positions of objects could enhance the model's ability to interpret expressions like "the cup next to the plate." Furthermore, the dual-region quantization (DRQ) and reorder-based outlier-retained quantization (RORQ) methods could be refined to account for the increased complexity in activation distributions that arise from processing multiple objects, ensuring that quantization errors remain minimal. Overall, these modifications would enhance the robustness of PTQ4RIS in handling diverse and intricate referring expressions.

What are the potential limitations or trade-offs of the DRQ and RORQ methods, and how could they be further improved to achieve even better quantization performance?

The DRQ and RORQ methods, while effective, have potential limitations and trade-offs. One limitation of DRQ is its reliance on the assumption that activation distributions can be effectively partitioned into distinct regions. In scenarios where the activation distributions are highly irregular or overlap significantly, this method may not yield optimal quantization performance. Additionally, the complexity of managing multiple scale factors for different regions can introduce computational overhead, potentially negating some of the efficiency gains from quantization. On the other hand, RORQ's iterative approach to handling outliers may lead to increased calibration time, especially in cases with large datasets or complex distributions. To improve these methods, future work could explore adaptive quantization strategies that dynamically adjust the number of regions or the thresholds for outlier detection based on the input data characteristics. Implementing machine learning techniques to predict optimal quantization parameters could also enhance performance while reducing the need for extensive calibration.

Given the success of PTQ4RIS in the RIS domain, how could the insights and techniques be applied to other multi-modal computer vision tasks, such as visual question answering or image captioning?

The insights and techniques from PTQ4RIS can be effectively applied to other multi-modal computer vision tasks, such as visual question answering (VQA) and image captioning. In VQA, the model must integrate visual features with textual questions, similar to how PTQ4RIS combines visual and linguistic information for referring image segmentation. The dual-region quantization (DRQ) approach could be adapted to handle the unique activation distributions that arise from processing both images and questions, ensuring that quantization does not compromise the model's ability to understand complex queries. Similarly, the reorder-based outlier-retained quantization (RORQ) method could be utilized to manage the diverse range of activations generated by different question types, enhancing the model's robustness against quantization errors. For image captioning, the techniques could be employed to optimize the model's performance while maintaining the ability to generate coherent and contextually relevant descriptions. By leveraging the quantization strategies developed in PTQ4RIS, other multi-modal tasks can achieve improved efficiency and performance, facilitating deployment on resource-constrained devices while maintaining high accuracy.