Towards Efficient Scene Understanding via Squeeze Reasoning (2011.03308v3)

Abstract: Graph-based convolutional model such as non-local block has shown to be effective for strengthening the context modeling ability in convolutional neural networks (CNNs). However, its pixel-wise computational overhead is prohibitive which renders it unsuitable for high resolution imagery. In this paper, we explore the efficiency of context graph reasoning and propose a novel framework called Squeeze Reasoning. Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector and perform reasoning within the single vector where the computation cost can be significantly reduced. Specifically, we build the node graph in the vector where each node represents an abstract semantic concept. The refined feature within the same semantic category results to be consistent, which is thus beneficial for downstream tasks. We show that our approach can be modularized as an end-to-end trained block and can be easily plugged into existing networks. {Despite its simplicity and being lightweight, the proposed strategy allows us to establish the considerable results on different semantic segmentation datasets and shows significant improvements with respect to strong baselines on various other scene understanding tasks including object detection, instance segmentation and panoptic segmentation.} Code is available at \url{https://github.com/lxtGH/SFSegNets}.

• The paper introduces a novel framework that compresses pixel information into a channel-wise global vector to drastically reduce computational costs.
• It achieves notable results, such as 82.2% mIoU on the Cityscapes test set with a ResNet-101 backbone, outperforming traditional non-local blocks.
• The method's modular design makes it versatile for tasks like semantic segmentation, object detection, and real-time applications in autonomous driving.

Efficient Scene Understanding through Squeeze Reasoning

This paper investigates the efficiency of context graph reasoning in convolutional neural networks (CNNs) and introduces the Squeeze Reasoning framework for scene understanding. The primary objective is to address the prohibitive computational overhead associated with pixel-wise graph-based convolutional models like the non-local block, particularly when dealing with high-resolution imagery. The proposed method provides a more resource-efficient alternative capable of capturing global context without the extensive computational demands.

Squeeze Reasoning Framework

Squeeze Reasoning is a novel approach that avoids pixel-wise information propagation. Instead, it compacts the input features into a channel-wise global vector, on which reasoning is performed. The node graph constructed in this vectorization process treats each node as an abstract semantic concept, facilitating consistency in features under the same semantic category. This method can be modularized and integrated into existing networks, making it versatile and applicable across various tasks, such as semantic segmentation, object detection, and instance segmentation.

Numerical Performance

The paper showcases the Squeeze Reasoning framework's significant improvements over strong baselines across multiple datasets, including Cityscapes, Pascal Context, ADE20K, and CamVid. For instance, on the Cityscapes test set, the model achieves 82.2% mIoU using a ResNet-101 backbone—a marked enhancement compared to previous methods. Additionally, the framework demonstrates superior speed and accuracy trade-offs, operating at 65 FPS with full-resolution inputs (1024x2048).

Efficiency and Effectiveness

One of the standout features of the Squeeze Reasoning approach is its efficiency. By moving reasoning operations from the spatial domain to the channel-wise domain, the model significantly reduces computational costs. In particular, the paper notes that Squeeze Reasoning uses substantially fewer floating-point operations and memory compared to traditional non-local blocks.

Broader Implications

Practically, the improved efficiency and reduced computational demands of Squeeze Reasoning make it highly applicable for real-time applications, such as autonomous driving and other scenario-based modeling tasks that require fast and reliable scene understanding. Theoretically, this work opens new avenues in understanding the relationship between channel attention mechanisms and spatial reasoning, potentially influencing future developments in machine learning models.

Future Directions

Potential future directions include exploring cross-layer reasoning and expanding the application of Squeeze Reasoning to other deep learning architectures. The integration of this reasoning framework into different domains or network architectures could broaden its applicability even further, enhancing a wide range of AI tasks.

In summary, Squeeze Reasoning offers an efficient, scalable, and highly effective method for global context modeling in scene understanding, providing substantial improvements across various tasks without incurring excessive computational costs. This paper represents a significant step forward in the pursuit of more efficient AI models.