CCNet: Criss-Cross Attention for Semantic Segmentation (1811.11721v2)

Abstract: Contextual information is vital in visual understanding problems, such as semantic segmentation and object detection. We propose a Criss-Cross Network (CCNet) for obtaining full-image contextual information in a very effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. Besides, a category consistent loss is proposed to enforce the criss-cross attention module to produce more discriminative features. Overall, CCNet is with the following merits: 1) GPU memory friendly. Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage. 2) High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85% of the non-local block. 3) The state-of-the-art performance. We conduct extensive experiments on semantic segmentation benchmarks including Cityscapes, ADE20K, human parsing benchmark LIP, instance segmentation benchmark COCO, video segmentation benchmark CamVid. In particular, our CCNet achieves the mIoU scores of 81.9%, 45.76% and 55.47% on the Cityscapes test set, the ADE20K validation set and the LIP validation set respectively, which are the new state-of-the-art results. The source codes are available at \url{https://github.com/speedinghzl/CCNet}.

• The paper introduces a criss-cross attention module that captures dense contextual information along horizontal and vertical paths to reduce computational complexity.
• The paper employs a recurrent criss-cross attention (RCCA) mechanism that progressively models full-image dependencies, leading to superior mIoU scores on multiple benchmarks.
• The paper integrates category consistent loss and extends the architecture to 3D, paving the way for efficient and robust segmentation in diverse applications.

Criss-Cross Attention for Semantic Segmentation: An Analytical Review

The paper "CCNet: Criss-Cross Attention for Semantic Segmentation" by Huang et al. presents an innovative approach to enhancing semantic segmentation performance by proposing a criss-cross attention mechanism. This essay will provide an in-depth overview and analysis of the paper's content, focusing on the technical contributions, numerical results, implications, and potential future directions.

Overview of Criss-Cross Network (CCNet)

Semantic segmentation involves distinguishing and labeling each pixel in an image, thereby requiring substantial contextual information. Traditional fully convolutional networks (FCNs) have limitations in capturing long-range dependencies due to their fixed geometric structures. CCNet addresses this limitation by introducing a criss-cross attention module that efficiently aggregates contextual information.

Criss-Cross Attention Mechanism

The criss-cross attention module operates by capturing contextual information from the criss-cross path—horizontal and vertical directions—of each pixel. This module reduces complexity by having each pixel in the feature map attend to only about $2\sqrt{N}$ other pixels, rather than $N$, leading to significant improvements in computational efficiency. The recurrent criss-cross attention (RCCA) further enhances this by performing multiple iterations, thereby allowing each pixel to eventually attend to the entire image.

Key Contributions

1. Criss-Cross Attention Module: The primary contribution is the design of a criss-cross attention module that captures dense contextual information along criss-cross paths.
2. Recurrent Criss-Cross Attention (RCCA): By recurrently applying the criss-cross attention, the method captures full-image dependencies with reduced computational effort.
3. Category Consistent Loss: A novel loss function is proposed to ensure that feature representations of the same category are close together in the feature space while those of different categories are far apart.
4. Extension to 3D: The CCNet framework is extended to 3D for capturing temporal and spatial information in video data, demonstrating the method's adaptability and robustness.

Experimental Results

The experimental evaluation includes extensive benchmarks on several datasets, including Cityscapes, ADE20K, LIP, and COCO. Key numerical results include:

• On the Cityscapes test set, CCNet achieved a mean Intersection over Union (mIoU) score of 81.9%, outperforming previous methods.
• For ADE20K, CCNet secured a new state-of-the-art result of 45.76% mIoU.
• On the LIP dataset, CCNet achieved an mIoU of 55.47%, marking a significant improvement over existing human parsing methods.
• The RCCA implementation within Mask R-CNN improved performance metrics on the COCO instance segmentation task.

Implications and Future Directions

The paper's contributions have both theoretical and practical significance:

1. Theoretical Implications:
   • The introduction of the criss-cross attention mechanism offers a new perspective on capturing contextual information efficiently.
   • The method redefines how full-image dependencies can be obtained recursively without prohibitive computational costs.
2. Practical Implications:
   • The reduced memory and computational footprint of the criss-cross attention module makes CCNet highly suitable for deployment in resource-constrained environments, such as mobile devices.
   • The improvement in segmentation accuracy across multiple datasets makes CCNet an attractive choice for applications requiring precise segmentation, including autonomous driving and medical imaging.
3. Future Directions:
   • Extended Recurrence: Exploring more sophisticated recurrent schemes or combining with other attention mechanisms could further enhance performance.
   • Domain-Specific Adaptations: Adapting the criss-cross attention mechanism for specific domains, such as medical imaging, where context varies significantly, could prove beneficial.
   • Real-Time Applications: Focusing on optimizing the computational efficiency further to enable real-time applications could be a valuable future endeavor.

Conclusion

The CCNet paper makes substantial strides in the domain of semantic segmentation by leveraging a novel criss-cross attention mechanism supplemented with a recurrent framework. The introduction of category consistent loss adds robustness to the learning process. The extensive validation across diverse datasets highlights the method’s efficacy and sets a new benchmark for future research in this area. The paper paves the way for further exploration into efficient context capturing mechanisms in deep learning frameworks for semantic segmentation and other dense prediction tasks.