Semantic Correlation Promoted Shape-Variant Context for Segmentation (1909.02651v1)

Abstract: Context is essential for semantic segmentation. Due to the diverse shapes of objects and their complex layout in various scene images, the spatial scales and shapes of contexts for different objects have very large variation. It is thus ineffective or inefficient to aggregate various context information from a predefined fixed region. In this work, we propose to generate a scale- and shape-variant semantic mask for each pixel to confine its contextual region. To this end, we first propose a novel paired convolution to infer the semantic correlation of the pair and based on that to generate a shape mask. Using the inferred spatial scope of the contextual region, we propose a shape-variant convolution, of which the receptive field is controlled by the shape mask that varies with the appearance of input. In this way, the proposed network aggregates the context information of a pixel from its semantic-correlated region instead of a predefined fixed region. Furthermore, this work also proposes a labeling denoising model to reduce wrong predictions caused by the noisy low-level features. Without bells and whistles, the proposed segmentation network achieves new state-of-the-arts consistently on the six public segmentation datasets.

• The paper introduces paired convolution and shape masks to dynamically adjust the convolution receptive field based on learned semantic correlations between pixels, moving beyond static spatial contexts.
• The Shape-Variant Convolution utilizes these semantic shape masks to control the convolution process, effectively modeling semantically meaningful context with improved discrimination.
• The proposed network achieves state-of-the-art results on six public datasets, empirically validating the efficacy of this novel semantic-driven context aggregation method for segmentation.

Semantic Correlation Promoted Shape-Variant Context for Segmentation

This paper, "Semantic Correlation Promoted Shape-Variant Context for Segmentation," presents a novel computational approach for semantic segmentation in scene images. The authors introduce a method to dynamically generate scale- and shape-variant semantic masks for each pixel, enhancing the contextual region and segmentation performance. This work is notable for its emphasis on semantic-driven context aggregation, departing from traditional fixed spatial regions, and offering a method that adapts the receptive field size and shape based on learned semantic correlations.

Key Contributions

1. Paired Convolution and Shape Mask Creation: At the core of the methodology is the novel paired convolution designed to infer semantic correlations between pixels. Each pixel's context is narrowed or expanded based on its semantic relationships within the image, as determined by a calculated shape mask. This mask allows the convolution layer's receptive field to be dynamic, adjusting to the semantic context rather than static spatial dimensions.
2. Shape-Variant Convolution (SV Conv): The authors propose the shape-variant convolution, which uses these semantic shape masks to control the convolution process. By varying the receptive fields based on the shape mask, the convolution layer can effectively model semantically meaningful context with improved discrimination.
3. Labeling Denoising Model: To tackle the issue of noisy predictions from low-level features, the paper introduces a labeling denoising mechanism. This model utilizes high-level features to filter out erroneous classifications enhancing robustness in predictions.
4. Empirical Validation: The proposed segmentation network attained superior state-of-the-art results across six public datasets: COCO-Stuff, SIFT-Flow, CamVid, PASCAL-Person-Part, PASCAL-Context, and Cityscapes, consistently demonstrating the efficacy of semantic-driven context aggregation.

Implications and Discussion

The implications of this research are multi-faceted:

• Practical Advantages: By dynamically adjusting based on semantic correlation, this approach reduces the computational inefficiencies associated with static, predefined contexts. Its application across various segmentation tasks showcases its adaptability and robustness, providing improvements in pixel accuracy and mean Intersection over Union (IoU).
• Theory and Future Directions: The integration of semantic information directly into the convolution process suggests a potential paradigm shift in semantic segmentation strategies. Future research could explore further refinements in semantic mask generation and its potential application to other neural network architectures beyond conventional CNNs. Additionally, the proposed shape-variant context model opens avenues for incorporating multi-modal data, such as integration of depth and temporal sequences, exploiting such additional dimensions for enhanced segmentation capabilities.

Conclusion

This paper offers a significant step forward in semantic segmentation technology, shifting focus from spatial to semantic-driven context aggregation. By introducing the paired convolution and shape-variant convolution, the authors offer tools that dynamically adapt to the semantic nuances of scene imagery. This contribution not only sets a benchmark for segmentation performance but also paves the way for future exploration into contextually adaptive neural networks, marking a shift towards more intelligent and semantically aware AI systems.