Fully Convolutional Networks for Semantic Segmentation (1605.06211v1)

Abstract: Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves improved segmentation of PASCAL VOC (30% relative improvement to 67.2% mean IU on 2012), NYUDv2, SIFT Flow, and PASCAL-Context, while inference takes one tenth of a second for a typical image.

• The paper introduces a novel fully convolutional architecture that replaces fully connected layers with convolutions, enabling efficient end-to-end segmentation.
• The paper implements skip connections that fuse deep semantic and shallow appearance cues, achieving a mean IU of 67.2% on benchmarks like PASCAL VOC 2012.
• The paper demonstrates how FCNs simplify segmentation pipelines by eliminating complex pre- and post-processing, paving the way for broader applications in computer vision.

Fully Convolutional Networks for Semantic Segmentation

The paper "Fully Convolutional Networks for Semantic Segmentation" by Shelhamer, Long, and Darrell discusses the development and application of Fully Convolutional Networks (FCNs). The primary focus is on leveraging convolutional networks to enhance semantic segmentation, a critical task in computer vision requiring pixelwise classification.

Overview

FCNs represent an evolution in convolutional neural network (CNN) architecture, tailored to handle arbitrary input sizes and produce correspondingly-sized output. This is achieved by converting traditional fully connected layers into convolutions, thus maintaining spatial hierarchies and enabling efficient feedforward and backpropagation computations.

Key Contributions

1. FCN Architecture:
   • The authors adapt well-established classification networks (AlexNet, VGGNet, GoogLeNet) into fully convolutional frameworks capable of pixel-level segmentation.
   • They introduce in-network upsampling layers, which allow FCNs to generate dense output maps from coarse predictions. This avoids the inefficiency and potential information loss inherent in alternative patchwise methods.
2. Skip Architectures:
   • A novel skip layer architecture is proposed, which fuses semantic information from deep, coarse layers with appearance information from shallow, fine layers.
   • This approach enhances segmentation accuracy by combining high-level contextual knowledge and detailed local cues, demonstrated through a multi-stream architecture (FCN-32s, FCN-16s, and FCN-8s).
3. Experimental Evaluation:
   • The authors report significant improvements over baseline performance metrics on several benchmarks, including PASCAL VOC 2011/2012, NYUDv2, SIFT Flow, and PASCAL-Context.
   • Notably, FCNs achieve a mean Intersection over Union (IU) of 67.2% on PASCAL VOC 2012, a 30% relative improvement over previous methods. On the NYUDv2 dataset, a late fusion model of RGB and depth information achieves a mean IU of 33.3%.

Implications and Future Directions

The success of FCNs in semantic segmentation has broad implications:

1. Simplified Pipeline:
   • FCNs eliminate the need for complex pre- and post-processing steps like superpixels, window proposals, and random fields, simplifying the semantic segmentation pipeline and reducing computational overhead.
2. End-to-End Learning:
   • The ability to train networks end-to-end on whole images ensures that all layers contribute to learning meaningful features for segmentation, optimizing both spatial and semantic accuracy.
3. Model Generalization:
   • The framework's applicability to diverse datasets and tasks underscores the generalizability of FCNs. Future research can explore extensions to other dense prediction tasks such as depth estimation, optical flow, and instance segmentation.
4. Technological Integration:
   • Enhanced segmentation accuracy facilitates more robust and reliable applications in autonomous driving, medical imaging, and augmented reality, among other fields.

Conclusion

The development of Fully Convolutional Networks for semantic segmentation marks a significant advancement in computer vision methodologies. By training end-to-end and integrating multi-scale information, FCNs achieve high precision in pixelwise classification tasks. This work lays a solid foundation for further exploratory research and practical applications in dense prediction tasks. The authors' contributions demonstrate the transformative potential of reimagining traditional network architectures to better fit spatially-resolved tasks.