Efficient piecewise training of deep structured models for semantic segmentation (1504.01013v4)

Abstract: Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through the use of contextual information; specifically, we explore patch-patch' context between image regions, andpatch-background' context. For learning from the patch-patch context, we formulate Conditional Random Fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state-of-the-art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow. In particular, we achieve an intersection-over-union score of 78.0 on the challenging PASCAL VOC 2012 dataset.

• The paper introduces efficient piecewise training for CRFs within CNN frameworks to integrate patch-patch and patch-background contexts.
• It leverages multi-scale inputs and sliding pyramid pooling to capture detailed spatial and contextual information, boosting segmentation accuracy.
• Experimental results show state-of-the-art IoU scores on datasets like NYUDv2 and PASCAL VOC 2012, validating the method's effectiveness.

Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

The paper "Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation" by Guosheng Lin, Chunhua Shen, Anton van den Hengel, and Ian Reid introduces a novel method to enhance semantic segmentation using deep convolutional neural networks (CNNs) incorporated with contextual information, specifically introducing efficient piecewise training for Conditional Random Fields (CRFs).

Overview

Semantic segmentation involves predicting categorical labels for every pixel in an image, a critical yet complex task in image understanding. While recent advancements predominantly leverage CNNs for their capacity in end-to-end learning and computational efficiency, this paper innovates by incorporating two types of spatial contexts: patch-patch and patch-background contexts.

The patch-patch context addresses the semantic relationships between image patches using CNN-based pairwise potential functions within a CRF framework. Conversely, patch-background context leverages multi-scale image inputs and sliding pyramid pooling to improve the segmentation task. The novelty lies in their efficient piecewise training approach, significantly reducing the computational cost typically associated with CRF inference in backpropagation.

Methodology

1. Patch-Patch Context: This context is incorporated by formulating CRFs with CNN-based pairwise potential functions to capture meaningful semantic correlations between neighboring patches. This helps in refining predictions by modeling complex relationships beyond just local smoothness constraints as seen in conventional methods. The CNNs train these potentials to understand semantic compatibility in a more generalized manner.
2. Patch-Background Context: Traditional methods often ignore the detailed contextual relationships between patches and global image background. This paper circumvents such limitations by implementing a network architecture that employs multi-scale input image networks and sliding pyramid pooling, thus capturing various scales of background information.
3. Piecewise Training: CRF training generally involves costly inference steps which make the process computationally prohibitive. The authors propose using piecewise training of CRFs, an approach that simplifies learning by treating potentials independently, thereby enabling efficient optimization through conventional SGD methods without compromising performance.

Experimental Results

The proposed model sets new benchmarks across several datasets, achieving superior performance metrics:

• NYUDv2: An Intersection-over-Union (IoU) score of 40.6, outperforming previous methods.
• PASCAL VOC 2012: Achieves an IoU score of 78.0 under VOC+COCO training data settings, marking the best result on this challenging dataset.
• PASCAL-Context: Best reported IoU score of 43.3.
• SIFT-flow: Best IoU score of 44.9.

These improvements underscore the effectiveness of integrating contextual CRF models with CNNs, guided by efficient piecewise training.

Implications and Future Work

The innovative use of CNN-based pairwise potentials within a CRF framework and the efficiency gained through piecewise training open new pathways for more detailed and computationally feasible semantic segmentation. This methodological advance can inspire further research into integrating complex relational models into deep learning architectures across various applications such as object detection, scene understanding, and even non-vision domains like NLP.

Looking forward, future research can expand on this framework by investigating more sophisticated CRF potentials, exploring different piecewise training strategies, or combining with advanced refinement techniques like learnable deconvolution networks to further push the boundaries of semantic segmentation performance. Additionally, this approach can be generalized and adapted to other dense prediction tasks involving intricate contextual dependencies.