Indices Matter: Learning to Index for Deep Image Matting (1908.00672v1)

Abstract: We show that existing upsampling operators can be unified with the notion of the index function. This notion is inspired by an observation in the decoding process of deep image matting where indices-guided unpooling can recover boundary details much better than other upsampling operators such as bilinear interpolation. By looking at the indices as a function of the feature map, we introduce the concept of learning to index, and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the pooling and upsampling operators, without the need of supervision. At the core of this framework is a flexible network module, termed IndexNet, which dynamically predicts indices given an input. Due to its flexibility, IndexNet can be used as a plug-in applying to any off-the-shelf convolutional networks that have coupled downsampling and upsampling stages. We demonstrate the effectiveness of IndexNet on the task of natural image matting where the quality of learned indices can be visually observed from predicted alpha mattes. Results on the Composition-1k matting dataset show that our model built on MobileNetv2 exhibits at least $16.1\%$ improvement over the seminal VGG-16 based deep matting baseline, with less training data and lower model capacity. Code and models has been made available at: https://tinyurl.com/IndexNetV1

• The paper introduces IndexNet, a novel framework that learns adaptive indices to guide both downsampling and upsampling in CNNs, fundamentally redefining the approach to upsampling operators.
• Integrating IndexNet significantly improves image matting accuracy, enhancing the preservation of boundaries and textures in alpha mattes, showing at least 16.1% improvement over baselines on Composition-1k.
• Beyond image matting, IndexNet is versatile and improves performance in other vision tasks like image classification and depth estimation, suggesting its potential for better feature representation across diverse visual content.

Learning to Index for Deep Image Matting

In the paper, "Indices Matter: Learning to Index for Deep Image Matting," the authors propose a novel framework that fundamentally redefines the approach to upsampling in convolutional neural networks (CNNs) by introducing the concept of learned indices. Unlike conventional upsampling operators like bilinear interpolation, deconvolution, and unpooling, which often suffer from boundary-detail preservation issues in tasks like image matting, the proposed framework leverages indices modelled as functions of feature maps to dynamically guide both downsampling and upsampling processes.

Key Concepts and Methodology:

• IndexNet Module: At the core of the framework is IndexNet, a flexible network module that learns adaptive indices from input feature maps without requiring supervision. These indices are used to guide two novel operators: indexed pooling (IP) and indexed upsampling (IU).
• Encoder-Decoder Framework: The framework is built upon the encoder-decoder architecture akin to SegNet but generalizes it by integrating learned indices to improve upsampling accuracy. IndexNet can be seamlessly applied to existing convolutional networks with coupled downsampling and upsampling stages, such as MobileNetv2 and VGG-16.
• Types of Index Networks: The authors paper holistic index networks (HINs) and depthwise index networks (DINs), which differ in how they generate index maps—either shared across feature channels or unique to each channel dimension.

Theoretical and Experimental Insights:

1. Performance Gain in Image Matting: The paper reports significant improvements in matting accuracy. For example, in the Composition-1k dataset, models integrated with IndexNet showed at least 16.1% improvement over the VGG-16 based deep matting baseline, noting substantial gains in preserving boundaries and textures within alpha mattes.
2. Versatility and Extensibility: Beyond image matting, IndexNet demonstrated enhanced performance in other vision tasks, including image classification, depth estimation, and scene understanding. It suggests that learning indices can provide better feature representation across diverse visual content and tasks.
3. Visual Quality of Learned Indices: The authors visually analyze learned indices and highlight their capacity to capture intricate spatial features, including complex structural and textural patterns, showcasing a level of attention not attainable with fixed operator indices.

Implications and Future Scope:

The introduction of learned indices in dense prediction tasks opens multiple avenues for further exploration and innovation. The generic nature of the IndexNet framework means it can easily be adapted to different tasks and incorporated into various architectures. The principles of learning-driven index functions can potentially revolutionize upsampling beyond image matting, making it conducive for complex tasks like object detection and instance segmentation.

In summary, this paper presents a compelling case for revisiting and enhancing traditional upsampling techniques through learned indices, fostering advancements in how CNNs handle tasks sensitive to spatial detail preservation. Future work may delve into optimizing the efficiency of IndexNet modules and further understanding their applicability and scalability across a broader spectrum of AI-driven applications.