Global Second-order Pooling Convolutional Networks (1811.12006v2)

Abstract: Deep Convolutional Networks (ConvNets) are fundamental to, besides large-scale visual recognition, a lot of vision tasks. As the primary goal of the ConvNets is to characterize complex boundaries of thousands of classes in a high-dimensional space, it is critical to learn higher-order representations for enhancing non-linear modeling capability. Recently, Global Second-order Pooling (GSoP), plugged at the end of networks, has attracted increasing attentions, achieving much better performance than classical, first-order networks in a variety of vision tasks. However, how to effectively introduce higher-order representation in earlier layers for improving non-linear capability of ConvNets is still an open problem. In this paper, we propose a novel network model introducing GSoP across from lower to higher layers for exploiting holistic image information throughout a network. Given an input 3D tensor outputted by some previous convolutional layer, we perform GSoP to obtain a covariance matrix which, after nonlinear transformation, is used for tensor scaling along channel dimension. Similarly, we can perform GSoP along spatial dimension for tensor scaling as well. In this way, we can make full use of the second-order statistics of the holistic image throughout a network. The proposed networks are thoroughly evaluated on large-scale ImageNet-1K, and experiments have shown that they outperformed non-trivially the counterparts while achieving state-of-the-art results.

• The paper introduces Global Second-order Pooling (GSoP) throughout ConvNets to leverage covariance statistics for richer feature representation.
• The proposed GSoP blocks integrate with architectures like ResNet and DenseNet while delivering significant accuracy gains on large-scale datasets.
• Empirical results on ImageNet-1K confirm that modular second-order pooling improves non-linear modeling with minimal extra computation.

An Overview of Global Second-order Pooling Convolutional Networks

The paper "Global Second-order Pooling Convolutional Networks" by Zilin Gao et al. investigates an important progression in the domain of deep neural networks, specifically within the architecture of Convolutional Neural Networks (ConvNets). The research puts forward a novel model architecture that incorporates Global Second-order Pooling (GSoP) throughout the entire network, as opposed to merely employing it at the final layers. This integration allows the model to leverage holistic image representations grounded in higher-order statistical information, thereby offering a significant enhancement to the non-linear modeling capacity of ConvNets.

Context and Motivation

ConvNets have long been integral to tackling computer vision challenges, boasting applications in tasks such as object recognition, detection, semantic segmentation, and video classification. Traditional implementations have focused on learning first-order representations through global average pooling, which captures mean values as statistics of the data. However, this approach may not adequately represent the complexity inherent in visual data, which motivates the pursuit of higher-order pooling strategies like GSoP for better characterizing these complexities by using covariance matrices for image representation.

Methodology

This paper introduces GSoP from the lower to higher network layers, differentiating it from earlier approaches that limit high-order pooling to the network end. The strategy involves calculating a covariance matrix from the output of previous convolutional layers, performing nonlinear transformations, and scaling the tensor along either the channel or spatial dimensions. This method ensures that second-order statistics encapsulate entire network layers, potentially offering a significant improvement in discriminative power across various vision tasks.

The GSoP block designed by the authors is modular and can be easily integrated with existing network architectures like ResNet, DenseNet, and Inception. These blocks are evaluated primarily on the ImageNet-1K dataset, reflecting the scalability and effectiveness of the approach on large-scale visual recognition tasks.

Key Findings and Results

Empirical evaluations on ImageNet-1K demonstrate that the proposed GSoP networks non-trivially outperform current models, including traditional first-order methods and recent second-order pooling approaches like iSQRT-COV. Specifically, on the ImageNet-1K dataset, the GSoP-Net1 (which combines GSoP at intermediate layers with average pooling at the end) and GSoP-Net2 (employing matrix normalization at the network end) achieved commendable accuracy improvements over baseline methods.

Moreover, the modular nature of the GSoP block suggests minimal increases in computation and memory requirements versus state-of-the-art networks without compromising performance, underscoring the efficacy of the GSoP strategy in improving the non-linear representational capacities of ConvNets.

Implications and Future Work

By demonstrating the effectiveness of integrating second-order statistics throughout deep networks, this paper lays the groundwork for further exploration into statistical methods for improving neural network performance. Future research could explore hybrid pooling methods, different covariance matrix sizes, and combination strategies with other advanced deep learning architectures. Additionally, the compatibility of GSoP blocks with alternative frameworks like Inception and DenseNet could offer insights into their generalized utility in enhancing deep learning models.

Overall, the introduction of intermediate and widespread use of second-order pooling in networks highlights a significant step forward in capturing complex data representations, promising further advancements in the field of computer vision and beyond.