Lightweight Pixel Difference Networks for Efficient Visual Representation Learning (2402.00422v1)

Abstract: Recently, there have been tremendous efforts in developing lightweight Deep Neural Networks (DNNs) with satisfactory accuracy, which can enable the ubiquitous deployment of DNNs in edge devices. The core challenge of developing compact and efficient DNNs lies in how to balance the competing goals of achieving high accuracy and high efficiency. In this paper we propose two novel types of convolutions, dubbed \emph{Pixel Difference Convolution (PDC) and Binary PDC (Bi-PDC)} which enjoy the following benefits: capturing higher-order local differential information, computationally efficient, and able to be integrated with existing DNNs. With PDC and Bi-PDC, we further present two lightweight deep networks named \emph{Pixel Difference Networks (PiDiNet)} and \emph{Binary PiDiNet (Bi-PiDiNet)} respectively to learn highly efficient yet more accurate representations for visual tasks including edge detection and object recognition. Extensive experiments on popular datasets (BSDS500, ImageNet, LFW, YTF, \emph{etc.}) show that PiDiNet and Bi-PiDiNet achieve the best accuracy-efficiency trade-off. For edge detection, PiDiNet is the first network that can be trained without ImageNet, and can achieve the human-level performance on BSDS500 at 100 FPS and with $<$1M parameters. For object recognition, among existing Binary DNNs, Bi-PiDiNet achieves the best accuracy and a nearly $2\times$ reduction of computational cost on ResNet18. Code available at \href{https://github.com/hellozhuo/pidinet}{https://github.com/hellozhuo/pidinet}.

• The paper introduces Pixel Difference Convolutions (PDC) and Binary PDC (Bi-PDC) to efficiently capture local differential information in visual tasks.
• It proposes PiDiNet and Bi-PiDiNet architectures that deliver high-speed edge detection and competitive object recognition with minimal parameters.
• The networks achieve human-level edge detection performance and 62.8% Top-1 accuracy on ImageNet, highlighting significant computational efficiency gains.

Lightweight Pixel Difference Networks for Efficient Visual Representation Learning

The paper "Lightweight Pixel Difference Networks for Efficient Visual Representation Learning" presents a novel approach to developing deep neural networks (DNNs) that are both compact and effective in visual tasks, addressing the critical issue of balancing accuracy with efficiency. This work introduces two types of convolutional operations: Pixel Difference Convolution (PDC) and its binary counterpart (Bi-PDC), designed to capture high-order local differential information, augmenting the representational capacity of DNNs.

Key Contributions

1. Introduction of PDC and Bi-PDC:
   • PDC and Bi-PDC are designed to probe pixel differences rather than pixel intensities, integrating traditional local descriptors like LBP into convolutional networks. These convolutions aim to capture more granular information, offering enriched feature map diversity.
2. PiDiNet and Bi-PiDiNet Architectures:
   • Based on these convolutions, the authors propose two networks: PiDiNet for edge detection and Bi-PiDiNet for object recognition.
   • PiDiNet achieves human-level performance on the BSDS500 dataset, operating at 100 FPS with less than 1M parameters, without the need for ImageNet pretraining.
   • Bi-PiDiNet, leveraging Bi-PDC, significantly reduces the computational cost by nearly 2x on ResNet-18, offering the best accuracy among existing binary DNNs.
3. Impactful Numerical Results:
   • On edge detection, PiDiNet outperforms most state-of-the-art methods like HED and RCF without relying on extensive pretraining datasets.
   • For object recognition, Bi-PiDiNet achieves a 62.8% Top-1 accuracy on ImageNet, leading its peers in binary networks while maintaining computational efficiency.

Implications and Future Directions

The introduction of PDC and Bi-PDC opens a new pathway for integrating high-order differential information into DNNs, which has implications for both low-level and high-level computer vision tasks. These techniques allow networks to be more expressive with fewer parameters, enhancing their deployment in edge devices where computational resources are limited.

The proposed networks offer a blueprint for building more efficient architectures that do not compromise on accuracy, paving the way for deploying sophisticated vision models in real-time applications. Future research could explore further augmentation of PDC techniques, potentially extending their application to areas like video analysis or anomaly detection, where precise and efficient computation is crucial.

Conclusion

This research contributes significantly to efficient visual representation learning by integrating traditional local descriptors into contemporary DNN architectures. By showcasing improved performance across a variety of tasks and datasets, the authors provide robust evidence of the potential benefits of their proposed methodologies, providing valuable insights and a strong foundation for future exploration in both academic and industrial settings.