Quantized Densely Connected U-Nets for Efficient Landmark Localization (1808.02194v2)

Abstract: In this paper, we propose quantized densely connected U-Nets for efficient visual landmark localization. The idea is that features of the same semantic meanings are globally reused across the stacked U-Nets. This dense connectivity largely improves the information flow, yielding improved localization accuracy. However, a vanilla dense design would suffer from critical efficiency issue in both training and testing. To solve this problem, we first propose order-K dense connectivity to trim off long-distance shortcuts; then, we use a memory-efficient implementation to significantly boost the training efficiency and investigate an iterative refinement that may slice the model size in half. Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers. We validate our approach in two tasks: human pose estimation and face alignment. The results show that our approach achieves state-of-the-art localization accuracy, but using ~70% fewer parameters, ~98% less model size and saving ~75% training memory compared with other benchmark localizers. The code is available at https://github.com/zhiqiangdon/CU-Net.

• The paper introduces a novel dense connectivity framework across U-Nets, which improves visual landmark localization with order-K connections that limit redundant feature propagation.
• It employs an iterative refinement approach and memory-efficient design that together drastically reduce model parameters and memory footprint.
• Network quantization further compresses the model, enabling competitive performance on datasets like MPII and 300-W for human pose and facial landmark tasks.

Quantized Densely Connected U-Nets for Efficient Landmark Localization

This paper presents a novel approach to visual landmark localization by introducing a framework called Quantized Densely Connected U-Nets (DU-Net). The proposed architecture significantly enhances information flow and reduces redundancy in stacked U-Nets, a popular model for tasks like human pose estimation and face alignment.

Overview of Methodology

The core innovation is the introduction of dense connectivity across U-Nets, where the authors ensure that blocks of the same semantic meaning are connected across different U-Nets. This feature reuse increases localization accuracy but traditionally leads to inefficiencies—both in computational resources required and in memory usage. The paper addresses these issues with several methodological advancements:

1. Order-K Dense Connectivity: Instead of generating quadratic growth in connections, the authors propose an order-K connectivity that allows only for connections to K immediate successor U-Nets. This trims off unnecessary long-distance connections, which helps maintain parameter efficiency.
2. Memory-Efficient Implementation: By pre-allocating shared memory spaces, this implementation alleviates the memory overhead typically associated with dense networks, enabling deeper stacking of U-Nets without compromising on memory constraints.
3. Iterative Refinement: The method introduces an iterative refinement approach where the input is passed twice through a DU-Net—once for initialization and again for refining the output. This refinement potentially halves the model size while maintaining performance levels.
4. Network Quantization: To significantly reduce memory consumption and model size, the authors explore quantization of weights, inputs, and gradients. This allows the model to be deployed in memory-constrained environments, such as mobile devices, without detracting from its performance.

Results and Implications

The proposed DU-Net shows improved localization accuracy while significantly reducing model size and memory usage. In terms of numerical results, DU-Net requires only about 30% of the parameters of traditional stacked U-Nets. Moreover, with quantization, the model size can be reduced to as little as 2% of traditional methods, while maintaining competitive performance on challenging datasets like MPII for human pose estimation and the 300-W dataset for facial landmark localization.

The implications of such advancements are considerable in both practical application and theoretical exploration. Practically, the reduced model size and resource requirements make DU-Net attractive for deployment in embedded systems and edge devices. Theoretically, the paper opens avenues to explore further optimizations in dense connectivity and hybrid models for varied computer vision tasks.

Speculative Future Directions

Future work might explore extending the DU-Net framework to different domains and tasks beyond landmark localization. Another potential research avenue could involve leveraging the efficient implementation and quantization strategies to combine DU-Net with other advanced deep learning architectures. The insights into memory efficiency could further benefit large-scale models tailored for medical imaging or autonomous vehicles, where both computational efficiency and model interpretivity remain paramount.

In conclusion, the paper successfully demonstrates how architectural innovations combined with quantization can lead to efficient and effective solutions for landmark localization problems, offering a significant contribution to the field of computer vision and its real-world applications.