Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper.

pdf/efficient-single-image-depth-estimation-on-mobile-devices-37gxbbmw.pdf

Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report

As mobile cameras with compact optics are unable to produce a strong bokeh effect, lots of interest is now devoted to deep learning-based solutions for this task. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale EBB! bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The runtime of the resulting models was evaluated on the Kirin 9000's Mali GPU that provides excellent acceleration results for the majority of common deep learning ops. A detailed description of all models developed in this challenge is provided in this paper.

pdf/realistic-bokeh-effect-rendering-on-mobile-gpus-mobile-ai-x4ueqvve.pdf

Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report

In modern smartphone cameras, the Image Signal Processor (ISP) is the core element that converts the RAW readings from the sensor into perceptually pleasant RGB images for the end users. The ISP is typically proprietary and handcrafted and consists of several blocks such as white balance, color correction, and tone mapping. Deep learning-based ISPs aim to transform RAW images into DSLR-like RGB images using deep neural networks. However, most learned ISPs are trained using patches (small regions) due to computational limitations. Such methods lack global context, which limits their efficacy on full-resolution images and harms their ability to capture global properties such as color constancy or illumination. First, we propose a novel module that can be integrated into any neural ISP to capture the global context information from the full RAW images. Second, we propose an efficient and simple neural ISP that utilizes our proposed module. Our model achieves state-of-the-art results on different benchmarks using diverse and real smartphone images.

pdf/simple-image-signal-processing-using-global-context-guidance-2j4xmlgx7a.pdf

Simple Image Signal Processing using Global Context Guidance

With the recent proliferation of on-device AI, there is an increasing need to run computationally intensive DNNs directly on mobile devices. However, the limited computing and memory resources of these devices necessitate effective pruning techniques. Block-wise pruning is promising due to its low accuracy drop tradeoff for speedup gains, but it requires block positions to be aligned with block size, hindering optimal position selection to minimize model accuracy drop. Unaligned block pruning (UBP) addresses this by allowing blocks to be selected at arbitrary positions, yet its practical use is limited by a time-consuming optimal block selection algorithm and lack of efficient inference kernels. In this paper, we propose a pseudo-optimal yet fast block selection algorithm called Block Expansion and Division (BED), which can be integrated into an iterative model training process. Additionally, we introduce an efficient inference kernel implementation for mobile devices, enabling a UBP-based model to achieve similar latency to a DNN model compressed by aligned block pruning. We demonstrate the superiority of our techniques on a real mobile phone with MobileNet and ResNet models. 

pdf/realizing-unaligned-block-wise-pruning-for-dnn-acceleration-6kf2j6glxy7c.pdf

Realizing Unaligned Block-wise Pruning for DNN Acceleration on Mobile
  Devices

At first, this article makes use of factors influencing the development of industrial design to measure the development level of Suzhou's industrial design, and summarize the status of it based on statistical data analysis; then put forward the existing problems of it according to the status; finally come up with development strategies and suggestions

The present situation and developing strategy of Suzhou's industrial design

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

pdf/learned-smartphone-isp-on-mobile-gpus-with-deep-learning-bqvsbyxh.pdf

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

Industrial Design: Impetus from “Made-in-China” to “Created-in-China”

The importance of industrial design in improving the innovative capacity of our country is well-known to all. In fact, design is part of the innovative industry and industrial design plays a very significant role in “created in China” from the point of economy and culture.

Industrial Design: Impetus from Made-in-China to Created-in-China

On the Bilingual Teaching Practice of "Industrial Design History"

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Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

Industrial Design: Impetus from “Made-in-China” to “Created-in-China”

Industrial Design: Impetus from Made-in-China to Created-in-China

On the Bilingual Teaching Practice of "Industrial Design History"