mlsanityData to 7 Oct 2026

Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report

arXiv 2105.08819 ↗First posted 17 May 2021eess.IV cs.CV cs.LGcited by 10

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Abstract (arXiv)

Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of the designed models were available publicly up until now. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms. For this, the participants were provided with a large-scale CamSDD dataset consisting of more than 11K images belonging to the 30 most important scene categories. The runtime of all models was evaluated on the popular Apple Bionic A11 platform that can be found in many iOS devices. The proposed solutions are fully compatible with all major mobile AI accelerators and can demonstrate more than 100-200 FPS on the majority of recent smartphone platforms while achieving a top-3 accuracy of more than 98%. A detailed description of all models developed in the challenge is provided in this paper.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 28 of its 48 references that are in the map. First posted on arXiv 17 May 2021.

Builds on

28 of 48 references.

References · 28

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16 more
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Cited by year

Citing papers per year: 2022: 5; 2023: 0; 2024: 4; 2025: 1.

10 papers.

  1. 2022Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Reportcited by 16
  2. 2022Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Reportcited by 13
  3. 2022Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Reportcited by 4
  4. 2022Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Reportcited by 3
  5. 2024DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognitioncited by 3
  6. 2025Cycle Training with Semi-Supervised Domain Adaptation: Bridging Accuracy and Efficiency for Real-Time Mobile Scene Detectioncited by 3
  7. 2022Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Reportcited by 2
  8. 2024Properties that allow or prohibit transferability of adversarial attacks among quantized networkscited by 0
  9. 2024Toward Efficient Convolutional Neural Networks With Structured Ternary Patternscited by 0
  10. 2024Realizing Unaligned Block-wise Pruning for DNN Acceleration on Mobile Devicescited by 0