Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

知识蒸馏（KD）最近成为压缩神经网络的一种流行方法。在最近的研究中，已经提出了同时找到学生模型的参数和体系结构的广义蒸馏方法。尽管如此，这种搜索方法仍需要大量的计算来搜索体系结构，并且缺点是仅考虑其搜索空间中的卷积块。本文介绍了一种新的算法，认为是信任区域意识架构搜索以有效提炼知识（贸易），该算法迅速找到了使用信任区域贝叶斯优化方法从几种最先进的架构中找到有效的学生体系结构。实验结果表明，我们提出的贸易算法始终优于KD培训下的常规NAS方法和预定义的架构。

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

光学相干断层扫描（OCT）是一种非侵入性技术，可在微米分辨率中捕获视网膜的横截面区域。它已被广泛用作辅助成像参考，以检测与眼睛有关的病理学并预测疾病特征的纵向进展。视网膜层分割是至关重要的特征提取技术之一，其中视网膜层厚度的变化和由于液体的存在而引起的视网膜层变形高度相关，与多种流行性眼部疾病（如糖尿病性视网膜病）和年龄相关的黄斑疾病高度相关。变性（AMD）。但是，这些图像是从具有不同强度分布或换句话说的不同设备中获取的，属于不同的成像域。本文提出了一种分割引导的域适应方法，以将来自多个设备的图像调整为单个图像域，其中可用的最先进的预训练模型可用。它避免了即将推出的新数据集的手动标签的时间消耗以及现有网络的重新培训。网络的语义一致性和全球特征一致性将最大程度地减少许多研究人员报告的幻觉效果，这些效应对周期矛盾的生成对抗网络（Cyclegan）体系结构。

在基于哈希的图像检索系统中，原始的变换输入通常会产生不同的代码，降低检索精度。要缓解此问题，可以在培训期间应用数据增强。然而，即使一个内容的增强样本在真实空间中相似，量化也可以在汉明空间远离它们。这导致可以阻碍培训和降低性能的表示差异。在这项工作中，我们提出了一种新型的自蒸馏散列方案，以最小化差异，同时利用增强数据的潜力。通过将弱变换样本的哈希知识转移到强大的样本，我们使哈希代码对各种变换不敏感。我们还引入了基于哈希代理的相似度学习和基于二进制交叉熵的量化损耗，以提供优质的质量哈希代码。最终，我们构建一个深度散列框架，产生鉴别性哈希代码。基准测试的广泛实验验证了我们的自蒸馏改善了现有的深度散列方法，我们的框架达到了最先进的检索结果。代码将很快发布。

监督基于深度学习的哈希和矢量量化是实现快速和大规模的图像检索系统。通过完全利用标签注释，与传统方法相比，它们正在实现出色的检索性能。但是，令人生心的是为大量训练数据准确地分配标签，并且还有注释过程易于出错。为了解决这些问题，我们提出了第一款深度无监督的图像检索方法被称为自我监督的产品量化（SPQ）网络，该方法是无标签和以自我监督的方式培训的。我们通过比较单独转换的图像（视图）来设计一个交叉量化的对比学习策略，该横向学习策略共同学习码字和深视觉描述符。我们的方法分析了图像内容以提取描述性功能，允许我们理解图像表示以准确检索。通过对基准进行广泛的实验，我们证明该方法即使没有监督预测，也会产生最先进的结果。

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years. Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.