Understanding the informative structures of scenes is essential for low-level vision tasks. Unfortunately, it is difficult to obtain a concrete visual definition of the informative structures because influences of visual features are task-specific. In this paper, we propose a single general neural network architecture for extracting task-specific structure guidance for scenes. To do this, we first analyze traditional spectral clustering methods, which computes a set of eigenvectors to model a segmented graph forming small compact structures on image domains. We then unfold the traditional graph-partitioning problem into a learnable network, named \textit{Scene Structure Guidance Network (SSGNet)}, to represent the task-specific informative structures. The SSGNet yields a set of coefficients of eigenvectors that produces explicit feature representations of image structures. In addition, our SSGNet is light-weight ($\sim$ 55K parameters), and can be used as a plug-and-play module for off-the-shelf architectures. We optimize the SSGNet without any supervision by proposing two novel training losses that enforce task-specific scene structure generation during training. Our main contribution is to show that such a simple network can achieve state-of-the-art results for several low-level vision applications including joint upsampling and image denoising. We also demonstrate that our SSGNet generalizes well on unseen datasets, compared to existing methods which use structural embedding frameworks. Our source codes are available at https://github.com/jsshin98/SSGNet.

Traversability estimation for mobile robots in off-road environments requires more than conventional semantic segmentation used in constrained environments like on-road conditions. Recently, approaches to learning a traversability estimation from past driving experiences in a self-supervised manner are arising as they can significantly reduce human labeling costs and labeling errors. However, the self-supervised data only provide supervision for the actually traversed regions, inducing epistemic uncertainty according to the scarcity of negative information. Negative data are rarely harvested as the system can be severely damaged while logging the data. To mitigate the uncertainty, we introduce a deep metric learning-based method to incorporate unlabeled data with a few positive and negative prototypes in order to leverage the uncertainty, which jointly learns using semantic segmentation and traversability regression. To firmly evaluate the proposed framework, we introduce a new evaluation metric that comprehensively evaluates the segmentation and regression. Additionally, we construct a driving dataset `Dtrail' in off-road environments with a mobile robot platform, which is composed of a wide variety of negative data. We examine our method on Dtrail as well as the publicly available SemanticKITTI dataset.

建模人行走的动力是对计算机视觉的长期兴趣的问题。许多涉及行人轨迹预测的以前的作品将一组特定的单个动作定义为隐式模型组动作。在本文中，我们介绍了一个名为GP-GRAPH的新颖架构，该架构具有集体的小组表示，用于在拥挤的环境中有效的人行道轨迹预测，并且与所有类型的现有方法兼容。 GP-GRAPH的一个关键思想是将个人和小组关系的关系作为图表表示。为此，GP-Graph首先学会将每个行人分配给最可能的行为组。然后，使用此分配信息，GP编写是图形的组内和组间相互作用，分别考虑了组和群体关系中的人类关系。要具体，对于小组内相互作用，我们掩盖了相关组中的行人图边缘。我们还建议小组合并和不致密操作，以代表一个具有多个行人作为一个图节点的小组。最后，GP-GRAPH从两个组相互作用的综合特征中渗透了一个可获得社会上可接受的未来轨迹的概率图。此外，我们介绍了一个小组潜在的矢量抽样，以确保对一系列可能的未来轨迹的集体推断。进行了广泛的实验来验证我们的体系结构的有效性，该实验证明了通过公开可用的基准测试的绩效一致。代码可在https://github.com/inhwanbae/gpgraph上公开获取。

为了获得更好的摄影，包括智能手机在内的最新商业摄像机要么采用大孔镜来收集更多的光线，要么使用突发模式在短时间内拍摄多个图像。这些有趣的功能使我们检查了焦点/散焦的深度。在这项工作中，我们提出了来自单个焦点堆栈的基于卷积神经网络的深度估计。我们的方法不同于相关的最新方法，具有三个独特的功能。首先，我们的方法允许以端到端方式推断深度图，即使图像对齐方式也是如此。其次，我们提出了一个尖锐的区域检测模块，以减少焦点变化和无纹理的区域中的模糊歧义。第三，我们设计了一个有效的下采样模块，以减轻特征提取中焦点信息的流动。此外，为了概括拟议的网络，我们开发了一个模拟器来实际重现商用摄像机的特征，例如视野的变化，焦点长度和主要点。通过有效合并这三个独特功能，我们的网络在大多数指标上达到了DDFF 12场景基准的最高等级。我们还证明了所提出的方法对与最新方法相比，从各种现成的摄像机拍摄的各种定量评估和现实世界图像的有效性。我们的源代码可在https://github.com/wcy199705/dffinthewild上公开获得。

许多移动制造商最近在其旗舰模型中采用了双像素（DP）传感器，以便更快的自动对焦和美学图像捕获。尽管他们的优势，由于DT在DP图像中的视差缺失的数据集和算法设计，但对3D面部理解的使用研究受到限制。这是因为子孔图像的基线非常窄，并且散焦模糊区域存在视差。在本文中，我们介绍了一种以DP为导向的深度/普通网络，该网络重建3D面部几何。为此目的，我们使用我们的多摄像头结构光系统捕获的101人拥有超过135k张图片的DP面部数据。它包含相应的地面真值3D模型，包括度量刻度的深度图和正常。我们的数据集允许建议的匹配网络广泛化，以便以3D面部深度/正常估计。所提出的网络由两种新颖的模块组成：自适应采样模块和自适应正常模块，专门用于处理DP图像中的散焦模糊。最后，该方法实现了最近基于DP的深度/正常估计方法的最先进的性能。我们还展示了估计深度/正常的适用性面对欺骗和致密。

For change detection in remote sensing, constructing a training dataset for deep learning models is difficult due to the requirements of bi-temporal supervision. To overcome this issue, single-temporal supervision which treats change labels as the difference of two semantic masks has been proposed. This novel method trains a change detector using two spatially unrelated images with corresponding semantic labels such as building. However, training on unpaired datasets could confuse the change detector in the case of pixels that are labeled unchanged but are visually significantly different. In order to maintain the visual similarity in unchanged area, in this paper, we emphasize that the change originates from the source image and show that manipulating the source image as an after-image is crucial to the performance of change detection. Extensive experiments demonstrate the importance of maintaining visual information between pre- and post-event images, and our method outperforms existing methods based on single-temporal supervision. code is available at https://github.com/seominseok0429/Self-Pair-for-Change-Detection.

Zero-shot quantization is a promising approach for developing lightweight deep neural networks when data is inaccessible owing to various reasons, including cost and issues related to privacy. By utilizing the learned parameters (statistics) of FP32-pre-trained models, zero-shot quantization schemes focus on generating synthetic data by minimizing the distance between the learned parameters ($\mu$ and $\sigma$) and distributions of intermediate activations. Subsequently, they distill knowledge from the pre-trained model (\textit{teacher}) to the quantized model (\textit{student}) such that the quantized model can be optimized with the synthetic dataset. In general, zero-shot quantization comprises two major elements: synthesizing datasets and quantizing models. However, thus far, zero-shot quantization has primarily been discussed in the context of quantization-aware training methods, which require task-specific losses and long-term optimization as much as retraining. We thus introduce a post-training quantization scheme for zero-shot quantization that produces high-quality quantized networks within a few hours on even half an hour. Furthermore, we propose a framework called \genie~that generates data suited for post-training quantization. With the data synthesized by \genie, we can produce high-quality quantized models without real datasets, which is comparable to few-shot quantization. We also propose a post-training quantization algorithm to enhance the performance of quantized models. By combining them, we can bridge the gap between zero-shot and few-shot quantization while significantly improving the quantization performance compared to that of existing approaches. In other words, we can obtain a unique state-of-the-art zero-shot quantization approach.

We study the compute-optimal trade-off between model and training data set sizes for large neural networks. Our result suggests a linear relation similar to that supported by the empirical analysis of Chinchilla. While that work studies transformer-based large language models trained on the MassiveText corpus (gopher), as a starting point for development of a mathematical theory, we focus on a simpler learning model and data generating process, each based on a neural network with a sigmoidal output unit and single hidden layer of ReLU activation units. We establish an upper bound on the minimal information-theoretically achievable expected error as a function of model and data set sizes. We then derive allocations of computation that minimize this bound. We present empirical results which suggest that this approximation correctly identifies an asymptotic linear compute-optimal scaling. This approximation can also generate new insights. Among other things, it suggests that, as the input space dimension or latent space complexity grows, as might be the case for example if a longer history of tokens is taken as input to a language model, a larger fraction of the compute budget should be allocated to growing the learning model rather than training data set.

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

Mixup is a popular data augmentation technique based on creating new samples by linear interpolation between two given data samples, to improve both the generalization and robustness of the trained model. Knowledge distillation (KD), on the other hand, is widely used for model compression and transfer learning, which involves using a larger network's implicit knowledge to guide the learning of a smaller network. At first glance, these two techniques seem very different, however, we found that ``smoothness" is the connecting link between the two and is also a crucial attribute in understanding KD's interplay with mixup. Although many mixup variants and distillation methods have been proposed, much remains to be understood regarding the role of a mixup in knowledge distillation. In this paper, we present a detailed empirical study on various important dimensions of compatibility between mixup and knowledge distillation. We also scrutinize the behavior of the networks trained with a mixup in the light of knowledge distillation through extensive analysis, visualizations, and comprehensive experiments on image classification. Finally, based on our findings, we suggest improved strategies to guide the student network to enhance its effectiveness. Additionally, the findings of this study provide insightful suggestions to researchers and practitioners that commonly use techniques from KD. Our code is available at https://github.com/hchoi71/MIX-KD.