人重新识别是识别非重叠摄像机的个体的问题。尽管在重新识别问题中取得了显着进展，但由于同一人的外观变化以及其他外观相似的人，这仍然是一个具有挑战性的问题。一些先前的作品通过将正样本的特征与负面的特征分开来解决这些问题。但是，现有模型的性能在很大程度上取决于用于培训的样品的特征和统计数据。因此，我们提出了一个名为“采样独立鲁棒特征表示网络”（sirnet）的新型框架，该框架学习了从随机选择的样品中嵌入的分离特征。对精心设计的采样独立的最大差异损失引入了与集群同一人的模型样本。结果，所提出的框架可以使用学识渊博的功能产生额外的硬质量/积极因素，从而可以更好地辨别其他身份。大规模基准数据集的广泛实验结果验证了所提出的模型比以前的最新模型更有效。

评估图像美学是一项具有挑战性的计算机视觉任务。原因之一是美学偏好是高度主观的，并且在某些图像中可能会有很大的不同。因此，重要的是要正确建模和量化此类\ textit {主观性}，但是解决此问题并没有太多努力。在本文中，我们提出了一个新型的统一概率框架，可以根据主观逻辑对主观美学偏好进行建模和量化。在此框架中，评级分配被建模为Beta分布，从中，绝对令人愉悦，绝对令人不快和不确定的概率可以得到。我们使用不确定的概率来定义主观性的直观指标。此外，我们提出了一种学习深度神经网络以预测图像美学的方法，该方法被证明可以有效地通过实验改善主观性预测的性能。我们还提出了一个应用程序方案，该方案对基于美学的图像建议有益。

在本文中，我们提出了一种用于多视图360 \级\：图像的密集深度估计流水线。所提出的管道利用了一个球形相机模型，可以在360 \ deption \：图像中补偿径向失真。本文的主要贡献是通过引入翻译缩放方案来扩展球形相机模型以多视图。此外，我们通过设定虚拟深度并最小化光子重新注入误差来提出有效的密集深度估计方法。我们使用自然场景的图像以及合成的数据集来验证所提出的管道的性能，以进行量化评估。实验结果验证了所提出的管道与当前最先进的密集深度估计方法相比提高了估计精度。

这项工作解决了自主EV充电机器人的电动车辆（EV）充电入口检测的任务。最近，自动化EV充电系统得到了普遍的关注，以提高用户的经验，并有效地利用充电基础设施和停车场。但是，大多数相关工程都专注于系统设计，机器人控制，规划和操作。朝向强大的EV充电入口检测，我们提出了一个新的数据集（EVCI数据集）和一种新颖的数据增强方法，该方法基于图像到图像转换，其中典型的图像到图像转换方法在给定的不同域中合成新图像一个图像。据我们所知，EVCI数据集是第一个EV充电入口数据集。对于数据增强方法，我们专注于能够以直观的方式控制合成的图像“捕获的环境（例如，时间，照明）。为实现这一目标，我们首先提出了人类可以直观地解释的环境指导载体。然后，我们提出了一种新颖的图像到图像翻译网络，其将给定图像转换为矢量的环境。因此，它旨在将具有与给定图像具有相同内容的新图像，同时通过环境指南向量观察在所提供的环境中捕获。最后，我们使用增强数据集训练检测方法。通过对EVCI数据集的实验，我们证明所提出的方法优于最先进的方法。我们还表明，所提出的方法能够使用图像和环境指南向量来控制合成的图像。

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.

Cellular automata (CA) captivate researchers due to teh emergent, complex individualized behavior that simple global rules of interaction enact. Recent advances in the field have combined CA with convolutional neural networks to achieve self-regenerating images. This new branch of CA is called neural cellular automata [1]. The goal of this project is to use the idea of idea of neural cellular automata to grow prediction machines. We place many different convolutional neural networks in a grid. Each conv net cell outputs a prediction of what the next state will be, and minimizes predictive error. Cells received their neighbors' colors and fitnesses as input. Each cell's fitness score described how accurate its predictions were. Cells could also move to explore their environment and some stochasticity was applied to movement.

There is a dramatic shortage of skilled labor for modern vineyards. The Vinum project is developing a mobile robotic solution to autonomously navigate through vineyards for winter grapevine pruning. This necessitates an autonomous navigation stack for the robot pruning a vineyard. The Vinum project is using the quadruped robot HyQReal. This paper introduces an architecture for a quadruped robot to autonomously move through a vineyard by identifying and approaching grapevines for pruning. The higher level control is a state machine switching between searching for destination positions, autonomously navigating towards those locations, and stopping for the robot to complete a task. The destination points are determined by identifying grapevine trunks using instance segmentation from a Mask Region-Based Convolutional Neural Network (Mask-RCNN). These detections are sent through a filter to avoid redundancy and remove noisy detections. The combination of these features is the basis for the proposed architecture.

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.

In this paper, we learn a diffusion model to generate 3D data on a scene-scale. Specifically, our model crafts a 3D scene consisting of multiple objects, while recent diffusion research has focused on a single object. To realize our goal, we represent a scene with discrete class labels, i.e., categorical distribution, to assign multiple objects into semantic categories. Thus, we extend discrete diffusion models to learn scene-scale categorical distributions. In addition, we validate that a latent diffusion model can reduce computation costs for training and deploying. To the best of our knowledge, our work is the first to apply discrete and latent diffusion for 3D categorical data on a scene-scale. We further propose to perform semantic scene completion (SSC) by learning a conditional distribution using our diffusion model, where the condition is a partial observation in a sparse point cloud. In experiments, we empirically show that our diffusion models not only generate reasonable scenes, but also perform the scene completion task better than a discriminative model. Our code and models are available at https://github.com/zoomin-lee/scene-scale-diffusion

We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. We give an $(\epsilon_{\text{dp}}, \delta)$-differentially private algorithm which, given $n$ samples of Lipschitz loss functions, obtains near-optimal optimization error and makes $\min(n, n^2\epsilon_{\text{dp}}^2 d^{-1}) + \min(n^{4/3}\epsilon_{\text{dp}}^{1/3}, (nd)^{2/3}\epsilon_{\text{dp}}^{-1})$ queries to the gradients of these functions. In the regime $d \le n \epsilon_{\text{dp}}^{2}$, where privacy comes at no cost in terms of the optimal loss up to constants, our algorithm uses $n + (nd)^{2/3}\epsilon_{\text{dp}}^{-1}$ queries and improves recent advancements of [KLL21, AFKT21]. In the moderately low-dimensional setting $d \le \sqrt n \epsilon_{\text{dp}}^{3/2}$, our query complexity is near-linear.