Frost damage is one of the main factors leading to wheat yield reduction. Therefore, the detection of wheat frost accurately and efficiently is beneficial for growers to take corresponding measures in time to reduce economic loss. To detect the wheat frost, in this paper we create a hyperspectral wheat frost data set by collecting the data characterized by temperature, wheat yield, and hyperspectral information provided by the handheld hyperspectral spectrometer. However, due to the imbalance of data, that is, the number of healthy samples is much higher than the number of frost damage samples, a deep learning algorithm tends to predict biasedly towards the healthy samples resulting in model overfitting of the healthy samples. Therefore, we propose a method based on deep cost-sensitive learning, which uses a one-dimensional convolutional neural network as the basic framework and incorporates cost-sensitive learning with fixed factors and adjustment factors into the loss function to train the network. Meanwhile, the accuracy and score are used as evaluation metrics. Experimental results show that the detection accuracy and the score reached 0.943 and 0.623 respectively, this demonstration shows that this method not only ensures the overall accuracy but also effectively improves the detection rate of frost samples.

Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition function in the existing GPSSMs are assumed to be independent, meaning that the GPSSMs cannot exploit the inductive biases between different outputs and lose certain model capacities. To address this issue, this paper proposes an output-dependent and more realistic GPSSM by utilizing the well-known, simple yet practical linear model of coregionalization (LMC) framework to represent the output dependency. To jointly learn the output-dependent GPSSM and infer the latent states, we propose a variational sparse GP-based learning method that only gently increases the computational complexity. Experiments on both synthetic and real datasets demonstrate the superiority of the output-dependent GPSSM in terms of learning and inference performance.

Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alternative to approaching general-purpose AI, existing generalist models are still at an early stage, where modality and task coverage is limited. To empower multi-modal task-scaling and speed up this line of research, we release a generalist model learning system, OFASys, built on top of a declarative task interface named multi-modal instruction. At the core of OFASys is the idea of decoupling multi-modal task representations from the underlying model implementations. In OFASys, a task involving multiple modalities can be defined declaratively even with just a single line of code. The system automatically generates task plans from such instructions for training and inference. It also facilitates multi-task training for diverse multi-modal workloads. As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data. The single OFA+ model achieves 95% performance in average with only 16% parameters of 15 task-finetuned models, showcasing the performance reliability of multi-modal task-scaling provided by OFASys. Available at https://github.com/OFA-Sys/OFASys

The long-standing theory that a colour-naming system evolves under the dual pressure of efficient communication and perceptual mechanism is supported by more and more linguistic studies including the analysis of four decades' diachronic data from the Nafaanra language. This inspires us to explore whether artificial intelligence could evolve and discover a similar colour-naming system via optimising the communication efficiency represented by high-level recognition performance. Here, we propose a novel colour quantisation transformer, CQFormer, that quantises colour space while maintaining the accuracy of machine recognition on the quantised images. Given an RGB image, Annotation Branch maps it into an index map before generating the quantised image with a colour palette, meanwhile the Palette Branch utilises a key-point detection way to find proper colours in palette among whole colour space. By interacting with colour annotation, CQFormer is able to balance both the machine vision accuracy and colour perceptual structure such as distinct and stable colour distribution for discovered colour system. Very interestingly, we even observe the consistent evolution pattern between our artificial colour system and basic colour terms across human languages. Besides, our colour quantisation method also offers an efficient quantisation method that effectively compresses the image storage while maintaining a high performance in high-level recognition tasks such as classification and detection. Extensive experiments demonstrate the superior performance of our method with extremely low bit-rate colours. We will release the source code soon.

The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competition, Autonomous Driving Security Competition, and Face Recognition Security Competition. This report will introduce the competition rules of these three tracks and the solutions of top-ranking teams in each track.

The problem of covariate-shift generalization has attracted intensive research attention. Previous stable learning algorithms employ sample reweighting schemes to decorrelate the covariates when there is no explicit domain information about training data. However, with finite samples, it is difficult to achieve the desirable weights that ensure perfect independence to get rid of the unstable variables. Besides, decorrelating within stable variables may bring about high variance of learned models because of the over-reduced effective sample size. A tremendous sample size is required for these algorithms to work. In this paper, with theoretical justification, we propose SVI (Sparse Variable Independence) for the covariate-shift generalization problem. We introduce sparsity constraint to compensate for the imperfectness of sample reweighting under the finite-sample setting in previous methods. Furthermore, we organically combine independence-based sample reweighting and sparsity-based variable selection in an iterative way to avoid decorrelating within stable variables, increasing the effective sample size to alleviate variance inflation. Experiments on both synthetic and real-world datasets demonstrate the improvement of covariate-shift generalization performance brought by SVI.

图像恢复算法（例如超级分辨率（SR））是低质量图像中对象检测的必不可少的预处理模块。这些算法中的大多数假定降解是固定的，并且已知先验。但是，实际上，实际降解或最佳的上采样率是未知或与假设不同的，导致预处理模块和随之而来的高级任务（例如对象检测）的性能恶化。在这里，我们提出了一个新颖的自我监督框架，以检测低分辨率图像降解的对象。我们利用下采样降解作为一种自我监督信号的一种转换，以探索针对各种分辨率和其他退化条件的模棱两可的表示。自我设计（AERIS）框架中的自动编码分辨率可以进一步利用高级SR体系结构，并使用任意分辨率恢复解码器，以从退化的输入图像中重建原始对应关系。表示学习和对象检测均以端到端的培训方式共同优化。通用AERIS框架可以在具有不同骨架的各种主流对象检测架构上实现。广泛的实验表明，与现有方法相比，我们的方法在面对变化降解情况时取得了卓越的性能。代码将在https://github.com/cuiziteng/eccv_aeris上发布。

卷积神经网络（CNN）和变压器在多媒体应用中取得了巨大成功。但是，几乎没有努力有效，有效地协调这两个架构以满足图像的范围。本文旨在统一这两种架构，以利用其学习优点来降低图像。特别是，CNN的局部连通性和翻译等效性以及变压器中自我注意力（SA）的全球聚合能力被完全利用用于特定的局部环境和全球结构表示。基于雨水分布揭示降解位置和程度的观察，我们在帮助背景恢复之前引入退化，并因此呈现关联细化方案。提出了一种新型的多输入注意模块（MAM），以将降雨的去除和背景恢复关联。此外，我们为模型配备了有效的深度可分离卷积，以学习特定的特征表示并权衡计算复杂性。广泛的实验表明，我们提出的方法（称为ELF）的表现平均比最先进的方法（MPRNET）优于0.25 dB，但仅占其计算成本和参数的11.7 \％和42.1 \％。源代码可从https://github.com/kuijiang94/magic-elf获得。

作为一种主动网络安全保护方案，入侵检测系统（IDS）承担以恶意网络流量形式检测网络攻击的重要责任。入侵检测技术是ID的重要组成部分。目前，许多学者已经对入侵检测技术进行了广泛的研究。但是，为大规模网络流量数据开发有效的入侵检测方法仍然很困难。由于生成的对抗网络（GAN）具有强大的建模功能，可用于复杂的高维数据，因此它们为解决此问题提供了新的想法。在本文中，我们提出了一种基于Ebgan的入侵检测方法IDS-Ebgan，该方法将网络记录归类为正常流量或恶意流量。 IDS-Ebgan中的发电机负责将培训中的原始恶意网络流量转换为对抗性恶意示例。这是因为我们想使用对抗性学习来提高歧视者检测恶意流量的能力。同时，鉴别器采用自动编码器模型。在测试过程中，IDS-Ebgan使用歧视器的重建错误来对流量记录进行分类。

图形神经网络（GNN）已被密切应用于各种基于图的应用程序。尽管他们成功了，但手动设计行为良好的GNN需要巨大的人类专业知识。因此，发现潜在的最佳数据特异性GNN体系结构效率低下。本文提出了DFG-NAS，这是一种新的神经体系结构搜索（NAS）方法，可自动搜索非常深入且灵活的GNN体系结构。与大多数专注于微构造的方法不同，DFG-NAS突出了另一个设计级别：搜索有关原子传播的宏观构造（\ TextBf {\ Textbf {\ Texttt {p}}}）和转换（\ texttt {\ textttt {\ texttt {\ texttt {\ texttt { T}}）的操作被整合并组织到GNN中。为此，DFG-NAS为\ textbf {\ texttt {p-t}}}的排列和组合提出了一个新颖的搜索空间，该搜索空间是基于消息传播的散布，定义了四个自定义设计的宏观架构突变，并采用了进化性algorithm to to the Evolutionary algorithm进行有效的搜索。关于四个节点分类任务的实证研究表明，DFG-NAS优于最先进的手动设计和GNN的NAS方法。