Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.

Fine-grained classification and counting of bone marrow erythroid cells are vital for evaluating the health status and formulating therapeutic schedules for leukemia or hematopathy. Due to the subtle visual differences between different types of erythroid cells, it is challenging to apply existing image-based deep learning models for fine-grained erythroid cell classification. Moreover, there is no large open-source datasets on erythroid cells to support the model training. In this paper, we introduce BMEC (Bone Morrow Erythroid Cells), the first large fine-grained image dataset of erythroid cells, to facilitate more deep learning research on erythroid cells. BMEC contains 5,666 images of individual erythroid cells, each of which is extracted from the bone marrow erythroid cell smears and professionally annotated to one of the four types of erythroid cells. To distinguish the erythroid cells, one key indicator is the cell shape which is closely related to the cell growth and maturation. Therefore, we design a novel shape-aware image classification network for fine-grained erythroid cell classification. The shape feature is extracted from the shape mask image and aggregated to the raw image feature with a shape attention module. With the shape-attended image feature, our network achieved superior classification performance (81.12\% top-1 accuracy) on the BMEC dataset comparing to the baseline methods. Ablation studies also demonstrate the effectiveness of incorporating the shape information for the fine-grained cell classification. To further verify the generalizability of our method, we tested our network on two additional public white blood cells (WBC) datasets and the results show our shape-aware method can generally outperform recent state-of-the-art works on classifying the WBC. The code and BMEC dataset can be found on https://github.com/wangye8899/BMEC.

Blind watermarking provides powerful evidence for copyright protection, image authentication, and tampering identification. However, it remains a challenge to design a watermarking model with high imperceptibility and robustness against strong noise attacks. To resolve this issue, we present a framework Combining the Invertible and Non-invertible (CIN) mechanisms. The CIN is composed of the invertible part to achieve high imperceptibility and the non-invertible part to strengthen the robustness against strong noise attacks. For the invertible part, we develop a diffusion and extraction module (DEM) and a fusion and split module (FSM) to embed and extract watermarks symmetrically in an invertible way. For the non-invertible part, we introduce a non-invertible attention-based module (NIAM) and the noise-specific selection module (NSM) to solve the asymmetric extraction under a strong noise attack. Extensive experiments demonstrate that our framework outperforms the current state-of-the-art methods of imperceptibility and robustness significantly. Our framework can achieve an average of 99.99% accuracy and 67.66 dB PSNR under noise-free conditions, while 96.64% and 39.28 dB combined strong noise attacks. The code will be available in https://github.com/rmpku/CIN.

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

Only increasing accuracy without considering uncertainty may negatively impact Deep Neural Network (DNN) decision-making and decrease its reliability. This paper proposes five combined preprocessing and post-processing methods for time-series binary classification problems that simultaneously increase the accuracy and reliability of DNN outputs applied in a 5G UAV security dataset. These techniques use DNN outputs as input parameters and process them in different ways. Two methods use a well-known Machine Learning (ML) algorithm as a complement, and the other three use only confidence values that the DNN estimates. We compare seven different metrics, such as the Expected Calibration Error (ECE), Maximum Calibration Error (MCE), Mean Confidence (MC), Mean Accuracy (MA), Normalized Negative Log Likelihood (NLL), Brier Score Loss (BSL), and Reliability Score (RS) and the tradeoffs between them to evaluate the proposed hybrid algorithms. First, we show that the eXtreme Gradient Boosting (XGB) classifier might not be reliable for binary classification under the conditions this work presents. Second, we demonstrate that at least one of the potential methods can achieve better results than the classification in the DNN softmax layer. Finally, we show that the prospective methods may improve accuracy and reliability with better uncertainty calibration based on the assumption that the RS determines the difference between MC and MA metrics, and this difference should be zero to increase reliability. For example, Method 3 presents the best RS of 0.65 even when compared to the XGB classifier, which achieves RS of 7.22.

在这项工作中，我们研究了基于价值的深钢筋学习（DRL）中简单但普遍适用的奖励成型案例。我们表明，线性转换形式的奖励转移等同于更改函数近似中$ q $ function的初始化。基于这样的等价性，我们带来了关键的见解，即积极的奖励转移会导致保守的剥削，而负面的奖励转移会导致好奇心驱动的探索。因此，保守的剥削改善了离线RL价值估计，乐观的价值估计改善了在线RL的勘探。我们验证了对一系列RL任务的见解，并显示了其对基准的改进：（1）在离线RL中，保守的剥削可根据现成的算法提高性能； （2）在在线连续控制中，具有不同转移常数的多个值函数可用于应对探索 - 诠释困境，以提高样品效率； （3）在离散控制任务中，负奖励转移可以改善基于好奇心的探索方法。

近年来，在各种特定于任务的情况下，盲目图像质量评估（BIQA）取得了巨大的成功，这些方案呈现出不变的失真类型和评估标准。但是，由于刚性结构和学习框架，它们不能应用于交叉任务BIQA方案，在这种情况下，失真类型和评估标准在实际应用中不断变化。本文提出了一个可扩展的增量学习框架（SILF），该框架可以在多个评估任务中依次执行BIQA，具有有限的记忆能力。更具体地说，我们开发了动态参数隔离策略，以依次更新特定于任务的参数子集，这些参数子集彼此之间并非重叠。每个参数子集都会暂时解决，以记住对其相应任务的一个评估偏好，并且可以在以下BIQA中自适应地重复使用先前的参数子集，以根据任务相关性实现更好的性能。为了抑制顺序任务学习中记忆容量的不受限制扩展，我们通过从先前解决的参数子集中逐渐和选择性地修剪不重要的神经元来开发可扩展的内存单元，这使我们能够忘记以前的经验的一部分，并释放有限的内存能力，以适应适应新的新任务。对11个IQA数据集进行的广泛实验表明，我们提出的方法在交叉任务BIQA中的其他最新方法显着优于其他最新方法。

鉴于探索性数据分析的日益普及（EDA），了解EDA获得的知识的基本原因至关重要，但仍未进行研究。这项研究首次促进了对数据分析的透明且可解释的观点，称为可解释的数据分析（XDA）。 XDA提供了有关因果和非因果语义的定性和定量解释的数据分析。这样，XDA将显着提高人类对数据分析结果的理解和信心，从而促进现实世界中准确的数据解释和决策。为此，我们提出Xinsight，这是XDA的一般框架。 Xinsight是一种旨在提取因果图，将因果原语转化为XDA语义的三模块，端到端管道，并量化每个解释对数据事实的定量贡献。 Xinsight使用一组设计概念和优化来解决与将因果集成到XDA中相关的固有困难。关于合成和现实世界数据集以及人类评估的实验证明了Xinsight的高度有希望的能力。

Navier-Stokes方程是描述液体和空气等流体运动的重要部分微分方程。由于Navier-Stokes方程的重要性，有效的数值方案的发展对科学和工程师都很重要。最近，随着AI技术的开发，已经设计了几种方法来整合深层神经网络，以模拟和推断不可压缩的Navier-Stokes方程所控制的流体动力学，这些方程可以以无网状和可不同的方式加速模拟或推断过程。在本文中，我们指出，现有的深入Navier-Stokes知情方法的能力仅限于处理非平滑或分数方程，这在现实中是两种关键情况。为此，我们提出了\ emph {深入的随机涡流方法}（drvm），该方法将神经网络与随机涡流动力学系统相结合，等效于Navier-Stokes方程。具体而言，随机涡流动力学激发了用于训练神经网络的基于蒙特卡洛的损失函数，从而避免通过自动差异计算衍生物。因此，DRVM不仅可以有效地求解涉及粗糙路径，非差异初始条件和分数运算符的Navier-Stokes方程，而且还继承了基于深度学习的求解器的无网格和可区分优势。我们对凯奇问题，参数求解器学习以及2-D和3-D不可压缩的Navier-Stokes方程的逆问题进行实验。所提出的方法为Navier-Stokes方程的仿真和推断提供了准确的结果。特别是对于包括奇异初始条件的情况，DRVM明显胜过现有的PINN方法。

类激活图（CAM）已被广泛研究，用于视觉解释卷积神经网络的内部工作机理。现有基于CAM的方法的关键是计算有效的权重以在目标卷积层中结合激活图。现有的基于梯度和得分的加权方案在确保CAM的可区分性或忠诚度方面表现出了优越性，但它们通常在这两种属性中都无法表现出色。在本文中，我们提出了一种名为FD-CAM的新型CAM加权方案，以提高基于CAM的CNN视觉解释的忠诚和可区分性。首先，我们通过执行分组的通道切换操作来提高基于分数的权重的忠诚和可区分性。具体而言，对于每个通道，我们计算其相似性组，并同时打开或关闭一组通道以计算类预测评分的变化为权重。然后，我们将改进的基于得分的权重与常规梯度的权重相结合，以便可以进一步提高最终CAM的可区分性。我们与最新的CAM算法进行了广泛的比较。定量和定性的结果表明，我们的FD-CAM可以对CNN产生更忠实，更具歧视性的视觉解释。我们还进行实验，以验证提出的分组通道切换和重量组合方案在改善结果方面的有效性。我们的代码可在https://github.com/crishhhhh1998/fd-cam上找到。