Considering the computation complexity, we propose a Guided Hybrid Quantization with One-to-one Self-Teaching (GHOST}) framework. More concretely, we first design a structure called guided quantization self-distillation (GQSD), which is an innovative idea for realizing lightweight through the synergy of quantization and distillation. The training process of the quantization model is guided by its full-precision model, which is time-saving and cost-saving without preparing a huge pre-trained model in advance. Second, we put forward a hybrid quantization (HQ) module to obtain the optimal bit width automatically under a constrained condition where a threshold for distribution distance between the center and samples is applied in the weight value search space. Third, in order to improve information transformation, we propose a one-to-one self-teaching (OST) module to give the student network a ability of self-judgment. A switch control machine (SCM) builds a bridge between the student network and teacher network in the same location to help the teacher to reduce wrong guidance and impart vital knowledge to the student. This distillation method allows a model to learn from itself and gain substantial improvement without any additional supervision. Extensive experiments on a multimodal dataset (VEDAI) and single-modality datasets (DOTA, NWPU, and DIOR) show that object detection based on GHOST outperforms the existing detectors. The tiny parameters (<9.7 MB) and Bit-Operations (BOPs) (<2158 G) compared with any remote sensing-based, lightweight or distillation-based algorithms demonstrate the superiority in the lightweight design domain. Our code and model will be released at https://github.com/icey-zhang/GHOST.

在本文中，我们为RSI（名为Superyolo）提出了一种准确而快速的小对象检测方法，该方法融合了多模式数据并通过利用辅助超级分辨率（SR）学习并考虑既有辅助的超级分辨率（SR）对象进行高分辨率（HR）对象检测检测准确性和计算成本。首先，我们通过删除焦点模块来保持人力资源特征并显着克服小物体缺失的误差来构建紧凑的基线。其次，我们利用像素级的多模式融合（MF）从各种数据中提取信息，以促进RSI中的小物体更合适和有效的功能。此外，我们设计了一个简单且灵活的SR分支来学习HR特征表示，可以区分具有低分辨率（LR）输入的庞大背景的小物体，从而进一步提高了检测准确性。此外，为避免引入其他计算，SR分支在推理阶段被丢弃，并且由于LR输入而减少了网络模型的计算。实验结果表明，在广泛使用的Vedai RS数据集上，Superyolo的精度为73.61％（在MAP50方面），比SOTA大型模型（例如Yolov5L，Yolov5X和RS设计的Yolors）高10％以上。同时，Superyolo的Gfolps和参数大小比Yolov5X少约18.1倍，4.2倍。我们提出的模型显示出与最新模型相比，具有良好的准确性速度权衡。该代码将在https://github.com/icey-zhang/superyolo上开放。

光谱超分辨率（SSR）是指从RGB对应物中恢复的高光谱图像（HSI）。由于SSR问题的一对多性，可以将单个RGB图像恢复到许多HSIS。解决这个暗示问题的关键是插入多源以前的信息，如自然RGB空间上下文的上下文，深度特征或固有的HSI统计事先等，以提高重建的置信度和保真度光谱。然而，大多数目前的方法只考虑设计定制的卷积神经网络（CNN）的一般和有限的前瞻，这导致无法有效地减轻不良程度。为解决有问题的问题，我们为SSR提出了一个新颖的全面的先前嵌入关系网络（HPRN）。基本上，核心框架由几个多剩余关系块（MRB）进行多种组装，其完全便于RGB信号之前的低频内容的传输和利用。创新性地，引入了RGB输入的语义之前，以识别类别属性，并且向前提出了语义驱动的空间关系模块（SSRM）以使用语义嵌入关系矩阵在聚类的类似特征之间执行特征聚合。此外，我们开发了一种基于变换器的通道关系模块（TCRM），其习惯使用标量作为先前深度特征中的频道方面关系的描述符，并用某些向量替换为变换器特征交互，支持表示更加歧视。为了保持高光谱频带之间的数学相关和光谱一致性，将二阶的先前约束（SOPC）结合到丢失功能中以引导HSI重建过程。

在大多数视频平台（如youtube和Tiktok）中，播放的视频通常经过多个视频编码，例如通过记录设备，视频编辑应用程序的软件编码，以及视频应用程序服务器的单个/多个视频转码。以前的压缩视频恢复工作通常假设压缩伪像是由一次性编码引起的。因此，衍生的解决方案通常在实践中通常不起作用。在本文中，我们提出了一种新的方法，时间空间辅助网络（TSAN），用于转码视频恢复。我们的方法考虑了视频编码和转码之间的独特特征，我们将初始浅编码视频视为中间标签，以帮助网络进行自我监督的注意培训。此外，我们采用相邻的多帧信息，并提出用于转码视频恢复的时间可变形对准和金字塔空间融合。实验结果表明，该方法的性能优于以前的技术。代码可在https://github.com/iceCherylxuli/tsan获得。

预测农作物易受影响的极端温室气温是在温室种植领域的必要条件。它有助于避免热量或冷冻损坏和经济损失。因此，开发可以准确预测它们的模型非常重要。由于数据集中缺乏极端的温度数据，模型准确预测它是具有挑战性的。在本文中，我们提出了一种改进的损耗功能，适用于各种机器学习模型。通过增加极端温度样本的重量并降低误判极端温度的可能性正常，所提出的损失函数可以增强极端情况的预测结果。为了验证所提出的方法的有效性，我们在LightGBM，长期内记忆和人工神经网络中实现了改进的损失功能，并在真实世界温室数据集进行实验。结果表明，与极端情况下的原始模型相比，增强了具有改进损耗功能的模型的性能。改进的模型可用于保证在农业温室中及时判断极端温度，从而防止由不正确的预测引起的不必要的损失。

单眼3D对象检测是自主驾驶中的重要任务。在存在自我汽车姿势改变W.R.T的情况下，它可以很容易难以解决。地平面。由于道路平滑度和斜坡的轻微波动，这很常见。由于工业应用缺乏洞察力，开放数据集的现有方法忽略了相机姿势信息，这不可避免地导致探测器易受相机外在参数的影响。物体的扰动在工业产品最自主驾驶案件中非常受欢迎。为此，我们提出了一种捕获摄像机姿势的新方法，以配制无自脉扰动的检测器。具体地，所提出的框架通过检测消失点和地平线改变来预测相机外在参数。转换器旨在纠正潜在空间中的扰动特征。通过这样做，我们的3D探测器独立于外在参数变化，并在现实情况下产生准确的结果，例如坑道和不均匀的道路，几乎所有现有的单眼检测器都无法处理。实验证明我们的方法与基蒂3D和NUSCENES数据集的大型裕度相比，我们的方法与其他最先进的最先进。

FDG-PET揭示了具有轻度认知障碍（MCI）和Alzheimer疾病（AD）的个体的脑代谢改变。通过计算机辅助诊断（CAD）技术源自FDG-PET的一些生物标志物已被证明可以准确诊断正常控制（NC），MCI和AD。然而，使用FDG-PET图像鉴定早期MCI（EMCI）和晚期MCI（LMCI）的研究仍然不足。与基于FMRI和DTI图像的研究相比，FDG-PET图像中区域间表示特征的研究不足。此外，考虑到不同个体的可变性，一些与两个类非常相似的硬样品限制了分类性能。为了解决这些问题，本文提出了一种新的双线性池和度量学习网络（BMNet），其可以通过构造嵌入空间来提取区域间表示特征并区分硬样品。为了验证所提出的方法，我们从ADNI收集998个FDG-PET图像。在常见的预处理步骤之后，根据自动解剖地标（AAL）模板从每个FDG-PET图像中提取90个特征，然后被发送到所提出的网络。对多种两类分类进行了广泛的5倍交叉验证实验。实验表明，在向基线模型中添加双线性池模块和度量损耗后，大多数度量都会得到改善。具体而言，在EMCI和LMCI之间的分类任务中，在添加三维度量损失后，特异性提高了6.38％，并且使用双线性池模块后，负预测值（NPV）在3.45％后提高了3.45％。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.