细颗粒实体打字（FET）旨在推断本文中提及的特定语义类型。 FET的现代方法主要集中于学习某种类型的外观。很少有作品直接建模类型差异，也就是说，让模型知道一种类型与其他类型不同的程度。为了减轻这个问题，我们提出了一种富含类型的FET的分层对比策略。我们的方法可以直接建模层次类型之间的差异，并提高区分多元类似类型的能力。一方面，我们将类型嵌入到实体上下文中，以使类型的信息直接感知。另一方面，我们在层次结构上设计了一个约束的对比策略，以直接建模类型差异，这可以同时感知不同粒度下类型之间的区分性。 BBN，Ontonotes和Figer的三个基准测试的实验结果表明，我们的方法通过有效建模类型差异在FET上实现了显着性能。

回归学习是经典的，是医学图像分析的基础。它为许多关键应用程序提供了连续的映射，例如属性估计，对象检测，分割和非刚性注册。但是，先前的研究主要以案例标准（如均方误差）为优化目标。他们忽略了非常重要的人口相关标准，这正是许多任务中的最终评估指标。在这项工作中，我们建议通过有关直接优化细粒相关损失的新型研究来重新审视经典回归任务。我们主要探索两个互补相关索引作为可学习的损失：Pearson线性相关（PLC）和Spearman等级相关性（SRC）。本文的贡献是两个折叠。首先，对于全球层面的PLC，我们提出了一项策略，以使其对异常值进行强大的态度并规范关键分布因素。这些努力显着稳定学习并扩大了PLC的功效。其次，对于本地级别的SRC，我们提出了一种粗到精细的方案，以减轻样品之间确切排名顺序的学习。具体而言，我们将样本排名的学习转换为样本之间相似关系的学习。我们在两个典型的超声图像回归任务上广泛验证了我们的方法，包括图像质量评估和生物措施测量。实验证明，通过直接优化相关性的细粒度指导，回归性能得到显着提高。我们提出的相关性损失是一般的，可以扩展到更重要的应用程序。

协作多代理增强学习（MARL）已在许多实际应用中广泛使用，在许多实际应用中，每个代理商都根据自己的观察做出决定。大多数主流方法在对分散的局部实用程序函数进行建模时，将每个局部观察结果视为完整的。但是，他们忽略了这样一个事实，即可以将局部观察信息进一步分为几个实体，只有一部分实体有助于建模推理。此外，不同实体的重要性可能会随着时间而变化。为了提高分散政策的性能，使用注意机制用于捕获本地信息的特征。然而，现有的注意模型依赖于密集的完全连接的图，并且无法更好地感知重要状态。为此，我们提出了一个稀疏的状态MARL（S2RL）框架，该框架利用稀疏的注意机制将无关的信息丢弃在局部观察中。通过自我注意力和稀疏注意机制估算局部效用函数，然后将其合并为标准的关节价值函数和中央评论家的辅助关节价值函数。我们将S2RL框架设计为即插即用的模块，使其足够一般，可以应用于各种方法。关于Starcraft II的广泛实验表明，S2RL可以显着提高许多最新方法的性能。

神经解码在大脑和外部世界之间的相互作用中起着至关重要的作用。在本文中，我们基于猕媚的神经信号直接解码手指的运动轨道。监督的回归方法可能会过度符合噪声包含的实际标签，并且需要高标签成本，而无监督的方法通常具有不令人满意的准确性。此外，空间和时间信息通常被忽略或不充分利用这些作品。这使我们提出了一种稳健的弱监管方法，用于神经解码的VIF-SD2E。特别地，VIF-SD2E由空间分割（SD）模块和探索开发（2E）策略组成，以有效利用外部世界的空间信息和神经活动的时间信息，其中SD2E输出进行了比较随着弱的0/1视觉反馈（VIF）标签进行培训。广泛的实验证明了我们方法的有效性，有时可能与监督对应物相当。

遥感图像中的实例分段的任务，旨在在实例级别执行对象的每像素标记，对于各种民用应用非常重要。尽管以前的成功，但大多数现有的实例分割方法设计用于自然图像时，可以在直接应用于顶视图遥感图像时遇到清晰的性能下降。通过仔细分析，我们观察到由于严重的规模变化，低对比度和聚类分布，挑战主要来自歧视性对象特征。为了解决这些问题，提出了一种新颖的上下文聚合网络（CATNET）来改善特征提取过程。所提出的模型利用了三个轻量级的即插即用模块，即密度特征金字塔网络（Densfpn），空间上下文金字塔（SCP）和兴趣提取器（Hroie）的分层区域，以聚合在功能，空间和的全局视觉上下文实例域分别。 DenseFPN是一种多尺度特征传播模块，通过采用级别的残差连接，交叉级密度连接和具有重新加权策略来建立更灵活的信息流。利用注意力机制，SCP进一步通过将全局空间上下文聚合到当地区域来增强特征。对于每个实例，Hroie自适应地为不同的下游任务生成ROI功能。我们对挑战ISAID，DIOR，NWPU VHR-10和HRSID数据集进行了广泛的评估。评估结果表明，所提出的方法优于具有类似的计算成本的最先进。代码可在https://github.com/yeliudev/catnet上获得。

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

In this paper, a semantic communication framework for image transmission is developed. In the investigated framework, a set of servers cooperatively transmit images to a set of users utilizing semantic communication techniques. To evaluate the performance of studied semantic communication system, a multimodal metric is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem aiming to minimize each server's transmission latency while reaching the ISS requirement. To solve this problem, a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) is proposed, which enables servers to coordinate for training and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL, the proposed RL improves the valuable action exploration of servers and the probability of finding a globally optimal RB allocation policy based on local observation. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% compared to traditional multi-agent RL.

New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon.

With the development of technology and sharing economy, Airbnb as a famous short-term rental platform, has become the first choice for many young people to select. The issue of Airbnb's pricing has always been a problem worth studying. While the previous studies achieve promising results, there are exists deficiencies to solve. Such as, (1) the feature attributes of rental are not rich enough; (2) the research on rental text information is not deep enough; (3) there are few studies on predicting the rental price combined with the point of interest(POI) around the house. To address the above challenges, we proposes a multi-source information embedding(MSIE) model to predict the rental price of Airbnb. Specifically, we first selects the statistical feature to embed the original rental data. Secondly, we generates the word feature vector and emotional score combination of three different text information to form the text feature embedding. Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding. Finally, this paper combines the three modules into multi source rental representations, and uses the constructed fully connected neural network to predict the price. The analysis of the experimental results shows the effectiveness of our proposed model.

Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. However, they neglect the relationship between multiple granularities and different features in alignment, degrading detection. Addressing this, we introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning. The key is to encode the dependencies across different granularities including pixel-, instance-, and category-levels simultaneously to align two domains. Specifically, based on pixel-level features, we first develop an omni-scale gated fusion (OSGF) module to aggregate discriminative representations of instances with scale-aware convolutions, leading to robust multi-scale detection. Besides, we introduce multi-granularity discriminators to identify where, either source or target domains, different granularities of samples come from. Note that, MGA not only leverages instance discriminability in different categories but also exploits category consistency between two domains for detection. Furthermore, we present an adaptive exponential moving average (AEMA) strategy that explores model assessments for model update to improve pseudo labels and alleviate local misalignment problem, boosting detection robustness. Extensive experiments on multiple domain adaption scenarios validate the superiority of MGA over other approaches on FCOS and Faster R-CNN detectors. Code will be released at https://github.com/tiankongzhang/MGA.