The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

无人驾驶飞机（UAV）的实时对象检测是一个具有挑战性的问题，因为Edge GPU设备作为物联网（IoT）节点的计算资源有限。为了解决这个问题，在本文中，我们提出了一种基于Yolox模型的新型轻型深度学习体系结构，用于Edge GPU上的实时对象检测。首先，我们设计了一个有效且轻巧的PixSF头，以更换Yolox的原始头部以更好地检测小物体，可以将其进一步嵌入深度可分离的卷积（DS Conv）中，以达到更轻的头。然后，开发为减少网络参数的颈层中的较小结构，这是精度和速度之间的权衡。此外，我们将注意模块嵌入头层中，以改善预测头的特征提取效果。同时，我们还改进了标签分配策略和损失功能，以减轻UAV数据集的类别不平衡和盒子优化问题。最后，提出了辅助头进行在线蒸馏，以提高PIXSF Head中嵌入位置嵌入和特征提取的能力。在NVIDIA Jetson NX和Jetson Nano GPU嵌入平台上，我们的轻质模型的性能得到了实验验证。扩展的实验表明，与目前的模型相比，Fasterx模型在Visdrone2021数据集中实现了更好的折衷和延迟之间的折衷。

零拍摄对象检测（ZSD），将传统检测模型扩展到检测来自Unseen类别的对象的任务，已成为计算机视觉中的新挑战。大多数现有方法通过严格的映射传输策略来解决ZSD任务，这可能导致次优ZSD结果：1）这些模型的学习过程忽略了可用的看不见的类信息，因此可以轻松地偏向所看到的类别; 2）原始视觉特征空间并不合适，缺乏歧视信息。为解决这些问题，我们开发了一种用于ZSD的新型语义引导的对比网络，命名为Contrastzsd，一种检测框架首先将对比学习机制带入零拍摄检测的领域。特别地，对比度包括两个语义导向的对比学学习子网，其分别与区域类别和区域区域对之间形成对比。成对对比度任务利用从地面真理标签和预定义的类相似性分布派生的附加监督信号。在那些明确的语义监督的指导下，模型可以了解更多关于看不见的类别的知识，以避免看到概念的偏见问题，同时优化视觉功能的数据结构，以更好地辨别更好的视觉语义对齐。广泛的实验是在ZSD，即Pascal VOC和MS Coco的两个流行基准上进行的。结果表明，我们的方法优于ZSD和广义ZSD任务的先前最先进的。

概率分类器输出置信信心得分随着他们的预测，并且应该校准这些置信分数，即，它们应该反映预测的可靠性。最小化标准度量的置信度分数，例如预期的校准误差（ECE）准确地测量整个人口平均值的可靠性。然而，通常不可能测量单独预测的可靠性。在这项工作中，我们提出了本地校准误差（LCE），以跨越平均值和各个可靠性之间的间隙。对于每个单独的预测，LCE测量一组类似预测的平均可靠性，其中通过预先训练的特征空间上的内核函数和通过预测模型信仰的融合方案来量化相似性。我们从理论上显示了LCE可以从数据估计，并经验地发现它显示出比ECE可以检测到更细粒度的错误级别模式。我们的关键结果是一种新颖的局部重新校准方法，以改善个人预测的置信度分数并减少LCE。实验，我们表明我们的重新校准方法产生更准确的置信度分数，从而提高了具有图像和表格数据的分类任务的下游公平性和决策。

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.

Although deep learning has made remarkable progress in processing various types of data such as images, text and speech, they are known to be susceptible to adversarial perturbations: perturbations specifically designed and added to the input to make the target model produce erroneous output. Most of the existing studies on generating adversarial perturbations attempt to perturb the entire input indiscriminately. In this paper, we propose ExploreADV, a general and flexible adversarial attack system that is capable of modeling regional and imperceptible attacks, allowing users to explore various kinds of adversarial examples as needed. We adapt and combine two existing boundary attack methods, DeepFool and Brendel\&Bethge Attack, and propose a mask-constrained adversarial attack system, which generates minimal adversarial perturbations under the pixel-level constraints, namely ``mask-constraints''. We study different ways of generating such mask-constraints considering the variance and importance of the input features, and show that our adversarial attack system offers users good flexibility to focus on sub-regions of inputs, explore imperceptible perturbations and understand the vulnerability of pixels/regions to adversarial attacks. We demonstrate our system to be effective based on extensive experiments and user study.