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.

With the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in area of animal face recognition were carried on pigs, cattle, sheep and other livestock. Face recognition consists of three sub-task: face detection, face normalizing and face identification. Most of animal face recognition study focuses on face detection and face identification. Animals are often uncooperative when taking photos, so the collected animal face images are often in arbitrary directions. The use of non-standard images may significantly reduce the performance of face recognition system. However, there is no study on normalizing of the animal face image with arbitrary directions. In this study, we developed a light-weight angle detection and region-based convolutional network (LAD-RCNN) containing a new rotation angle coding method that can detect the rotation angle and the location of animal face in one-stage. LAD-RCNN has a frame rate of 72.74 FPS (including all steps) on a single GeForce RTX 2080 Ti GPU. LAD-RCNN has been evaluated on multiple dataset including goat dataset and gaot infrared image. Evaluation result show that the AP of face detection was more than 95% and the deviation between the detected rotation angle and the ground-truth rotation angle were less than 0.036 (i.e. 6.48{\deg}) on all the test dataset. This shows that LAD-RCNN has excellent performance on livestock face and its direction detection, and therefore it is very suitable for livestock face detection and Normalizing. Code is available at https://github.com/SheepBreedingLab-HZAU/LAD-RCNN/

Active域适应（ADA）查询所选目标样本的标签，以帮助将模型从相关的源域调整为目标域。由于其有希望的表现，标签成本最少，因此最近引起了人们越来越多的关注。然而，现有的ADA方法尚未完全利用查询数据的局部环境，这对ADA很重要，尤其是当域间隙较大时。在本文中，我们提出了一个局部环境感知的活动域适应性（LADA）的新框架，该框架由两个关键模块组成。本地上下文感知的活动选择（LAS）模块选择其类概率预测与邻居不一致的目标样本。局部上下文感知模型适应（LMA）模块完善了具有查询样本及其扩展的邻居的模型，并由上下文保留损失正规化。广泛的实验表明，与现有的主动选择策略相比，LAS选择了更多的信息样本。此外，配备了LMA，整个LADA方法的表现优于各种基准测试的最先进的ADA解决方案。代码可在https://github.com/tsun/lada上找到。

增强对未标记目标数据的模型预测置信度是无监督域适应（UDA）的重要目标。在本文中，我们探讨了关于倒数第二个线性分类层的输入特征的对抗性训练。我们表明，这种策略比以前的作品所使用的对对抗性图像或中间特征的对抗训练更有效，并且与提高预测置信度的目的更加相关。此外，通过在域适应中通常使用激活归一化以减少域间隙，我们得出了两个变体，并系统地分析了归一化对对抗性训练的影响。这在理论上和通过对实际适应任务的经验分析都进行了说明。在标准设置和无源DATA设置下，对流行的UDA基准测试进行了广泛的实验。结果证明了我们的方法可以在以前的艺术中取得最佳分数。

本文提出了一个最佳的运动计划框架，以自动生成多功能的四足动物跳跃运动（例如，翻转，旋转）。通过质心动力学的跳跃运动被配制为受机器人基诺动力约束的12维黑盒优化问题。基于梯度的方法在解决轨迹优化方面取得了巨大成功（TO），但是，需要先验知识（例如，参考运动，联系时间表），并导致次级最佳解决方案。新提出的框架首先采用了基于启发式的优化方法来避免这些问题。此外，针对机器人地面反作用力（GRF）计划中的基于启发式算法的算法创建了优先级的健身函数，增强收敛性和搜索性能。由于基于启发式的算法通常需要大量的时间，因此计划离线运动并作为运动前库存储。选择器旨在自动选择用用户指定或感知信息作为输入的动作。该框架仅通过几项具有挑战性的跳跃动作在开源迷你室中的简单连续跟踪PD控制器进行了成功验证，包括跳过30厘米高度的窗户形状的障碍物，并在矩形障碍物上与左悬挂式障碍物。 27厘米高。

目标域中的标签放弃使无监督的域适应性（UDA）成为许多现实世界应用中的吸引力技术，尽管它也带来了巨大的挑战，因为没有标记目标数据，模型适应变得更加困难。在本文中，我们通过从目标领域的先验知识中寻求赔偿来解决这个问题，这在实践中通常（部分）可用于人类专业知识。这导致了一个新颖而实用的环境，除了训练数据外，还可以提供有关目标类别分布的一些先验知识。我们将该设置称为知识引导的无监督域适应性（KUDA）。特别是，我们考虑了有关目标域中类别分布的两种特定类型的先验知识：一个描述单个类概率的下层和上限的Unary Bound，以及描述了两个类概率之间关系的二进制关系。我们提出了一个使用此类先验知识来完善模型生成的伪标签的通用整流模块。该模块被配制为从先验知识和光滑的正常化程序中得出的零一编程问题。它可以很容易地插入基于自我训练的UDA方法中，我们将其与两种最先进的方法结合使用，即射击和用餐。四个基准测试的经验结果证实，整流模块显然改善了伪标签的质量，这反过来又受益于自我训练阶段。在先验知识的指导下，两种方法的性能都大大提高。我们希望我们的工作能够激发进一步的调查，以整合UDA的先验知识。代码可在https://github.com/tsun/kuda上找到。

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.

Function approximation (FA) has been a critical component in solving large zero-sum games. Yet, little attention has been given towards FA in solving \textit{general-sum} extensive-form games, despite them being widely regarded as being computationally more challenging than their fully competitive or cooperative counterparts. A key challenge is that for many equilibria in general-sum games, no simple analogue to the state value function used in Markov Decision Processes and zero-sum games exists. In this paper, we propose learning the \textit{Enforceable Payoff Frontier} (EPF) -- a generalization of the state value function for general-sum games. We approximate the optimal \textit{Stackelberg extensive-form correlated equilibrium} by representing EPFs with neural networks and training them by using appropriate backup operations and loss functions. This is the first method that applies FA to the Stackelberg setting, allowing us to scale to much larger games while still enjoying performance guarantees based on FA error. Additionally, our proposed method guarantees incentive compatibility and is easy to evaluate without having to depend on self-play or approximate best-response oracles.

Correlated Equilibrium is a solution concept that is more general than Nash Equilibrium (NE) and can lead to outcomes with better social welfare. However, its natural extension to the sequential setting, the \textit{Extensive Form Correlated Equilibrium} (EFCE), requires a quadratic amount of space to solve, even in restricted settings without randomness in nature. To alleviate these concerns, we apply \textit{subgame resolving}, a technique extremely successful in finding NE in zero-sum games to solving general-sum EFCEs. Subgame resolving refines a correlation plan in an \textit{online} manner: instead of solving for the full game upfront, it only solves for strategies in subgames that are reached in actual play, resulting in significant computational gains. In this paper, we (i) lay out the foundations to quantify the quality of a refined strategy, in terms of the \textit{social welfare} and \textit{exploitability} of correlation plans, (ii) show that EFCEs possess a sufficient amount of independence between subgames to perform resolving efficiently, and (iii) provide two algorithms for resolving, one using linear programming and the other based on regret minimization. Both methods guarantee \textit{safety}, i.e., they will never be counterproductive. Our methods are the first time an online method has been applied to the correlated, general-sum setting.

Supervised learning methods have been suffering from the fact that a large-scale labeled dataset is mandatory, which is difficult to obtain. This has been a more significant issue for fashion compatibility prediction because compatibility aims to capture people's perception of aesthetics, which are sparse and changing. Thus, the labeled dataset may become outdated quickly due to fast fashion. Moreover, labeling the dataset always needs some expert knowledge; at least they should have a good sense of aesthetics. However, there are limited self/semi-supervised learning techniques in this field. In this paper, we propose a general color distortion prediction task forcing the baseline to recognize low-level image information to learn more discriminative representation for fashion compatibility prediction. Specifically, we first propose to distort the image by adjusting the image color balance, contrast, sharpness, and brightness. Then, we propose adding Gaussian noise to the distorted image before passing them to the convolutional neural network (CNN) backbone to learn a probability distribution over all possible distortions. The proposed pretext task is adopted in the state-of-the-art methods in fashion compatibility and shows its effectiveness in improving these methods' ability in extracting better feature representations. Applying the proposed pretext task to the baseline can consistently outperform the original baseline.