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.

Out-Of-Distribution (OOD) detection has received broad attention over the years, aiming to ensure the reliability and safety of deep neural networks (DNNs) in real-world scenarios by rejecting incorrect predictions. However, we notice a discrepancy between the conventional evaluation vs. the essential purpose of OOD detection. On the one hand, the conventional evaluation exclusively considers risks caused by label-space distribution shifts while ignoring the risks from input-space distribution shifts. On the other hand, the conventional evaluation reward detection methods for not rejecting the misclassified image in the validation dataset. However, the misclassified image can also cause risks and should be rejected. We appeal to rethink OOD detection from a human-centric perspective, that a proper detection method should reject the case that the deep model's prediction mismatches the human expectations and adopt the case that the deep model's prediction meets the human expectations. We propose a human-centric evaluation and conduct extensive experiments on 45 classifiers and 8 test datasets. We find that the simple baseline OOD detection method can achieve comparable and even better performance than the recently proposed methods, which means that the development in OOD detection in the past years may be overestimated. Additionally, our experiments demonstrate that model selection is non-trivial for OOD detection and should be considered as an integral of the proposed method, which differs from the claim in existing works that proposed methods are universal across different models.

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we explore how HCI and AI researchers conduct user studies in XAI applications based on a systematic literature review. After identifying and thoroughly analyzing 85 core papers with human-based XAI evaluations over the past five years, we categorize them along the measured characteristics of explanatory methods, namely trust, understanding, fairness, usability, and human-AI team performance. Our research shows that XAI is spreading more rapidly in certain application domains, such as recommender systems than in others, but that user evaluations are still rather sparse and incorporate hardly any insights from cognitive or social sciences. Based on a comprehensive discussion of best practices, i.e., common models, design choices, and measures in user studies, we propose practical guidelines on designing and conducting user studies for XAI researchers and practitioners. Lastly, this survey also highlights several open research directions, particularly linking psychological science and human-centered XAI.

对抗性训练（AT）通常被认为是防御对抗性例子的最有效的方法之一，可能会在很大程度上损害标准绩效，因此对工业规模的生产和应用的有用性有限。令人惊讶的是，这种现象在自然语言处理（NLP）任务中完全相反，在该任务中甚至可以从中受益。我们注意到NLP任务中AT的优点可能来自离散和符号输入空间。为了借用NLP风格的优势，我们提出了离散的对抗训练（DAT）。 DAT利用VQGAN改革图像数据以离散类似文本的输入，即视觉单词。然后，它可以最大程度地减少这种离散图像的最大风险，并具有符号对抗扰动。我们从分布的角度进一步提供了解释，以证明DAT的有效性。作为增强视觉表示的插件技术，DAT可以在多个任务上取得重大改进，包括图像分类，对象检测和自我监督学习。尤其是，该模型通过胶带自动编码（MAE）预先训练并由我们的DAT进行微调，而没有额外的数据可以在Imagenet-C上获得31.40 MCE，并且在Stylized-Imagenet上进行了32.77％的TOP-1准确性，建立了新的状态 - 艺术。该代码将在https://github.com/alibaba/easyrobust上找到。

对理解和分解学习的嵌入空间的兴趣正在增长。例如，最近基于概念的解释技术通过可解释的潜在组件分析机器学习模型。必须在模型的嵌入空间中发现此类组件，例如，通过独立的组件分析（ICA）或现代的分离学习技术。尽管这些无监督的方法提供了一个合理的正式框架，但它们要么需要访问数据生成功能，要么对数据分布（例如组件的独立性）施加严格的假设，而这些假设通常在实践中受到侵犯。在这项工作中，我们将视觉模型的概念解释性与解开学习和ICA联系起来。这使我们能够提供有关如何识别组件的第一个理论结果，而无需任何分配假设。从这些见解中，我们得出了与当前方法相比，它适用于更广泛的问题，但拥有正式的可识别性保证。在与组件分析和300多个最先进的分解模型的广泛比较中，即使在不同的分布和相关强度下，DA也稳定地保持了卓越的性能。

最近的研究表明，诸如RNN和Transformers之类的深度学习模型为长期预测时间序列带来了显着的性能增长，因为它们有效地利用了历史信息。但是，我们发现，如何在神经网络中保存历史信息，同时避免过度适应历史上的噪音，这仍然有很大的改进空间。解决此问题可以更好地利用深度学习模型的功能。为此，我们设计了一个\ textbf {f}要求\ textbf {i} mpraved \ textbf {l} egendre \ textbf {m} emory模型，或{\ bf film}：它应用了legendre promotions topimate legendre provientions近似历史信息，近似历史信息，使用傅立叶投影来消除噪声，并添加低级近似值以加快计算。我们的实证研究表明，所提出的膜显着提高了由（\ textbf {20.3 \％}，\ textbf {22.6 \％}）的多变量和单变量长期预测中最新模型的准确性。我们还证明，这项工作中开发的表示模块可以用作一般插件，以提高其他深度学习模块的长期预测性能。代码可从https://github.com/tianzhou2011/film/获得。

近年来提出了各种本地特征归因方法，后续工作提出了几种评估策略。为了评估不同归因技术的归因质量，在图像域中这些评估策略中最流行的是使用像素扰动。但是，最近的进步发现，不同的评估策略会产生归因方法的冲突排名，并且计算的昂贵。在这项工作中，我们介绍了基于像素扰动的评估策略的信息理论分析。我们的发现表明，与其实际值相比，通过删除像素的形状而不是信息泄漏的结果。使用我们的理论见解，我们提出了一个新的评估框架，称为“删除和Debias”（ROAD），该框架提供了两种贡献：首先，它减轻了混杂因素的影响，这需要在评估策略之间更高的一致性。其次，与最先进的时间相比，道路不需要计算昂贵的重新训练步骤，并节省了高达99％的计算成本。我们在https://github.com/tleemann/road_evaluation上发布源代码。

人类参加，过程和分类给定图像的方式有可能使深层学习模型的性能大大效益。利用人类聚焦的地方可以在偏离基本特征时纠正模型以获得正确的决策。为了验证人类注意力包含诸如细粒度分类等决策过程的有价值的信息，我们可以比较人类注意和模型解释在发现重要特征方面。为了实现这一目标，我们为细粒度分类数据集幼崽收集人的凝视数据，并建立一个名为CUB-GHA的数据集（基于凝视的人类注意）。此外，我们提出了凝视增强培训（GAT）和知识融合网络（KFN），将人类凝视知识整合到分类模型中。我们在Cub-Gha和最近发布的医疗数据集CXR眼中实施了我们的胸部X射线图像的建议，包括从放射科医师收集的凝视数据。我们的结果表明，整合人类注意知识有效效益，有效地进行分类，例如，在CXR上改善基线4.38％。因此，我们的工作不仅提供了在细粒度分类中了解人类注意的有价值的见解，而且还有助于将人类凝视与计算机视觉任务集成的未来研究。 CUB-GHA和代码可在https://github.com/yaorong0921/cub -gha获得。

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.