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.

Although pre-trained language models (PLMs) have shown impressive performance by text-only self-supervised training, they are found lack of visual semantics or commonsense, e.g., sizes, shapes, and colors of commonplace objects. Existing solutions often rely on explicit images for visual knowledge augmentation (requiring time-consuming retrieval or generation), and they also conduct the augmentation for the whole input text, without considering whether it is actually needed in specific inputs or tasks. To address these issues, we propose a novel visually-augmented fine-tuning approach that can be generally applied to various PLMs or NLP tasks, without using any retrieved or generated images, namely VAWI. Specifically, we first identify the visually-hungry words (VH-words) from input text via a token selector, where three different methods have been proposed, including syntax-, attention- and learning-based strategies. Then, we adopt a fixed CLIP text encoder to generate the visually-augmented representations of these VH-words. As it has been pre-trained by vision-language alignment task on the large-scale corpus, it is capable of injecting visual semantics into the aligned text representations. Finally, the visually-augmented features will be fused and transformed into the pre-designed visual prompts based on VH-words, which can be inserted into PLMs to enrich the visual semantics in word representations. We conduct extensive experiments on ten NLP tasks, i.e., GLUE benchmark, CommonsenseQA, CommonGen, and SNLI-VE. Experimental results show that our approach can consistently improve the performance of BERT, RoBERTa, BART, and T5 at different scales, and outperform several competitive baselines significantly. Our codes and data are publicly available at~\url{https://github.com/RUCAIBox/VAWI}.

This paper studies the problem of stochastic continuum-armed bandit with constraints (SCBwC), where we optimize a black-box reward function $f(x)$ subject to a black-box constraint function $g(x)\leq 0$ over a continuous space $\mathcal X$. We model reward and constraint functions via Gaussian processes (GPs) and propose a Rectified Pessimistic-Optimistic Learning framework (RPOL), a penalty-based method incorporating optimistic and pessimistic GP bandit learning for reward and constraint functions, respectively. We consider the metric of cumulative constraint violation $\sum_{t=1}^T(g(x_t))^{+},$ which is strictly stronger than the traditional long-term constraint violation $\sum_{t=1}^Tg(x_t).$ The rectified design for the penalty update and the pessimistic learning for the constraint function in RPOL guarantee the cumulative constraint violation is minimal. RPOL can achieve sublinear regret and cumulative constraint violation for SCBwC and its variants (e.g., under delayed feedback and non-stationary environment). These theoretical results match their unconstrained counterparts. Our experiments justify RPOL outperforms several existing baseline algorithms.

有两种流行的损失功能用于视觉检索，即三胞胎损失和对比度学习损失，这两者本质上都可以最大程度地减少负对和正对的相似性之间的差异。更具体地说，在现有的检索模型中广泛使用的硬采矿（三重态HN）的三胞胎损失很容易落入训练中的局部最小值。另一方面，广泛用于视觉的预训练中的视觉对比学习损失（VLC）已被证明可以在视觉语言检索上获得显着的性能提高，但通过使用微调的性能来实现。小型数据集上的VLC并不令人满意。本文提出了对视觉语言检索的统一损失相似性优化，为理解现有的损失功能提供了强大的工具。我们的统一损失包括VLC的硬样品挖掘策略，并引入了三胞胎损失使用的边距，以获得更好的相似性分离。结果表明，三重态HN和VLC都是我们统一损失的特殊形式。与三胞胎-HN相比，我们的统一损失具有快速的收敛速度。与VLC相比，我们的统一损失更具歧视性，可以在下游微调任务中更好地概括。图像文本和视频检索基准测试的实验表明，我们的统一损失可以显着提高最新检索模型的性能。

本文提供了功能线性模型的在线随机梯度下降算法的收敛分析。采用坡度函数规律性，内核空间容量以及采样过程协方差运算符的能力的表征，可以实现收敛速率的显着提高。研究了预测问题和估计问题，我们表明，随着目标函数的规律性的增加，能力假设可以减轻收敛速率的饱和。我们表明，通过正确选择的内核，容量假设可以完全弥补预测问题的规律性假设（但不能用于估计问题）。这表明了功能数据分析中的预测问题与估计问题之间的显着差异。

在本文中，我们提出了一种基于排名的水下图像质量评估（UIQA）方法，该方法缩写为Uranker。乌兰克（Uranker）建立在高效的注意力图像变压器上。在水下图像方面，我们特别设计（1）直方图嵌入了水下图像作为直方图表的颜色分布以参加全局降解，以及（2）与模型局部降解的动态跨尺度对应关系。最终预测取决于不同量表的类代币，该标记是全面考虑多尺度依赖性的。随着保证金排名损失，我们的乌员可以根据其视觉质量通过不同的水下图像增强（UIE）算法来准确对同一场景的水下图像的顺序进行排名。为此，我们还贡献了一个数据集，即Urankerset，其中包含不同的UIE算法和相应的感知排名增强的足够结果，以训练我们的uranker。除了Uranker的良好表现外，我们发现一个简单的U-Shape UIE网络与我们的预训练的Uranker相结合时可以获得有希望的性能。此外，我们还提出了一个标准化尾巴，可以显着提高UIE网络的性能。广泛的实验证明了我们方法的最新性能。讨论了我们方法的关键设计。我们将发布我们的数据集和代码。

关于语言引导的图像操纵的最新作品在提供丰富的语义方面表现出了极大的语言力量，尤其是对于面部图像。但是，语言中的其他自然信息，动作的探索较少。在本文中，我们利用运动信息并研究一项新颖的任务，语言引导的面部动画，旨在在语言的帮助下对静态面部图像进行动画。为了更好地利用语言的语义和动作，我们提出了一个简单而有效的框架。具体而言，我们提出了一个经常性运动生成器，以从语言中提取一系列语义和运动信息，并将其与视觉信息一起提供给预训练的样式，以生成高质量的帧。为了优化所提出的框架，提出了三个精心设计的损失功能，包括保持面部身份的正规化损失，路径长度正规化损失以确保运动平滑度和对比度损失，以在一个模型中使用各种语言指导启用视频综合。对不同领域的定性和定量评估进行了广泛的实验（\ textit {ef。语。代码将在https://github.com/tiankaihang/language-guided-animation.git上找到。

组合推荐人（CR）系统一次在结果页面中一次将项目列表馈送给用户，其中用户行为受到上下文信息和项目的影响。 CR被称为组合优化问题，目的是最大程度地提高整个列表的建议奖励。尽管它很重要，但由于在线环境中的效率，动态和个性化要求，建立实用的CR系统仍然是一个挑战。特别是，我们将问题分为两个子问题，即列表生成和列表评估。新颖和实用的模型体系结构是为这些子问题设计的，旨在共同优化有效性和效率。为了适应在线案例，给出了形成参与者批判性增强框架的自举算法，以探索在长期用户互动中更好的推荐模式。离线和在线实验结果证明了拟议的JDREC框架的功效。 JDREC已应用于在线JD建议中，将点击率提高了2.6％，平台的合成价值提高了5.03％。我们将发布本研究中使用的大规模数据集，以为研究界做出贡献。

我们提出了Pangu-Coder，这是一种仅预读的解码器语言模型，该模型采用pangu-alpha架构进行文本到代码生成，即给定自然语言问题描述的编程语言解决方案的合成。我们使用两阶段策略训练Pangu-Coder：第一阶段采用因果语言建模（CLM）来预先培训原始编程语言数据，而第二阶段则使用因果语言建模和掩盖语言建模（MLM）的组合培训目标，专注于文本到代码生成的下游任务，并培训松散的自然语言程序定义和代码功能。最后，我们讨论了pangu-coder-ft，该pander the是通过竞争性编程问题和代码与持续集成测试的结合进行了微调的。我们评估了pangu-coder，重点是它是否生成功能上正确的程序，并证明它在参加较小的上下文窗口和较少的数据培训的同时，它比诸如Codex之类的类似大小的模型（例如Codex）实现等效性或更好的性能。

机器的图像编码（ICM）旨在压缩图像进行AI任务分析，而不是满足人类的看法。学习一种既是一般（用于AI任务）的特征，也是紧凑的（用于压缩）的功能，这对于其成功而言至关重要。在本文中，我们试图通过学习通用功能，同时考虑压缩来开发ICM框架。我们将诸如无所不能功能和相应框架的功能命名为Omni-ICM。考虑到自我监督学习（SSL）提高了特征的概括，我们将其与压缩任务集成到OMNI-ICM框架中，以学习无所不能的功能。但是，在SSL中协调语义建模并在压缩中删除冗余是不平凡的，因此我们通过合作实例区分和熵最小化以自适应掉落的信息来设计新颖的信息过滤（如果）模块，以较弱相关的信息执行AI任务（例如，某些纹理冗余）。与以前的特定解决方案不同，Omni-ICM可以直接基于学习的无能功能的AI任务分析，而无需联合培训或额外的转换。尽管简单而直观，但Omni-ICM在多个基本愿景任务上大大优于现有的传统和基于学习的编解码器。