Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. Previous MMT systems mainly focus on better access and use of visual information and tend to validate their methods on image-related datasets. These studies face two challenges. First, they can only utilize triple data (bilingual texts with images), which is scarce; second, current benchmarks are relatively restricted and do not correspond to realistic scenarios. Therefore, this paper correspondingly establishes new methods and new datasets for MMT. First, we propose a framework 2/3-Triplet with two new approaches to enhance MMT by utilizing large-scale non-triple data: monolingual image-text data and parallel text-only data. Second, we construct an English-Chinese {e}-commercial {m}ulti{m}odal {t}ranslation dataset (including training and testing), named EMMT, where its test set is carefully selected as some words are ambiguous and shall be translated mistakenly without the help of images. Experiments show that our method is more suitable for real-world scenarios and can significantly improve translation performance by using more non-triple data. In addition, our model also rivals various SOTA models in conventional multimodal translation benchmarks.

Real-time individual endpoint prediction has always been a challenging task but of great clinic utility for both patients and healthcare providers. With 6,879 chronic kidney disease stage 4 (CKD4) patients as a use case, we explored the feasibility and performance of gated recurrent units with decay that models Weibull probability density function (GRU-D-Weibull) as a semi-parametric longitudinal model for real-time individual endpoint prediction. GRU-D-Weibull has a maximum C-index of 0.77 at 4.3 years of follow-up, compared to 0.68 achieved by competing models. The L1-loss of GRU-D-Weibull is ~66% of XGB(AFT), ~60% of MTLR, and ~30% of AFT model at CKD4 index date. The average absolute L1-loss of GRU-D-Weibull is around one year, with a minimum of 40% Parkes serious error after index date. GRU-D-Weibull is not calibrated and significantly underestimates true survival probability. Feature importance tests indicate blood pressure becomes increasingly important during follow-up, while eGFR and blood albumin are less important. Most continuous features have non-linear/parabola impact on predicted survival time, and the results are generally consistent with existing knowledge. GRU-D-Weibull as a semi-parametric temporal model shows advantages in built-in parameterization of missing, native support for asynchronously arrived measurement, capability of output both probability and point estimates at arbitrary time point for arbitrary prediction horizon, improved discrimination and point estimate accuracy after incorporating newly arrived data. Further research on its performance with more comprehensive input features, in-process or post-process calibration are warranted to benefit CKD4 or alike terminally-ill patients.

Visual anomaly detection plays a crucial role in not only manufacturing inspection to find defects of products during manufacturing processes, but also maintenance inspection to keep equipment in optimum working condition particularly outdoors. Due to the scarcity of the defective samples, unsupervised anomaly detection has attracted great attention in recent years. However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working. We focus on outdoor maintenance inspection and contribute a comprehensive Maintenance Inspection Anomaly Detection (MIAD) dataset which contains more than 100K high-resolution color images in various outdoor industrial scenarios. This dataset is generated by a 3D graphics software and covers both surface and logical anomalies with pixel-precise ground truth. Extensive evaluations of representative algorithms for unsupervised anomaly detection are conducted, and we expect MIAD and corresponding experimental results can inspire research community in outdoor unsupervised anomaly detection tasks. Worthwhile and related future work can be spawned from our new dataset.

We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making, which includes Markov decision process (MDP), partially observable Markov decision process (POMDP), and predictive state representation (PSR) as special cases. Toward finding the minimum assumption that empowers sample efficient learning, we propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation in online interactive decision making. In specific, GEC captures the hardness of exploration by comparing the error of predicting the performance of the updated policy with the in-sample training error evaluated on the historical data. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR, where generalized regular PSR, a new tractable PSR class identified by us, includes nearly all known tractable POMDPs. Furthermore, in terms of algorithm design, we propose a generic posterior sampling algorithm, which can be implemented in both model-free and model-based fashion, under both fully observable and partially observable settings. The proposed algorithm modifies the standard posterior sampling algorithm in two aspects: (i) we use an optimistic prior distribution that biases towards hypotheses with higher values and (ii) a loglikelihood function is set to be the empirical loss evaluated on the historical data, where the choice of loss function supports both model-free and model-based learning. We prove that the proposed algorithm is sample efficient by establishing a sublinear regret upper bound in terms of GEC. In summary, we provide a new and unified understanding of both fully observable and partially observable RL.

Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. It is thus natural to ask whether it is possible to leverage these large models as knowledge bases for downstream tasks. In this work, we answer the aforementioned question in unsupervised knowledge-grounded conversation. We explore various methods that best elicit knowledge from large models. Our human study indicates that, though hallucinations exist, large models post the unique advantage of being able to output common sense and summarize facts that cannot be directly retrieved from the search engine. To better exploit such generated knowledge in dialogue generation, we treat the generated knowledge as a noisy knowledge source and propose the posterior-based reweighing as well as the noisy training strategy. Empirical results on two benchmarks show advantages over the state-of-the-art methods.

视觉任务的输出格式和相关内容差异很大，因此很难以相同的结构处理它们。一个主要障碍在于对象级别的视觉任务中的高维输出。在本文中，我们提出了一个以对象为中心的视觉框架OBJ2Seq。 OBJ2Seq将对象作为基本单元，并将大多数对象级的视觉任务视为对象的序列生成问题。因此，这些视觉任务可以分为两个步骤。首先识别给定类别的对象，然后为每个对象生成一个序列。输出序列的定义对于不同的任务有所不同，并且通过将这些序列与地面真相目标匹配来监督模型。 OBJ2SEQ能够灵活地确定输入类别以满足自定义要求，并可以轻松扩展到不同的视觉任务。在对MS Coco进行实验时，OBJ2SEQ在对象检测时可获得45.7％的AP，多标签分类的89.0％AP和人类姿势估计的65.0％AP。这些结果证明了其通常应用于不同视觉任务的潜力。代码已在以下网址提供：https：//github.com/casia-iva-lab/obj2seq。

自我训练在半监督学习中表现出巨大的潜力。它的核心思想是使用在标记数据上学习的模型来生成未标记样本的伪标签，然后自我教学。为了获得有效的监督，主动尝试通常会采用动量老师进行伪标签的预测，但要观察确认偏见问题，在这种情况下，错误的预测可能会提供错误的监督信号并在培训过程中积累。这种缺点的主要原因是，现行的自我训练框架充当以前的知识指导当前状态，因为老师仅与过去的学生更新。为了减轻这个问题，我们提出了一种新颖的自我训练策略，该策略使模型可以从未来学习。具体而言，在每个培训步骤中，我们都会首先优化学生（即，在不将其应用于模型权重的情况下缓存梯度），然后用虚拟未来的学生更新老师，最后要求老师为伪标记生产伪标签目前的学生作为指导。这样，我们设法提高了伪标签的质量，从而提高了性能。我们还通过深入（FST-D）和广泛（FST-W）窥视未来，开发了我们未来自我训练（FST）框架的两个变体。将无监督的域自适应语义分割和半监督语义分割的任务作为实例，我们在广泛的环境下实验表明了我们方法的有效性和优越性。代码将公开可用。

由于乳腺癌的发生和死亡率很高，乳房X线照片中检测肿块很重要。在乳房X线照片质量检测中，对成对病变对应的建模特别重要。但是，大多数现有方法构建了相对粗糙的对应关系，并且尚未利用对应的监督。在本文中，我们提出了一个新的基于变压器的框架CL-NET，以端到端的方式学习病变检测和成对对应。在CL-NET中，提出了观察性病变检测器来实现跨视图候选者的动态相互作用，而病变接头则采用通信监督来更准确地指导相互作用过程。这两种设计的组合实现了对乳房X线照片的成对病变对应的精确理解。实验表明，CL-NET在公共DDSM数据集和我们的内部数据集上产生最先进的性能。此外，在低FPI制度中，它的表现优于先前的方法。

本文探讨了管状结构提取任务的点集表示。与传统的掩码表示相比，点集表示享有其灵活性和表示能力，这不会受到固定网格作为掩模的限制。受此启发，我们提出了PointCatter，这是管状结构提取任务的分割模型的替代方法。PointCatter将图像分为散射区域，并对每个散点区域预测点。我们进一步提出了基于贪婪的区域的两分匹配算法，以端到端训练网络。我们在四个公共管状数据集上基准测试了点刻表，并且有关管状结构分割和中心线提取任务的广泛实验证明了我们方法的有效性。代码可在https://github.com/zhangzhao2022/pointscatter上找到。

3D对象检测通过将点云作为唯一的输入来取得了显着的进展。但是，点云通常遭受不完整的几何结构和缺乏语义信息，这使得检测器难以准确地对检测到的对象进行分类。在这项工作中，我们专注于如何有效利用来自图像的对象级信息来提高基于点的3D检测器的性能。我们提出DEMF，这是一种简单而有效的方法，将图像信息融合到点特征中。给定一组点特征和图像特征图，DEMF通过将3D点的投影2D位置作为参考来自适应地汇总图像特征。我们在挑战性的Sun RGB-D数据集上评估了我们的方法，从而提高了最新的结果（+2.1 map@0.25和+2.3map@0.5）。代码可从https://github.com/haoy945/demf获得。