以前通过一个位置的历史轨迹可能有助于推断该位置当前代理的未来轨迹。尽管在高清图的指导下进行了轨迹预测的大大改善，但只有少数作品探讨了这种当地历史信息。在这项工作中，我们将这些信息重新引入了轨迹预测系统的新类型的输入数据：本地行为数据，我们将其概念化为特定于位置的历史轨迹的集合。局部行为数据有助于系统强调预测区域，并更好地了解静态地图对象对移动代理的影响。我们提出了一个新型的本地行为感知（LBA）预测框架，该框架通过从观察到的轨迹，高清图和局部行为数据中融合信息来提高预测准确性。同样，如果这种历史数据不足或不可用，我们采用了本地行为（LBF）预测框架，该框架采用了基于知识依据的架构来推断缺失数据的影响。广泛的实验表明，通过这两个框架升级现有方法可显着提高其性能。特别是，LBA框架将SOTA方法在Nuscenes数据集上的性能提高了至少14％的K = 1度量。

Temporal modeling is crucial for various video learning tasks. Most recent approaches employ either factorized (2D+1D) or joint (3D) spatial-temporal operations to extract temporal contexts from the input frames. While the former is more efficient in computation, the latter often obtains better performance. In this paper, we attribute this to a dilemma between the sufficiency and the efficiency of interactions among various positions in different frames. These interactions affect the extraction of task-relevant information shared among frames. To resolve this issue, we prove that frame-by-frame alignments have the potential to increase the mutual information between frame representations, thereby including more task-relevant information to boost effectiveness. Then we propose Alignment-guided Temporal Attention (ATA) to extend 1-dimensional temporal attention with parameter-free patch-level alignments between neighboring frames. It can act as a general plug-in for image backbones to conduct the action recognition task without any model-specific design. Extensive experiments on multiple benchmarks demonstrate the superiority and generality of our module.

将现有的旅游照片从部分捕获的场景扩展到完整的场景是摄影应用的理想体验之一。尽管对照片的外推进行了充分的研究，但是将照片（即自拍照）从狭窄的视野推断到更广阔的视野，同时保持相似的视觉样式是更具挑战性的。在本文中，我们提出了一个分解的神经重新渲染模型，以从混乱的户外互联网照片集中产生逼真的新颖观点，该视图可以使应用程序包括可控场景重新渲染，照片外推甚至外推3D照片生成。具体而言，我们首先开发出一种新颖的分解重新渲染管道，以处理几何，外观和照明分解中的歧义。我们还提出了一种合成的培训策略，以应对互联网图像中意外的阻塞。此外，为了推断旅游照片时增强照片现实主义，我们提出了一个新颖的现实主义增强过程来补充外观细节，该过程会自动传播质地细节，从狭窄的捕获照片到外推神经渲染图像。室外场景上的实验和照片编辑示例证明了我们在照片现实主义和下游应用中提出的方法的出色性能。

基于学习的多视图立体声（MVS）方法取得了令人印象深刻的进步，并且近年来超越了传统方法。但是，它们的准确性和完整性仍在挣扎。在本文中，我们提出了一种新方法，以增强受对比度学习和功能匹配启发的现有网络的性能。首先，我们提出了一个对比匹配损失（CML），该损失将正确的匹配点视为正样品，将正确的匹配点视为正样本，并将其他点视为阴性样本，并根据特征的相似性计算对比度损失。我们进一步提出了一个加权局灶性损失（WFL），以提高分类能力，从而削弱了根据预测的置信度，在不重要的区域中低信任像素对损失的贡献。在DTU，坦克和寺庙和混合MVS数据集上进行的广泛实验表明，我们的方法可实现最先进的性能，并在基线网络上取得了重大改进。

软演员 - 评论家（SAC）是最先进的偏离策略强化学习（RL）算法之一，其在基于最大熵的RL框架内。 SAC被证明在具有良好稳定性和稳健性的持续控制任务的列表中表现得非常好。 SAC了解一个随机高斯政策，可以最大限度地提高预期奖励和政策熵之间的权衡。要更新策略，SAC可最大限度地减少当前策略密度与软值函数密度之间的kl分歧。然后用于获得这种分歧的近似梯度的回报。在本文中，我们提出了跨熵策略优化（SAC-CEPO）的软演员 - 评论家，它使用跨熵方法（CEM）来优化SAC的政策网络。初始思想是使用CEM来迭代地对软价函数密度的最接近的分布进行采样，并使用结果分布作为更新策略网络的目标。为了降低计算复杂性，我们还介绍了一个解耦的策略结构，该策略结构将高斯策略解耦为一个策略，了解了学习均值的均值和另一个策略，以便只有CEM训练平均政策。我们表明，这种解耦的政策结构确实会聚到最佳，我们还通过实验证明SAC-CEPO实现对原始囊的竞争性能。

A storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards however remains challenging which not only requires association between high-level texts and images, but also demands for long-term reasoning to make transitions smooth across shots. In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images to visualize the text synopsis. We construct a MovieNet-TeViS benchmark based on the public MovieNet dataset. It contains 10K text synopses each paired with keyframes that are manually selected from corresponding movies by considering both relevance and cinematic coherence. We also present an encoder-decoder baseline for the task. The model uses a pretrained vision-and-language model to improve high-level text-image matching. To improve coherence in long-term shots, we further propose to pre-train the decoder on large-scale movie frames without text. Experimental results demonstrate that our proposed model significantly outperforms other models to create text-relevant and coherent storyboards. Nevertheless, there is still a large gap compared to human performance suggesting room for promising future work.

Cooperative multi-agent reinforcement learning (c-MARL) is widely applied in safety-critical scenarios, thus the analysis of robustness for c-MARL models is profoundly important. However, robustness certification for c-MARLs has not yet been explored in the community. In this paper, we propose a novel certification method, which is the first work to leverage a scalable approach for c-MARLs to determine actions with guaranteed certified bounds. c-MARL certification poses two key challenges compared with single-agent systems: (i) the accumulated uncertainty as the number of agents increases; (ii) the potential lack of impact when changing the action of a single agent into a global team reward. These challenges prevent us from directly using existing algorithms. Hence, we employ the false discovery rate (FDR) controlling procedure considering the importance of each agent to certify per-state robustness and propose a tree-search-based algorithm to find a lower bound of the global reward under the minimal certified perturbation. As our method is general, it can also be applied in single-agent environments. We empirically show that our certification bounds are much tighter than state-of-the-art RL certification solutions. We also run experiments on two popular c-MARL algorithms: QMIX and VDN, in two different environments, with two and four agents. The experimental results show that our method produces meaningful guaranteed robustness for all models and environments. Our tool CertifyCMARL is available at https://github.com/TrustAI/CertifyCMA

We present Hybrid Infused Reranking for Passages Retrieval (HYRR), a framework for training rerankers based on a hybrid of BM25 and neural retrieval models. Retrievers based on hybrid models have been shown to outperform both BM25 and neural models alone. Our approach exploits this improved performance when training a reranker, leading to a robust reranking model. The reranker, a cross-attention neural model, is shown to be robust to different first-stage retrieval systems, achieving better performance than rerankers simply trained upon the first-stage retrievers in the multi-stage systems. We present evaluations on a supervised passage retrieval task using MS MARCO and zero-shot retrieval tasks using BEIR. The empirical results show strong performance on both evaluations.

Drug-Drug Interactions (DDIs) prediction is an essential issue in the molecular field. Traditional methods of observing DDIs in medical experiments require plenty of resources and labor. In this paper, we present a computational model dubbed MedKGQA based on Graph Neural Networks to automatically predict the DDIs after reading multiple medical documents in the form of multi-hop machine reading comprehension. We introduced a knowledge fusion system to obtain the complete nature of drugs and proteins and exploited a graph reasoning system to infer the drugs and proteins contained in the documents. Our model significantly improves the performance compared to previous state-of-the-art models on the QANGAROO MedHop dataset, which obtained a 4.5% improvement in terms of DDIs prediction accuracy.

Evaluating automatically-generated text summaries is a challenging task. While there have been many interesting approaches, they still fall short of human evaluations. We present RISE, a new approach for evaluating summaries by leveraging techniques from information retrieval. RISE is first trained as a retrieval task using a dual-encoder retrieval setup, and can then be subsequently utilized for evaluating a generated summary given an input document, without gold reference summaries. RISE is especially well suited when working on new datasets where one may not have reference summaries available for evaluation. We conduct comprehensive experiments on the SummEval benchmark (Fabbri et al., 2021) and the results show that RISE has higher correlation with human evaluations compared to many past approaches to summarization evaluation. Furthermore, RISE also demonstrates data-efficiency and generalizability across languages.