Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements. Although there have been many explorations in negotiation dialogue systems, a systematic review of this task has to date remained notably absent. To this end, we aim to fill this gap by reviewing contemporary studies in the emerging field of negotiation dialogue systems, covering benchmarks, evaluations, and methodologies. Furthermore, we also discuss potential future directions, including multi-modal, multi-party, and cross-cultural negotiation scenarios. Our goal is to provide the community with a systematic overview of negotiation dialogue systems and to inspire future research.

Recent advances on text-to-image generation have witnessed the rise of diffusion models which act as powerful generative models. Nevertheless, it is not trivial to exploit such latent variable models to capture the dependency among discrete words and meanwhile pursue complex visual-language alignment in image captioning. In this paper, we break the deeply rooted conventions in learning Transformer-based encoder-decoder, and propose a new diffusion model based paradigm tailored for image captioning, namely Semantic-Conditional Diffusion Networks (SCD-Net). Technically, for each input image, we first search the semantically relevant sentences via cross-modal retrieval model to convey the comprehensive semantic information. The rich semantics are further regarded as semantic prior to trigger the learning of Diffusion Transformer, which produces the output sentence in a diffusion process. In SCD-Net, multiple Diffusion Transformer structures are stacked to progressively strengthen the output sentence with better visional-language alignment and linguistical coherence in a cascaded manner. Furthermore, to stabilize the diffusion process, a new self-critical sequence training strategy is designed to guide the learning of SCD-Net with the knowledge of a standard autoregressive Transformer model. Extensive experiments on COCO dataset demonstrate the promising potential of using diffusion models in the challenging image captioning task. Source code is available at \url{https://github.com/YehLi/xmodaler/tree/master/configs/image_caption/scdnet}.

Learning from changing tasks and sequential experience without forgetting the obtained knowledge is a challenging problem for artificial neural networks. In this work, we focus on two challenging problems in the paradigm of Continual Learning (CL) without involving any old data: (i) the accumulation of catastrophic forgetting caused by the gradually fading knowledge space from which the model learns the previous knowledge; (ii) the uncontrolled tug-of-war dynamics to balance the stability and plasticity during the learning of new tasks. In order to tackle these problems, we present Progressive Learning without Forgetting (PLwF) and a credit assignment regime in the optimizer. PLwF densely introduces model functions from previous tasks to construct a knowledge space such that it contains the most reliable knowledge on each task and the distribution information of different tasks, while credit assignment controls the tug-of-war dynamics by removing gradient conflict through projection. Extensive ablative experiments demonstrate the effectiveness of PLwF and credit assignment. In comparison with other CL methods, we report notably better results even without relying on any raw data.

Responding with multi-modal content has been recognized as an essential capability for an intelligent conversational agent. In this paper, we introduce the MMDialog dataset to better facilitate multi-modal conversation. MMDialog is composed of a curated set of 1.08 million real-world dialogues with 1.53 million unique images across 4,184 topics. MMDialog has two main and unique advantages. First, it is the largest multi-modal conversation dataset by the number of dialogues by 8x. Second, it contains massive topics to generalize the open-domain. To build engaging dialogue system with this dataset, we propose and normalize two response producing tasks based on retrieval and generative scenarios. In addition, we build two baselines for above tasks with state-of-the-art techniques and report their experimental performance. We also propose a novel evaluation metric MM-Relevance to measure the multi-modal responses. Our dataset and scripts are available in https://github.com/victorsungo/MMDialog.

With the advanced request to employ a team of robots to perform a task collaboratively, the research community has become increasingly interested in collaborative simultaneous localization and mapping. Unfortunately, existing datasets are limited in the scale and variation of the collaborative trajectories, even though generalization between inter-trajectories among different agents is crucial to the overall viability of collaborative tasks. To help align the research community's contributions with realistic multiagent ordinated SLAM problems, we propose S3E, a large-scale multimodal dataset captured by a fleet of unmanned ground vehicles along four designed collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor sequences that each exceed 200 seconds, consisting of well temporal synchronized and spatial calibrated high-frequency IMU, high-quality stereo camera, and 360 degree LiDAR data. Crucially, our effort exceeds previous attempts regarding dataset size, scene variability, and complexity. It has 4x as much average recording time as the pioneering EuRoC dataset. We also provide careful dataset analysis as well as baselines for collaborative SLAM and single counterparts. Data and more up-to-date details are found at https://github.com/PengYu-Team/S3E.

开放设定的半监督学习（OSSL）引起了人们日益增长的兴趣，该学习调查了一个更实用的情况，在该情况下，仅在未标记的数据中包含了分布式（OOD）样本。现有的OSSL方法（例如OpenMatch）学习一个OOD检测器以识别离群值，该检测器通常会更新所有模态参数（即完整的微调），以从标记的数据传播类信息到未标记的数据。当前，已经开发了及时的学习来弥合预训练和微调之间的差距，这在几个下游任务中显示出较高的计算效率。在本文中，我们提出了一个迅速驱动的有效OSSL框架，称为OpenPrompt，该框架可以将类别的类信息传播到标记到未标记数据的类信息，只有少数可训练的参数。我们提出了一种迅速驱动的关节空间学习机制来检测OOD数据，通过在未标记的数据中最大化ID和OOD样本之间的分布差距，从而使我们的方法可以以新的方式检测到异常值。三个公共数据集的实验结果表明，OpenPrompt优于不到1％可训练参数的最先进方法。更重要的是，OpenPrompt在CIFAR10上完全监督模型的AUROC检测方面取得了4％的改善。

在移动设备上部署机器学习模型已引起越来越多的关注。为了解决设备上硬件资源的局限性解决模型概括问题，设备模型需要通过诸如云模型的模型压缩等技术轻量级。但是，改善设备模型概括的主要障碍是云数据和设备模型数据之间的分布变化，因为设备模型上的数据分布通常会随着时间而变化（例如，用户在建议系统中可能具有不同的偏好）。尽管实时微调和蒸馏方法考虑到了这种情况，但这些方法需要进行设备训练，由于计算能力较低和设备上缺乏实时标记样品，因此实际上是不可行的。在本文中，我们提出了一个名为Metanetwork的新型任务无关框架，用于从云中生成自适应设备模型参数，而无需进行设备训练。具体而言，我们的元网络部署在云上，由元培养剂和转移器模块组成。 Metagenerator旨在学习从样本到模型参数的映射函数，并且可以根据从设备上传到云的样本生成和传递自适应参数到设备。转移剂旨在减少元烯剂的振荡，加速收敛并在训练和推理过程中提高模型性能。我们使用三个数据集评估了两个任务的方法。广泛的实验表明，元网可以以不同的方式实现竞争性能。

人类对象相互作用（HOI）检测的任务目标是人类与环境相互作用的细粒度视觉解析，从而实现了广泛的应用。先前的工作证明了有效的体系结构设计和相关线索的集成的好处，以进行更准确的HOI检测。但是，现有方法的设计适当的预训练策略的设计仍未得到充实。为了解决这一差距，我们提出了关系语言图像预训练（RLIP），这是一种利用实体和关系描述的对比预训练的策略。为了有效利用此类预训练，我们做出了三个技术贡献：（1）一种新的并行实体检测和顺序关系推理（Parse）体系结构，可在整体优化的预训练期间使用实体和关系描述； （2）合成数据生成框架，标签序列扩展，扩展了每个Minibatch中可用的语言数据的规模； （3）解释歧义，关系质量标签和关系伪标签的机制，以减轻训练数据中模棱两可/嘈杂样本的影响。通过广泛的实验，我们证明了这些贡献的好处，共同称为rlip-parse，以改善零射击，很少射击和微调的HOI检测性能以及从噪音注释中学习的鲁棒性。代码将在\ url {https://github.com/jacobyuan7/rlip}上找到。

视觉表示学习是解决各种视力问题的关键。依靠开创性的网格结构先验，卷积神经网络（CNN）已成为大多数深视觉模型的事实上的标准架构。例如，经典的语义分割方法通常采用带有编码器编码器体系结构的完全横向卷积网络（FCN）。编码器逐渐减少了空间分辨率，并通过更大的接受场来学习更多抽象的视觉概念。由于上下文建模对于分割至关重要，因此最新的努力一直集中在通过扩张（即极度）卷积或插入注意力模块来增加接受场。但是，基于FCN的体系结构保持不变。在本文中，我们旨在通过将视觉表示学习作为序列到序列预测任务来提供替代观点。具体而言，我们部署纯变压器以将图像编码为一系列贴片，而无需局部卷积和分辨率减少。通过在变压器的每一层中建立的全球环境，可以学习更强大的视觉表示形式，以更好地解决视力任务。特别是，我们的细分模型（称为分割变压器（SETR））在ADE20K上擅长（50.28％MIOU，这是提交当天测试排行榜中的第一个位置），Pascal环境（55.83％MIOU），并在CityScapes上达到竞争成果。此外，我们制定了一个分层局部全球（HLG）变压器的家族，其特征是窗户内的本地关注和跨窗户的全球性专注于层次结构和金字塔架构。广泛的实验表明，我们的方法在各种视觉识别任务（例如，图像分类，对象检测和实例分割和语义分割）上实现了吸引力的性能。

联合学习（FL）是一种从分散数据源训练机器学习模型的技术。我们根据当地的隐私约束概念研究FL，该概念通过在离开客户之前使数据混淆，为敏感数据披露提供了强烈的保护。我们确定了设计实用隐私的FL算法的两个主要问题：沟通效率和高维度的兼容性。然后，我们开发一种基于梯度的学习算法，称为\ emph {sqsgd}（选择性量化的随机梯度下降），以解决这两个问题。所提出的算法基于一种新颖的隐私量化方案，该方案使用每个客户每个维度的恒定位数。然后，我们通过三种方式改进基本算法：首先，我们采用梯度亚采样策略，同时在固定隐私预算下提供更好的培训性能和较小的沟通成本。其次，我们利用随机旋转作为预处理步骤来减少量化误差。第三，采用了自适应梯度标准上限策略来提高准确性和稳定训练。最后，在基准数据集中证明了拟议框架的实用性。实验结果表明，SQSGD成功地学习了Lenet和Resnet等局部隐私约束的大型模型。此外，凭借固定的隐私和通信水平，SQSGD的性能显着主导了各种基线算法。