Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have been proposed recently to identify those changes, they still suffer from missing subtle changes, poor scalability, or/and sensitive to noise points. To meet these challenges, we are the first to generalise the CPD problem as a special case of the Change-Interval Detection (CID) problem. Then we propose a CID method, named iCID, based on a recent Isolation Distributional Kernel (IDK). iCID identifies the change interval if there is a high dissimilarity score between two non-homogeneous temporal adjacent intervals. The data-dependent property and finite feature map of IDK enabled iCID to efficiently identify various types of change points in data streams with the tolerance of noise points. Moreover, the proposed online and offline versions of iCID have the ability to optimise key parameter settings. The effectiveness and efficiency of iCID have been systematically verified on both synthetic and real-world datasets.

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.

Is it possible to leverage large scale raw and raw parallel corpora to build a general learned metric? Existing learned metrics have gaps to human judgements, are model-dependent or are limited to the domains or tasks where human ratings are available. In this paper, we propose SEScore2, a model-based metric pretrained over million-scale synthetic dataset constructed by our novel retrieval augmented data synthesis pipeline. SEScore2 achieves high correlation to human judgements without any human rating supervisions. Importantly, our unsupervised SEScore2 can outperform supervised metrics, which are trained on the News human ratings, at the TED domain. We evaluate SEScore2 over four text generation tasks across three languages. SEScore2 outperforms all prior unsupervised evaluation metrics in machine translation, speech translation, data-to-text and dialogue generation, with average Kendall improvements 0.158. SEScore2 even outperforms SOTA supervised BLEURT at data-to-text, dialogue generation and overall correlation.

Learning continuous image representations is recently gaining popularity for image super-resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary scales from low-resolution inputs. Existing methods mostly ensemble nearby features to predict the new pixel at any queried coordinate in the SR image. Such a local ensemble suffers from some limitations: i) it has no learnable parameters and it neglects the similarity of the visual features; ii) it has a limited receptive field and cannot ensemble relevant features in a large field which are important in an image; iii) it inherently has a gap with real camera imaging since it only depends on the coordinate. To address these issues, this paper proposes a continuous implicit attention-in-attention network, called CiaoSR. We explicitly design an implicit attention network to learn the ensemble weights for the nearby local features. Furthermore, we embed a scale-aware attention in this implicit attention network to exploit additional non-local information. Extensive experiments on benchmark datasets demonstrate CiaoSR significantly outperforms the existing single image super resolution (SISR) methods with the same backbone. In addition, the proposed method also achieves the state-of-the-art performance on the arbitrary-scale SR task. The effectiveness of the method is also demonstrated on the real-world SR setting. More importantly, CiaoSR can be flexibly integrated into any backbone to improve the SR performance.

Video captioning aims to generate natural language sentences that describe the given video accurately. Existing methods obtain favorable generation by exploring richer visual representations in encode phase or improving the decoding ability. However, the long-tailed problem hinders these attempts at low-frequency tokens, which rarely occur but carry critical semantics, playing a vital role in the detailed generation. In this paper, we introduce a novel Refined Semantic enhancement method towards Frequency Diffusion (RSFD), a captioning model that constantly perceives the linguistic representation of the infrequent tokens. Concretely, a Frequency-Aware Diffusion (FAD) module is proposed to comprehend the semantics of low-frequency tokens to break through generation limitations. In this way, the caption is refined by promoting the absorption of tokens with insufficient occurrence. Based on FAD, we design a Divergent Semantic Supervisor (DSS) module to compensate for the information loss of high-frequency tokens brought by the diffusion process, where the semantics of low-frequency tokens is further emphasized to alleviate the long-tailed problem. Extensive experiments indicate that RSFD outperforms the state-of-the-art methods on two benchmark datasets, i.e., MSR-VTT and MSVD, demonstrate that the enhancement of low-frequency tokens semantics can obtain a competitive generation effect. Code is available at https://github.com/lzp870/RSFD.

语义本地化（SELO）是指使用语义信息（例如文本）在大规模遥感（RS）图像中获得最相关位置的任务。作为基于跨模式检索的新兴任务，Selo仅使用字幕级注释来实现语义级检索，这表明了其在统一下游任务方面的巨大潜力。尽管Selo已连续执行，但目前没有系统地探索并分析了这一紧急方向。在本文中，我们彻底研究了这一领域，并根据指标和测试数据提供了完整的基准，以推进SELO任务。首先，基于此任务的特征，我们提出了多个判别评估指标来量化SELO任务的性能。设计的显着面积比例，注意力转移距离和离散的注意距离可用于评估从像素级别和区域级别中产生的SELO图。接下来，为了为SELO任务提供标准评估数据，我们为多样化的，多语义的，多目标语义定位测试集（AIR-SLT）贡献。 AIR-SLT由22个大型RS图像和59个具有不同语义的测试用例组成，旨在为检索模型提供全面的评估。最后，我们详细分析了RS跨模式检索模型的SELO性能，探索不同变量对此任务的影响，并为SELO任务提供了完整的基准测试。我们还建立了一个新的范式来引用RS表达理解，并通过将其与检测和道路提取等任务相结合，证明了Selo在语义中的巨大优势。拟议的评估指标，语义本地化测试集和相应的脚本已在github.com/xiaoyuan1996/semanticlocalizationmetrics上访问。

时间一致性是视频深度估计的主要挑战。以前的作品基于额外的光流或相机姿势，这是耗时的。相比之下，我们获得了较少信息的一致性。由于固有的视频存在着沉重的时间冗余，因此可以从附近的框架中恢复缺失的框架。受此启发的启发，我们提出了框架屏蔽网络（FMNET），这是一种空间 - 速度变压器网络，可根据其相邻框架预测蒙版框架的深度。通过重建掩盖的时间特征，FMNET可以学习固有的框架间相关性，从而导致一致性。与先前的艺术相比，实验结果表明，我们的方法可以达到可比的空间准确性和更高的时间一致性，而没有任何其他信息。我们的工作为一致的视频深度估计提供了新的视角。

神经隐式表示在新的视图合成和来自多视图图像的高质量3D重建方面显示了其有效性。但是，大多数方法都集中在整体场景表示上，但忽略了其中的各个对象，从而限制了潜在的下游应用程序。为了学习对象组合表示形式，一些作品将2D语义图作为训练中的提示，以掌握对象之间的差异。但是他们忽略了对象几何和实例语义信息之间的牢固联系，这导致了单个实例的不准确建模。本文提出了一个新颖的框架ObjectsDF，以在3D重建和对象表示中构建具有高保真度的对象复合神经隐式表示。观察常规音量渲染管道的歧义，我们通过组合单个对象的签名距离函数（SDF）来对场景进行建模，以发挥明确的表面约束。区分不同实例的关键是重新审视单个对象的SDF和语义标签之间的牢固关联。特别是，我们将语义信息转换为对象SDF的函数，并为场景和对象开发统一而紧凑的表示形式。实验结果表明，ObjectSDF框架在表示整体对象组合场景和各个实例方面的优越性。可以在https://qianyiwu.github.io/objectsdf/上找到代码

痤疮检测对于解释性诊断和对皮肤疾病的精确治疗至关重要。任意边界和痤疮病变的尺寸较小，导致在两阶段检测中大量质量较差的建议。在本文中，我们提出了一个针对地区建议网络的新型头部结构，以两种方式提高建议的质量。首先，提出了一个空间意识的双头（SADH）结构，以从两个不同的空间角度从分类和本地化进行分类和本地化的表示。拟议的SADH确保了更陡峭的分类信心梯度，并抑制了与匹配的地面真理相交（IOU）低相交（IOU）的建议。然后，我们提出了一个归一化的Wasserstein距离预测分支，以改善提议分类评分与IOU之间的相关性。此外，为了促进痤疮检测的进一步研究，我们构建了一个名为Acnescu的新数据集，具有高分辨率成像，精确的注释和细粒度的病变类别。对AcnesCU和公共数据集Acne04进行了广泛的实验，结果表明该方法可以提高建议的质量，始终超过最先进的方法。代码和收集的数据集可在https://github.com/pingguokiller/acnedetection中找到。

在机器人操作中，以前未见的新物体的自主抓住是一个持续的挑战。在过去的几十年中，已经提出了许多方法来解决特定机器人手的问题。最近引入的Unigrasp框架具有推广到不同类型的机器人抓手的能力。但是，此方法不适用于具有闭环约束的抓手，并且当应用于具有MultiGRASP配置的机器人手时，具有数据范围。在本文中，我们提出了有效绘制的，这是一种独立于抓手模型规范的广义掌握合成和抓地力控制方法。有效绘制利用抓地力工作空间功能，而不是Unigrasp的抓属属性输入。这在训练过程中将记忆使用量减少了81.7％，并可以推广到更多类型的抓地力，例如具有闭环约束的抓手。通过在仿真和现实世界中进行对象抓住实验来评估有效绘制的有效性；结果表明，所提出的方法在仅考虑没有闭环约束的抓手时也胜过Unigrasp。在这些情况下，有效抓取在产生接触点的精度高9.85％，模拟中的握把成功率提高了3.10％。现实世界实验是用带有闭环约束的抓地力进行的，而Unigrasp无法处理，而有效绘制的成功率达到了83.3％。分析了该方法的抓地力故障的主要原因，突出了增强掌握性能的方法。