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.

在过去的几年中，多方计算（MPC）作为安全计算模型一直在越来越受欢迎，尤其是对于机器学习（ML）推断。与竞争对手相比，MPC的开销少于同构加密（HE），并且比基于硬件的可信执行环境（TEE）（例如Intel SGX）具有更强的威胁模型。尽管具有明显的优势，但在应用于ML算法时，MPC协议仍然与针对性相比，仍要支付大量的绩效罚款。开销是由于增加的计算和通信成本。对于在ML算法中无处不在的乘法，MPC协议在MPC服务器之间增加了32x更多的计算成本和1轮广播。此外，由于SoftMax，Relu和其他非线性操作，其具有微不足道的成本的ML计算由于增加了沟通而变得非常昂贵。这些添加的开销使MPC不太适合在实时ML推理框架（例如语音翻译）中部署。在这项工作中，我们提出了MPC-Pipe，这是一种使用两种ML特异性方法的MPC管道推理技术。 1）内线间管道和2）内层管道。这两种技术缩短了机器学习模型的总推理运行时。与当前的MPC协议实现相比，当模型权重公开时，我们的实验已显示可将ML推断潜伏期降低多达12.6％，而在模型权重公开时，将ML推断潜伏期最高12.6％。

随着机器学习到达不同的应用领域，与隐私和安全有关的问题正在越来越大。数据持有人希望在利用云中托管的加速器（例如GPU）的同时训练或推断私人数据。云系统容易受到损害数据隐私和计算完整性的攻击者的影响。应对这样的挑战需要将理论隐私算法统一使用硬件安全功能。本文介绍了Darknight，这是一个大型DNN培训的框架，同时保护输入隐私和计算完整性。 Darknight依赖于受信任的执行环境（TEE）和加速器之间的合作执行，其中TEE提供了隐私和完整性验证，而加速器则执行大部分线性代数计算以优化性能。特别是，Darknight使用基于矩阵掩码的自定义数据编码策略来在TEE中创建输入混淆。然后将混淆的数据卸载到GPU，以进行快速线性代数计算。 Darknight的数据混淆策略在云服务器中提供了可证明的数据隐私和计算完整性。虽然先前的作品应对推理隐私，并且不能用于培训，但Darknight的编码方案旨在支持培训和推理。

For best performance, today's semantic segmentation methods use large and carefully labeled datasets, requiring expensive annotation budgets. In this work, we show that coarse annotation is a low-cost but highly effective alternative for training semantic segmentation models. Considering the urban scene segmentation scenario, we leverage cheap coarse annotations for real-world captured data, as well as synthetic data to train our model and show competitive performance compared with finely annotated real-world data. Specifically, we propose a coarse-to-fine self-training framework that generates pseudo labels for unlabeled regions of the coarsely annotated data, using synthetic data to improve predictions around the boundaries between semantic classes, and using cross-domain data augmentation to increase diversity. Our extensive experimental results on Cityscapes and BDD100k datasets demonstrate that our method achieves a significantly better performance vs annotation cost tradeoff, yielding a comparable performance to fully annotated data with only a small fraction of the annotation budget. Also, when used as pretraining, our framework performs better compared to the standard fully supervised setting.

Recent works have shown that unstructured text (documents) from online sources can serve as useful auxiliary information for zero-shot image classification. However, these methods require access to a high-quality source like Wikipedia and are limited to a single source of information. Large Language Models (LLM) trained on web-scale text show impressive abilities to repurpose their learned knowledge for a multitude of tasks. In this work, we provide a novel perspective on using an LLM to provide text supervision for a zero-shot image classification model. The LLM is provided with a few text descriptions from different annotators as examples. The LLM is conditioned on these examples to generate multiple text descriptions for each class(referred to as views). Our proposed model, I2MVFormer, learns multi-view semantic embeddings for zero-shot image classification with these class views. We show that each text view of a class provides complementary information allowing a model to learn a highly discriminative class embedding. Moreover, we show that I2MVFormer is better at consuming the multi-view text supervision from LLM compared to baseline models. I2MVFormer establishes a new state-of-the-art on three public benchmark datasets for zero-shot image classification with unsupervised semantic embeddings.

尽管在零射门学习（ZSL）方面取得了巨大进展，但大多数现有方法仍然依赖于人类通知的属性，这些属性很难注释和扩展。一个无监督的替代方法是使用与其语义类名称相关的单词嵌入来表示每个类。但是，从预训练的语言模型中提取的单词嵌入不一定会捕获视觉相似性，从而导致零拍的性能差。在这项工作中，我们认为在线文本文档，例如Wikipedia，包含有关对象类的丰富视觉描述，因此可以用作ZSL的强大无监督的侧面信息。为此，我们提出了I2Dformer，这是一种基于变压器的新型ZSL框架，共同学会通过在共享嵌入空间中对齐两个方式来编码图像和文档。为了从嘈杂的文档中提取歧视性的视觉单词，我们介绍了一个新的跨模式注意模块，该模块可以学习图像补丁和文档单词之间的细粒度相互作用。因此，我们的i2dformer不仅学习了捕获视觉相似性的高度歧视文档的嵌入，而且还获得了将视觉相关单词定位在图像区域中的能力。定量地，我们证明我们的i2形式在三个公共数据集上的零照片和广义零局学习设置下都显着优于先前无监督的语义嵌入。定性地，我们表明我们的方法会导致高度可解释的结果，其中文档单词可以基于图像区域。

零件代表不同对象的几何和语义相似性的基本单位。我们争辩说，部分知识应与观察到的对象课程中有款组合。对此，我们将3D组成零射击学习作为从看作识的零件泛化的问题，从而看成了语义分割。我们通过将任务与所提出的组成部分数据集进行基准测试，提供结构化研究。该数据集是通过处理原始PartNet来创建的，以最大化不同对象的部分重叠。现有点云部分段方法未能在此设置中概括到未遵守的对象类。作为解决方案，我们提出了分解共识，其将零件分割网络与部分评分网络相结合。我们方法的关键直觉是某些部件的分割掩码应该具有与其部分分数分开的零件分数的共识。在生成最合适的分割掩模之前在每个对象部分中定义的不同部分组合的两个网络原因。我们展示了我们的方法允许组成零射分段和广义零拍分类，并在两个任务中建立最先进的状态。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.