Blind watermarking provides powerful evidence for copyright protection, image authentication, and tampering identification. However, it remains a challenge to design a watermarking model with high imperceptibility and robustness against strong noise attacks. To resolve this issue, we present a framework Combining the Invertible and Non-invertible (CIN) mechanisms. The CIN is composed of the invertible part to achieve high imperceptibility and the non-invertible part to strengthen the robustness against strong noise attacks. For the invertible part, we develop a diffusion and extraction module (DEM) and a fusion and split module (FSM) to embed and extract watermarks symmetrically in an invertible way. For the non-invertible part, we introduce a non-invertible attention-based module (NIAM) and the noise-specific selection module (NSM) to solve the asymmetric extraction under a strong noise attack. Extensive experiments demonstrate that our framework outperforms the current state-of-the-art methods of imperceptibility and robustness significantly. Our framework can achieve an average of 99.99% accuracy and 67.66 dB PSNR under noise-free conditions, while 96.64% and 39.28 dB combined strong noise attacks. The code will be available in https://github.com/rmpku/CIN.

Embedding tables are usually huge in click-through rate (CTR) prediction models. To train and deploy the CTR models efficiently and economically, it is necessary to compress their embedding tables at the training stage. To this end, we formulate a novel quantization training paradigm to compress the embeddings from the training stage, termed low-precision training (LPT). Also, we provide theoretical analysis on its convergence. The results show that stochastic weight quantization has a faster convergence rate and a smaller convergence error than deterministic weight quantization in LPT. Further, to reduce the accuracy degradation, we propose adaptive low-precision training (ALPT) that learns the step size (i.e., the quantization resolution) through gradient descent. Experiments on two real-world datasets confirm our analysis and show that ALPT can significantly improve the prediction accuracy, especially at extremely low bit widths. For the first time in CTR models, we successfully train 8-bit embeddings without sacrificing prediction accuracy. The code of ALPT is publicly available.

Masked Language Modeling (MLM) has proven to be an essential component of Vision-Language (VL) pretraining. To implement MLM, the researcher must make two design choices: the masking strategy, which determines which tokens to mask, and the masking rate, which determines how many tokens to mask. Previous work has focused primarily on the masking strategy while setting the masking rate at a default of 15\%. In this paper, we show that increasing this masking rate improves downstream performance while simultaneously reducing performance gap among different masking strategies, rendering the uniform masking strategy competitive to other more complex ones. Surprisingly, we also discover that increasing the masking rate leads to gains in Image-Text Matching (ITM) tasks, suggesting that the role of MLM goes beyond language modeling in VL pretraining.

This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading. Specifcally, we train two segmentation networks to first extract the lung region and then the inner lesion region. The lesion segmentation mask serves as complementary information for the original CT slices. To relieve the issue of imbalanced data distribution, we further improve the advanced Contrastive Mixup Classification network by weighted cross-entropy loss. On the COVID-19 severity detection leaderboard, our approach won the first place with a Macro F1 Score of 51.76%. It significantly outperforms the baseline method by over 11.46%.

Automatic diabetic retinopathy (DR) grading based on fundus photography has been widely explored to benefit the routine screening and early treatment. Existing researches generally focus on single-field fundus images, which have limited field of view for precise eye examinations. In clinical applications, ophthalmologists adopt two-field fundus photography as the dominating tool, where the information from each field (i.e.,macula-centric and optic disc-centric) is highly correlated and complementary, and benefits comprehensive decisions. However, automatic DR grading based on two-field fundus photography remains a challenging task due to the lack of publicly available datasets and effective fusion strategies. In this work, we first construct a new benchmark dataset (DRTiD) for DR grading, consisting of 3,100 two-field fundus images. To the best of our knowledge, it is the largest public DR dataset with diverse and high-quality two-field images. Then, we propose a novel DR grading approach, namely Cross-Field Transformer (CrossFiT), to capture the correspondence between two fields as well as the long-range spatial correlations within each field. Considering the inherent two-field geometric constraints, we particularly define aligned position embeddings to preserve relative consistent position in fundus. Besides, we perform masked cross-field attention during interaction to flter the noisy relations between fields. Extensive experiments on our DRTiD dataset and a public DeepDRiD dataset demonstrate the effectiveness of our CrossFiT network. The new dataset and the source code of CrossFiT will be publicly available at https://github.com/FDU-VTS/DRTiD.

本文介绍了我们针对第二届COVID-19比赛的解决方案，该竞赛是在欧洲计算机视觉会议（ECCV 2022）的Aimia研讨会框架内举行的。在我们的方法中，我们采用有效的3D对比度混合分类网络，用于在胸部CT图像上进行COVID-19诊断，该图像由对比度表示学习和混合分类组成。对于COVID-19检测挑战，我们的方法在484验证CT扫描中达到0.9245宏F1得分，这显着优于基线方法的16.5％。在COVID-19的严重性检测挑战中，我们的方法在61个验证样本上达到0.7186宏F1得分，这也超过了基线8.86％。

已经发现，旨在在未修剪视频的开始和终点范围内发现的时间动作实例的时间动作提案生成可以在很大程度上受益于适当的时间和语义上下文的剥削。最新的努力致力于通过自我发项模块来考虑基于时间的环境和基于相似性的语义上下文。但是，他们仍然遭受混乱的背景信息和有限的上下文特征学习的困扰。在本文中，我们提出了一个基于金字塔区域的新型插槽注意（PRSLOT）模块来解决这些问题。我们的PRSLOT模块不使用相似性计算，而是直接以编码器方式来学习本地关系，并基于注意力输入功能（称为\ textit {slot}}的注意力输入功能，生成了局部区域的表示。具体而言，在输入段级级别上，PRSLOT模块将目标段作为\ textIt {query}，其周围区域为\ textit {key}，然后通过聚集每个\ textit {query-key}插槽来生成插槽表示。具有平行金字塔策略的本地摘要上下文。基于PRSLOT模块，我们提出了一种基于金字塔区域的新型插槽注意网络，称为PRSA-NET，以学习具有丰富的时间和语义上下文的统一视觉表示，以获得更好的建议生成。广泛的实验是在两个广泛采用的Thumos14和ActivityNet-1.3基准上进行的。我们的PRSA-NET优于其他最先进的方法。特别是，我们将AR@100从以前的最佳50.67％提高到56.12％，以生成提案，并在0.5 TIOU下将地图从51.9 \％\％提高到58.7 \％\％\％\％\％，以在Thumos14上进行动作检测。 \ textit {代码可在} \ url {https://github.com/handhand123/prsa-net}中获得

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

最近的参数效率语言模型调整（PELT）方法可以使微调的性能与较少的可训练参数相匹配，并且在训练数据受到限制时尤其表现良好。但是，不同的PELT方法在相同的任务上的性能可能会有所不同，因此为特定任务选择最合适的方法是不平凡的，尤其是考虑到快速增长的新PELT方法和任务。鉴于模型多样性和模型选择的难度，我们提出了一个统一的框架Unipelt，该框架将不同的毛皮方法纳入了子模型，并学会了激活最适合当前数据或通过门控机制设置的方法。在胶水基准上，与最佳的单个毛皮方法相比，UniPelt始终达到1〜4％的增长，而其融合甚至超过了不同设置下的微调。此外，UniPelt通常超过上限，该上限在每个任务上单独使用的所有子模型的最佳性能，表明多种PELT方法的混合物可能本质上比单个方法更有效。

视频人群本地化是一项至关重要但又具有挑战性的任务，旨在估算给定拥挤视频中人头的确切位置。为了模拟人类活动性的时空依赖性，我们提出了多焦点高斯邻里注意力（GNA），可以有效利用远程对应关系，同时保持输入视频的空间拓扑结构。特别是，我们的GNA还可以使用配备的多聚焦机制良好地捕获人头的尺度变化。基于多聚焦GNA，我们开发了一个名为GNANET的统一神经网络，以通过场景建模模块和上下文交叉意见模块充分聚合时空信息来准确地定位视频片段中的头部中心。此外，为了促进该领域的未来研究，我们介绍了一个名为VScrowd的大规模人群视频基准，该视频由60k+框架组成，这些框架在各种监视场景和2M+头部注释中捕获。最后，我们在包括我们的SenseCrowd在内的三个数据集上进行了广泛的实验，实验结果表明，所提出的方法能够实现视频人群本地化和计数的最新性能。