多个实例学习（MIL）是对诊断病理学的整个幻灯片图像（WSI）进行分类的强大方法。 MIL对WSI分类的基本挑战是发现触发袋子标签的\ textit {critical Instances}。但是，先前的方法主要是在独立和相同的分布假设（\ textit {i.i.d}）下设计的，忽略了肿瘤实例或异质性之间的相关性。在本文中，我们提出了一种新颖的基于多重检测的多重实例学习（MDMIL）来解决上述问题。具体而言，MDMIL是由内部查询产生模块（IQGM）和多重检测模块（MDM）构建的，并在训练过程中基于内存的对比度损失的辅助。首先，IQGM给出了实例的概率，并通过在分布分析后汇总高度可靠的功能来为后续MDM生成内部查询（IQ）。其次，在MDM中，多重检测交叉注意（MDCA）和多头自我注意力（MHSA）合作以生成WSI的最终表示形式。在此过程中，智商和可训练的变异查询（VQ）成功建立了实例之间的联系，并显着提高了模型对异质肿瘤的鲁棒性。最后，为了进一步在特征空间中实施限制并稳定训练过程，我们采用基于内存的对比损失，即使在每次迭代中有一个样本作为输入，也可以实现WSI分类。我们对三个计算病理数据集进行实验，例如CamelyOn16，TCGA-NSCLC和TCGA-RCC数据集。优越的准确性和AUC证明了我们提出的MDMIL比其他最先进方法的优越性。

盐和辣椒噪声去除是图像处理中的常见问题。传统的去噪方法有两个限制。首先，通常未准确描述噪声特性。例如，噪声位置信息通常被忽略，并且盐和辣椒噪声的稀疏性通常由L1标准描述，这不能清楚地示出稀疏变量。其次，传统方法将污染图像分离成恢复的图像和噪声部分，从而导致恢复具有不满足平滑部件和细节部件的图像。在本研究中，我们引入了噪声检测策略来确定噪声的位置，并且采用由LP准规范描绘的非凸稀稀曲面正规化来描述噪声的稀疏性，从而解决了第一个限制。采用静止框架变换的形态分析框架将处理的图像分解为卡通，纹理和噪声部件以解决第二个限制。然后，采用乘法器（ADMM）的交替方向方法来解决所提出的模型。最后，进行实验以验证所提出的方法，并将其与一些最新的最先进的去噪方法进行比较。实验结果表明，该方法可以在保留加工图像的细节时去除盐和辣椒噪声。

视觉问题的视觉关注在视觉问题上应答（VQA）目标在定位有关答案预测的右图像区域，提供强大的技术来促进多模态理解。然而，最近的研究指出，来自视觉关注的突出显示的图像区域通常与给定的问题和答案无关，导致模型混淆正确的视觉推理。为了解决这个问题，现有方法主要是为了对准人类关注的视觉注意力。尽管如此，收集这种人类数据是费力且昂贵的，使其在数据集中调整良好开发的模型。为了解决这个问题，在本文中，我们设计了一种新的视觉关注正规化方法，即attreg，以便在VQA中更好地视觉接地。具体而言，attraT首先识别了由骨干模型出乎意料地忽略（即，分配低注意重量）的问题所必需的图像区域。然后，利用掩模引导的学习方案来规范视觉注意力，以便更多地关注这些忽略的关键区域。所提出的方法是非常灵活的，模型不可知，可以集成到基于大多数基于视觉关注的VQA模型中，并且不需要人类注意监督。已经进行了三个基准数据集，即VQA-CP V2，VQA-CP V1和VQA V2的广泛实验，以评估attreg的有效性。作为副产品，将Attreg纳入强基线LMH时，我们的方法可以实现新的最先进的准确性为60.00％，在VQA-CP V2基准数据集上绝对性能增益为7.01％。 。

Stance detection models may tend to rely on dataset bias in the text part as a shortcut and thus fail to sufficiently learn the interaction between the targets and texts. Recent debiasing methods usually treated features learned by small models or big models at earlier steps as bias features and proposed to exclude the branch learning those bias features during inference. However, most of these methods fail to disentangle the ``good'' stance features and ``bad'' bias features in the text part. In this paper, we investigate how to mitigate dataset bias in stance detection. Motivated by causal effects, we leverage a novel counterfactual inference framework, which enables us to capture the dataset bias in the text part as the direct causal effect of the text on stances and reduce the dataset bias in the text part by subtracting the direct text effect from the total causal effect. We novelly model bias features as features that correlate with the stance labels but fail on intermediate stance reasoning subtasks and propose an adversarial bias learning module to model the bias more accurately. To verify whether our model could better model the interaction between texts and targets, we test our model on recently proposed test sets to evaluate the understanding of the task from various aspects. Experiments demonstrate that our proposed method (1) could better model the bias features, and (2) outperforms existing debiasing baselines on both the original dataset and most of the newly constructed test sets.

Text-based speech editing allows users to edit speech by intuitively cutting, copying, and pasting text to speed up the process of editing speech. In the previous work, CampNet (context-aware mask prediction network) is proposed to realize text-based speech editing, significantly improving the quality of edited speech. This paper aims at a new task: adding emotional effect to the editing speech during the text-based speech editing to make the generated speech more expressive. To achieve this task, we propose Emo-CampNet (emotion CampNet), which can provide the option of emotional attributes for the generated speech in text-based speech editing and has the one-shot ability to edit unseen speakers' speech. Firstly, we propose an end-to-end emotion-selectable text-based speech editing model. The key idea of the model is to control the emotion of generated speech by introducing additional emotion attributes based on the context-aware mask prediction network. Secondly, to prevent the emotion of the generated speech from being interfered by the emotional components in the original speech, a neutral content generator is proposed to remove the emotion from the original speech, which is optimized by the generative adversarial framework. Thirdly, two data augmentation methods are proposed to enrich the emotional and pronunciation information in the training set, which can enable the model to edit the unseen speaker's speech. The experimental results that 1) Emo-CampNet can effectively control the emotion of the generated speech in the process of text-based speech editing; And can edit unseen speakers' speech. 2) Detailed ablation experiments further prove the effectiveness of emotional selectivity and data augmentation methods. The demo page is available at https://hairuo55.github.io/Emo-CampNet/

Large-scale cross-modal pre-training paradigms have recently shown ubiquitous success on a wide range of downstream tasks, e.g., zero-shot classification, retrieval and image captioning. However, their successes highly rely on the scale and quality of web-crawled data that naturally contain incomplete and noisy information (e.g., wrong or irrelevant content). Existing works either design manual rules to clean data or generate pseudo-targets as auxiliary signals for reducing noise impact, which do not explicitly tackle both the incorrect and incomplete challenges simultaneously. In this paper, to automatically mitigate the impact of noise by solely mining over existing data, we propose a principled Noise-robust Language-Image Pre-training framework (NLIP) to stabilize pre-training via two schemes: noise-harmonization and noise-completion. First, in noise-harmonization scheme, NLIP estimates the noise probability of each pair according to the memorization effect of cross-modal transformers, then adopts noise-adaptive regularization to harmonize the cross-modal alignments with varying degrees. Second, in noise-completion scheme, to enrich the missing object information of text, NLIP injects a concept-conditioned cross-modal decoder to obtain semantic-consistent synthetic captions to complete noisy ones, which uses the retrieved visual concepts (i.e., objects' names) for the corresponding image to guide captioning generation. By collaboratively optimizing noise-harmonization and noise-completion schemes, our NLIP can alleviate the common noise effects during image-text pre-training in a more efficient way. Extensive experiments show the significant performance improvements of our NLIP using only 26M data over existing pre-trained models (e.g., CLIP, FILIP and BLIP) on 12 zero-shot classification datasets, MSCOCO image captioning and zero-shot image-text retrieval tasks.

Text Summarization is recognised as one of the NLP downstream tasks and it has been extensively investigated in recent years. It can assist people with perceiving the information rapidly from the Internet, including news articles, social posts, videos, etc. Most existing research works attempt to develop summarization models to produce a better output. However, advent limitations of most existing models emerge, including unfaithfulness and factual errors. In this paper, we propose a novel model, named as Knowledge-aware Abstractive Text Summarization, which leverages the advantages offered by Knowledge Graph to enhance the standard Seq2Seq model. On top of that, the Knowledge Graph triplets are extracted from the source text and utilised to provide keywords with relational information, producing coherent and factually errorless summaries. We conduct extensive experiments by using real-world data sets. The results reveal that the proposed framework can effectively utilise the information from Knowledge Graph and significantly reduce the factual errors in the summary.

Dunhuang murals are a collection of Chinese style and national style, forming a self-contained Chinese-style Buddhist art. It has very high historical and cultural value and research significance. Among them, the lines of Dunhuang murals are highly general and expressive. It reflects the character's distinctive character and complex inner emotions. Therefore, the outline drawing of murals is of great significance to the research of Dunhuang Culture. The contour generation of Dunhuang murals belongs to image edge detection, which is an important branch of computer vision, aims to extract salient contour information in images. Although convolution-based deep learning networks have achieved good results in image edge extraction by exploring the contextual and semantic features of images. However, with the enlargement of the receptive field, some local detail information is lost. This makes it impossible for them to generate reasonable outline drawings of murals. In this paper, we propose a novel edge detector based on self-attention combined with convolution to generate line drawings of Dunhuang murals. Compared with existing edge detection methods, firstly, a new residual self-attention and convolution mixed module (Ramix) is proposed to fuse local and global features in feature maps. Secondly, a novel densely connected backbone extraction network is designed to efficiently propagate rich edge feature information from shallow layers into deep layers. Compared with existing methods, it is shown on different public datasets that our method is able to generate sharper and richer edge maps. In addition, testing on the Dunhuang mural dataset shows that our method can achieve very competitive performance.

Previous databases have been designed to further the development of fake audio detection. However, fake utterances are mostly generated by altering timbre, prosody, linguistic content or channel noise of original audios. They ignore a fake situation, in which the attacker manipulates an acoustic scene of the original audio with another forgery one. It will pose a major threat to our society if some people misuse the manipulated audio with malicious purpose. Therefore, this motivates us to fill in the gap. This paper designs such a dataset for scene fake audio detection (SceneFake). A manipulated audio in the SceneFake dataset involves only tampering the acoustic scene of an utterance by using speech enhancement technologies. We can not only detect fake utterances on a seen test set but also evaluate the generalization of fake detection models to unseen manipulation attacks. Some benchmark results are described on the SceneFake dataset. Besides, an analysis of fake attacks with different speech enhancement technologies and signal-to-noise ratios are presented on the dataset. The results show that scene manipulated utterances can not be detected reliably by the existing baseline models of ASVspoof 2019. Furthermore, the detection of unseen scene manipulation audio is still challenging.

Partial label learning (PLL) is a typical weakly supervised learning, where each sample is associated with a set of candidate labels. The basic assumption of PLL is that the ground-truth label must reside in the candidate set. However, this assumption may not be satisfied due to the unprofessional judgment of the annotators, thus limiting the practical application of PLL. In this paper, we relax this assumption and focus on a more general problem, noisy PLL, where the ground-truth label may not exist in the candidate set. To address this challenging problem, we further propose a novel framework called "Automatic Refinement Network (ARNet)". Our method consists of multiple rounds. In each round, we purify the noisy samples through two key modules, i.e., noisy sample detection and label correction. To guarantee the performance of these modules, we start with warm-up training and automatically select the appropriate correction epoch. Meanwhile, we exploit data augmentation to further reduce prediction errors in ARNet. Through theoretical analysis, we prove that our method is able to reduce the noise level of the dataset and eventually approximate the Bayes optimal classifier. To verify the effectiveness of ARNet, we conduct experiments on multiple benchmark datasets. Experimental results demonstrate that our ARNet is superior to existing state-of-the-art approaches in noisy PLL. Our code will be made public soon.