低光图像噪声和色差的问题是对象检测，语义分割，实例分割等任务的挑战性问题。在本文中，我们提出了用于低照明增强的算法。KIND-LE使用网络结构中的光曲线估计模块来增强视网膜分解图像中的照明图，从而改善图像亮度。我们提出了照明图和反射图融合模块，以恢复恢复的图像细节并减少细节损失。最后，我们包括了消除噪声的总变化损失函数。我们的方法将GLADNET数据集用作训练集，而LOL数据集则是测试集，并使用Exdark作为下游任务的数据集进行了验证。基准上的广泛实验证明了我们方法的优势，并且接近最先进的结果，该结果的PSNR为19.7216，SSIM在指标方面为0.8213。

旨在估算每个广告接触点在转换旅程中的贡献的多点触摸归因（MTA）对于预算分配和自动广告至关重要。现有方法首先训练模型，以通过历史数据来预测广告旅程的转换概率，并使用反事实预测来计算每个接触点的归因。这些作品的假设是转换预测模型是公正的，即，它可以对任何随机分配的旅程（包括事实和反事实）提供准确的预测。然而，由于根据用户偏好推荐裸露的广告，因此这个假设并不总是存在。用户的这种混杂偏见将导致反事实预测中的分布（OOD）问题，并导致归因中的概念漂移。在本文中，我们定义了因果MTA任务，并提出Causalmta来消除用户偏好的影响。它从系统地消除了静态和动态偏好的混杂偏见，以使用历史数据来学习转换预测模型。我们还提供理论分析，以证明Causalmta可以学习具有足够数据的无偏见模型。电子商务公司的公共数据集和印象数据的广泛实验表明，Causalmta不仅比最先进的方法实现了更好的预测性能，而且还可以在不同的广告渠道上产生有意义的属性信用。

创建视觉布局是图形设计的重要步骤。当我们寻求比例和多样化的视觉设计时，这种布局的自动生成很重要。在自动布局的作品上，专注于无条件生成，其中模型在忽略用户需要进行特定问题的同时生成布局。为了提前有条件布局，我们介绍了BLT，双向布局变压器。 BLT与自回归解码不同，因为它首先生成满足用户输入的布局，然后迭代地改进布局。我们验证了具有各种保真度量的多个基准测试模型。我们的结果表明，最先进的布局变压器模型的两个主要进步。首先，我们的模型授权布局变压器来满足可控布局的制作。其次，我们的模型削减了自回归解码的线性推理时间达到恒定的复杂度，从而在推理时间以制定布局实现4x-10x的加速。

核细胞分割是数字病理分析中的基本任务，可以通过基于深度学习的方法自动化。然而，这种自动化方法的发展需要大量数据具有精确的注释掩模，这很难获得。具有弱标记数据的培训是减少注释工作量的流行解决方案。在本文中，我们提出了一种新的基于元学习的核细胞分段方法，其跟随标签校正范例，以利用嘈杂的面具利用数据。具体而言，我们设计一个完全传统的元模型，可以使用少量清洁的元数据来纠正嘈杂的掩模。然后，纠正的掩模可用于监督分割模型的训练。同时，采用双级优化方法来交替地以端到端的方式更新主要分段模型和元模型的参数。两个核细分数据集的广泛实验结果表明，我们的方法实现了最先进的结果。它甚至可以在一些嘈杂的设置中实现了对监督数据的模型培训相当的性能。

我们使用无卷积的变压器架构提出了一种从未标记数据学习多式式表示的框架。具体而言，我们的视频音频文本变压器（Vatt）将原始信号作为输入提取，提取丰富的多式化表示，以使各种下游任务受益。我们使用多模式对比损失从头划线训练Vatt端到端，并通过视频动作识别，音频事件分类，图像分类和文本到视频检索的下游任务评估其性能。此外，我们通过共享三种方式之间的重量来研究模型 - 无话的单骨架变压器。我们表明，无卷积VATT优于下游任务中的最先进的Convnet架构。特别是，Vatt的视觉变压器在动力学-400上实现82.1％的高精度82.1％，在动力学-600，72.7％的动力学-700上的72.7％，以及时间的时间，新的记录，在避免受监督的预训练时，新的记录。通过从头划伤训练相同的变压器，转移到图像分类导致图像分类导致78.7％的ImageNet精度为64.7％，尽管视频和图像之间的域间差距，我们的模型概括了我们的模型。 Vatt的音雅音频变压器还通过在没有任何监督的预训练的情况下在Audioset上实现39.4％的地图来设置基于波形的音频事件识别的新记录。 Vatt的源代码是公开的。

Letting a deep network be aware of the quality of its own predictions is an interesting yet important problem. In the task of instance segmentation, the confidence of instance classification is used as mask quality score in most instance segmentation frameworks. However, the mask quality, quantified as the IoU between the instance mask and its ground truth, is usually not well correlated with classification score. In this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks. The proposed network block takes the instance feature and the corresponding predicted mask together to regress the mask IoU. The mask scoring strategy calibrates the misalignment between mask quality and mask score, and improves instance segmentation performance by prioritizing more accurate mask predictions during COCO AP evaluation. By extensive evaluations on the COCO dataset, Mask Scoring R-CNN brings consistent and noticeable gain with different models, and outperforms the state-of-the-art Mask R-CNN. We hope our simple and effective approach will provide a new direction for improving instance segmentation. The source code of our method is available at https:// github.com/zjhuang22/maskscoring_rcnn. * The work was done when Zhaojin Huang was an intern in Horizon Robotics Inc.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computational methods on MLS quantification not only require intensive labeling in millimeter-level measurement but also suffer from poor performance due to their dependence on specific landmarks or simplified anatomical assumptions. In this paper, we propose a novel semi-supervised framework to accurately measure the scale of MLS from head CT scans. We formulate the MLS measurement task as a deformation estimation problem and solve it using a few MLS slices with sparse labels. Meanwhile, with the help of diffusion models, we are able to use a great number of unlabeled MLS data and 2793 non-MLS cases for representation learning and regularization. The extracted representation reflects how the image is different from a non-MLS image and regularization serves an important role in the sparse-to-dense refinement of the deformation field. Our experiment on a real clinical brain hemorrhage dataset has achieved state-of-the-art performance and can generate interpretable deformation fields.

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-balanced Re-weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 \& V6 show the performances and generality of the SCR with the traditional SGG models.