Conventional cameras capture image irradiance on a sensor and convert it to RGB images using an image signal processor (ISP). The images can then be used for photography or visual computing tasks in a variety of applications, such as public safety surveillance and autonomous driving. One can argue that since RAW images contain all the captured information, the conversion of RAW to RGB using an ISP is not necessary for visual computing. In this paper, we propose a novel $\rho$-Vision framework to perform high-level semantic understanding and low-level compression using RAW images without the ISP subsystem used for decades. Considering the scarcity of available RAW image datasets, we first develop an unpaired CycleR2R network based on unsupervised CycleGAN to train modular unrolled ISP and inverse ISP (invISP) models using unpaired RAW and RGB images. We can then flexibly generate simulated RAW images (simRAW) using any existing RGB image dataset and finetune different models originally trained for the RGB domain to process real-world camera RAW images. We demonstrate object detection and image compression capabilities in RAW-domain using RAW-domain YOLOv3 and RAW image compressor (RIC) on snapshots from various cameras. Quantitative results reveal that RAW-domain task inference provides better detection accuracy and compression compared to RGB-domain processing. Furthermore, the proposed \r{ho}-Vision generalizes across various camera sensors and different task-specific models. Additional advantages of the proposed $\rho$-Vision that eliminates the ISP are the potential reductions in computations and processing times.

Large-scale diffusion models have achieved state-of-the-art results on text-to-image synthesis (T2I) tasks. Despite their ability to generate high-quality yet creative images, we observe that attribution-binding and compositional capabilities are still considered major challenging issues, especially when involving multiple objects. In this work, we improve the compositional skills of T2I models, specifically more accurate attribute binding and better image compositions. To do this, we incorporate linguistic structures with the diffusion guidance process based on the controllable properties of manipulating cross-attention layers in diffusion-based T2I models. We observe that keys and values in cross-attention layers have strong semantic meanings associated with object layouts and content. Therefore, we can better preserve the compositional semantics in the generated image by manipulating the cross-attention representations based on linguistic insights. Built upon Stable Diffusion, a SOTA T2I model, our structured cross-attention design is efficient that requires no additional training samples. We achieve better compositional skills in qualitative and quantitative results, leading to a 5-8% advantage in head-to-head user comparison studies. Lastly, we conduct an in-depth analysis to reveal potential causes of incorrect image compositions and justify the properties of cross-attention layers in the generation process.

Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion. However, the segmentation process only considers each pixel independently, and the expansion process is difficult to achieve a favorable accuracy-speed trade-off. In this paper, we propose a Context-aware and Boundary-guided Network (CBN) to tackle these problems. In CBN, a basic text detector is firstly used to predict initial segmentation results. Then, we propose a context-aware module to enhance text kernel feature representations, which considers both global and local contexts. Finally, we introduce a boundary-guided module to expand enhanced text kernels adaptively with only the pixels on the contours, which not only obtains accurate text boundaries but also keeps high speed, especially on high-resolution output maps. In particular, with a lightweight backbone, the basic detector equipped with our proposed CBN achieves state-of-the-art results on several popular benchmarks, and our proposed CBN can be plugged into several segmentation-based methods. Code will be available on https://github.com/XiiZhao/cbn.pytorch.

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

Prompt tuning is a new few-shot transfer learning technique that only tunes the learnable prompt for pre-trained vision and language models such as CLIP. However, existing prompt tuning methods tend to learn spurious or entangled representations, which leads to poor generalization to unseen concepts. Towards non-spurious and efficient prompt learning from limited examples, this paper presents a novel \underline{\textbf{C}}ounterfactual \underline{\textbf{P}}rompt \underline{\textbf{L}}earning (CPL) method for vision and language models, which simultaneously employs counterfactual generation and contrastive learning in a joint optimization framework. Particularly, CPL constructs counterfactual by identifying minimal non-spurious feature change between semantically-similar positive and negative samples that causes concept change, and learns more generalizable prompt representation from both factual and counterfactual examples via contrastive learning. Extensive experiments demonstrate that CPL can obtain superior few-shot performance on different vision and language tasks than previous prompt tuning methods on CLIP. On image classification, we achieve 3.55\% average relative improvement on unseen classes across seven datasets; on image-text retrieval and visual question answering, we gain up to 4.09\% and 25.08\% relative improvements across three few-shot scenarios on unseen test sets respectively.

有两种流行的损失功能用于视觉检索，即三胞胎损失和对比度学习损失，这两者本质上都可以最大程度地减少负对和正对的相似性之间的差异。更具体地说，在现有的检索模型中广泛使用的硬采矿（三重态HN）的三胞胎损失很容易落入训练中的局部最小值。另一方面，广泛用于视觉的预训练中的视觉对比学习损失（VLC）已被证明可以在视觉语言检索上获得显着的性能提高，但通过使用微调的性能来实现。小型数据集上的VLC并不令人满意。本文提出了对视觉语言检索的统一损失相似性优化，为理解现有的损失功能提供了强大的工具。我们的统一损失包括VLC的硬样品挖掘策略，并引入了三胞胎损失使用的边距，以获得更好的相似性分离。结果表明，三重态HN和VLC都是我们统一损失的特殊形式。与三胞胎-HN相比，我们的统一损失具有快速的收敛速度。与VLC相比，我们的统一损失更具歧视性，可以在下游微调任务中更好地概括。图像文本和视频检索基准测试的实验表明，我们的统一损失可以显着提高最新检索模型的性能。

三维（3D）图像（例如CT，MRI和PET）在医学成像应用中很常见，在临床诊断中很重要。语义歧义是许多医学图像标签的典型特征。这可能是由许多因素引起的，例如成像特性，病理解剖学以及二进制面具的弱表示，这给精确的3D分割带来了挑战。在2D医学图像中，使用软面膜代替图像垫形式产生的二进制掩码来表征病变可以提供丰富的语义信息，更全面地描述病变的结构特征，从而使后续诊断和分析受益。在这项工作中，我们将图像垫子介绍到3D场景中，以描述3D医学图像中的病变。 3D模态中图像垫的研究有限，并且没有与3D矩阵相关的高质量注释数据集，因此减慢了基于数据驱动的深度学习方法的发展。为了解决这个问题，我们构建了第一个3D医疗垫数据集，并通过质量控制和下游实验中的肺结节分类中令人信服地验证了数据集的有效性。然后，我们将四个选定的最新2D图像矩阵算法调整为3D场景，并进一步自定义CT图像的方法。此外，我们提出了第一个端到端的深3D垫网络，并实施了可靠的3D医疗图像垫测试基准，该基准将被发布以鼓励进一步的研究。

视觉导航要求代理商遵循自然语言说明以达到特定目标。可见的环境和看不见的环境之间的巨大差异使代理商概括良好的挑战。先前的研究提出了数据增强方法，以明确或隐式地减轻数据偏见并提供概括的改进。但是，他们试图记住增强的轨迹，并在测试时忽略在看不见的环境下的分布变化。在本文中，我们提出了一个看不见的差异，预期视力和语言导航（戴维斯），该差异通过鼓励测试时间的视觉一致性来概括为看不见的环境。具体来说，我们设计了：1）半监督框架戴维斯（Davis），该框架利用类似的语义观测来利用视觉一致性信号。 2）一个两阶段的学习程序，鼓励适应测试时间分布。该框架增强了模仿和强化学习的基本混合物与动量形成对比，以鼓励在联合训练阶段和测试时间适应阶段对类似观察的稳定决策。广泛的实验表明，戴维斯在R2R和RXR基准上实现了与先前最先进的VLN基线相比，取得了模型不合命源性的改进。我们的源代码和数据是补充材料。

近年来，压缩图像超分辨率已引起了极大的关注，其中图像被压缩伪像和低分辨率伪影降解。由于复杂的杂化扭曲变形，因此很难通过简单的超分辨率和压缩伪像消除掉的简单合作来恢复扭曲的图像。在本文中，我们向前迈出了一步，提出了层次的SWIN变压器（HST）网络，以恢复低分辨率压缩图像，该图像共同捕获分层特征表示并分别用SWIN Transformer增强每个尺度表示。此外，我们发现具有超分辨率（SR）任务的预处理对于压缩图像超分辨率至关重要。为了探索不同的SR预审查的影响，我们将常用的SR任务（例如，比科比奇和不同的实际超分辨率仿真）作为我们的预处理任务，并揭示了SR在压缩的图像超分辨率中起不可替代的作用。随着HST和预训练的合作，我们的HST在AIM 2022挑战中获得了低质量压缩图像超分辨率轨道的第五名，PSNR为23.51db。广泛的实验和消融研究已经验证了我们提出的方法的有效性。

在统一框架中为检测和跟踪建模的时间信息已被证明是视频实例分割（VIS）的有希望的解决方案。但是，如何有效地将时间信息纳入在线模型仍然是一个空旷的问题。在这项工作中，我们提出了一个名为Inspeacity（IAI）的新的在线Vis范式，该范式以有效的方式对检测和跟踪进行建模。详细说明，IAI采用了一个新颖的识别模块来明确预测跟踪实例的标识号。为了传递时间信息跨框架，IAI使用了结合当前特征和过去嵌入的关联模块。值得注意的是，IAI可以与不同的图像模型集成。我们对三个VIS基准进行了广泛的实验。 IAI在YouTube-VIS-2019（Resnet-101 41.9地图）和YouTube-VIS-2021（Resnet-50 37.7地图）上胜过所有在线竞争对手。令人惊讶的是，在更具挑战性的OVI上，IAI实现了SOTA性能（20.3地图）。代码可从https://github.com/zfonemore/iai获得