零拍语义分割（ZS3）旨在分割培训中没有看到的新型类别。现有的作品将zs3作为像素级零拍分类问题，以及在仅使用文本预先培训的语言模型的帮助下，将语义知识从看见课程转移到未知一体。虽然简单，像素级ZS3配方显示了集成具有图像文本对预训练的视觉语言模型的有限能力，并且目前展示了愿景任务的巨大潜力。灵感来自观察，人类经常执行段级语义标签，我们建议将zs3分成两个子任务：1）将像素分组到段中的类别不可知的分组任务。 2）段的零拍分类任务。前者的子任务不涉及类别信息，可以直接传输到未安装类的组像素。后一子任务在段级执行，提供了一种自然的方式，可以利用预先培训的大规模视觉模型，用于ZS3的图像文本对（例如剪辑）。基于解耦制剂，我们提出了一种简单且有效的零拍语义分割模型，称为ZegFormer，这优于大幅边缘的先前方法，例如，Pascal VOC的35分和3分在Coco-在宫颈课程方面的东西。代码将在https://github.com/dingjiansw101/zegformer发布。

最近，Vision-Language预训练的零拍图像分类已经表现出令人难以置信的成就，即该模型可以对任意类别进行分类而不看到该类别的其他注释图像。然而，目前尚不清楚如何在更广泛的视觉问题上进行零射识别，例如对象检测和语义分割。在本文中，我们通过在现成的预训练的视觉模型，即剪辑上建立零拍语义分割来定位零拍语义分割。很难因为语义分割和剪辑模型在不同的视觉粒度上执行，该语义分段处理在像素上时，而剪辑在图像上执行。为了解决处理粒度的差异，我们拒绝使用普遍的一级FCN基于FCN的框架，并倡导一个两级语义分割框架，其中第一阶段提取一个完全提取的掩模提案和第二阶段利用基于图像的剪辑模型在第一阶段生成的蒙版图像作物上执行零拍分类。我们的实验结果表明，这种简单的框架通过大型利润率超越了先前的最先进：+29.5 Hiou On Pascal VOC 2012 DataSet，+8.9 Hiou On Coco Stuff DataSet。凭借其简单性和强大的表现，我们希望本框架成为促进未来研究的基准。

Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a two-stage scheme. The general idea is to first generate class-agnostic region proposals and then feed the cropped proposal regions to CLIP to utilize its image-level zero-shot classification capability. While effective, such a scheme requires two image encoders, one for proposal generation and one for CLIP, leading to a complicated pipeline and high computational cost. In this work, we pursue a simpler-and-efficient one-stage solution that directly extends CLIP's zero-shot prediction capability from image to pixel level. Our investigation starts with a straightforward extension as our baseline that generates semantic masks by comparing the similarity between text and patch embeddings extracted from CLIP. However, such a paradigm could heavily overfit the seen classes and fail to generalize to unseen classes. To handle this issue, we propose three simple-but-effective designs and figure out that they can significantly retain the inherent zero-shot capacity of CLIP and improve pixel-level generalization ability. Incorporating those modifications leads to an efficient zero-shot semantic segmentation system called ZegCLIP. Through extensive experiments on three public benchmarks, ZegCLIP demonstrates superior performance, outperforming the state-of-the-art methods by a large margin under both "inductive" and "transductive" zero-shot settings. In addition, compared with the two-stage method, our one-stage ZegCLIP achieves a speedup of about 5 times faster during inference. We release the code at https://github.com/ZiqinZhou66/ZegCLIP.git.

为了弥合监督语义细分与现实世界应用程序之间的差距，这些应用程序获取一个模型以识别任意新概念，最近的零弹性细分通过探索看不见的对象类别之间的关系，吸引了很多关注带有不同基础类别的通知数据。在本文中，我们提出了一种新的开放世界语义分割管道，该管道首次尝试学习各种开放世界类别的语义对象，而无需对密集注释进行任何努力，纯粹是通过纯粹利用自然存在的图像捕获数据来进行的。互联网。我们的方法，视觉语言驱动的语义分割（VIL-SEG），采用图像和文本编码器来生成图像捕获数据的视觉和文本嵌入，具有两个核心组件，具有赋予其分割能力的两个核心组件：首先，图像，图像，图像编码器通过基于视觉的对比和跨模式对比度进行了共同训练，这鼓励视觉嵌入既保留对细分任务至关重要的细粒语义和高级类别信息。此外，在图像编码器上设计了一个在线聚类头，该群体可以动态地将视觉嵌入到不同的语义组中，以便可以通过与各种文本嵌入来完成分类以完成我们的细分管道来对其进行分类。实验表明，如果不使用任何具有密集注释的数据，我们的方法可以直接分割任意类别的对象，超过了需要在三个基准数据集上进行数据标记的零摄像分割方法。

分组和识别是视觉场景理解的重要组成部分，例如，用于对象检测和语义分割。借助端到端的深度学习系统，图像区域的分组通常通过像素级识别标签的自上而下的监督隐式进行。取而代之的是，在本文中，我们建议将分组机制恢复到深层网络中，从而使语义片段仅在文本监督下自动出现。我们提出了一个分层分组视觉变压器（GroupVit），它超出了常规的网格结构表示，并学会了将图像区域分组为逐渐更大的任意形状段。我们通过对比度损失在大规模图像文本数据集上与文本编码器共同训练小组vit。只有文本监督并且没有任何像素级注释，GroupVit就学会了将语义区域分组在一起，并以零拍的方式成功地将语义分割的任务转移到语义分割的任务，即，而没有任何进一步的微调。它在Pascal VOC 2012上获得了52.3％MIOU的零拍摄精度和Pascal上下文数据集中的22.4％MIOU，并竞争性地表现为需要更高水平监督的最先进的转移学习方法。我们在https://github.com/nvlabs/groupvit上开放代码。

现代方法通常将语义分割标记为每个像素分类任务，而使用替代掩码分类处理实例级分割。我们的主要洞察力：掩码分类是足够的一般，可以使用完全相同的模型，丢失和培训过程来解决语义和实例级分段任务。在此观察之后，我们提出了一个简单的掩模分类模型，该模型预测了一组二进制掩码，每个模型与单个全局类标签预测相关联。总的来说，所提出的基于掩模分类的方法简化了语义和Panoptic分割任务的有效方法的景观，并显示出优异的经验结果。特别是，当类的数量大时，我们观察到掩码形成器优于每个像素分类基线。我们的面具基于分类的方法优于当前最先进的语义（ADE20K上的55.6 miou）和Panoptic Seation（Coco）模型的Panoptic Seationation（52.7 PQ）。

我们设计了一个开放式图像分割模型，以将图像组织到任意文本指示的有意义区域中。最近的作品（剪辑和对齐），尽管使用图像级字幕标签获得了令人印象深刻的开放式摄氏分类精度，但仍无法用像素分段视觉概念。我们认为这些模型错过了视觉分组的重要步骤，该模型在学习视觉语义对齐之前将像素组织成小组。我们建议OpenSeg解决上述问题，同时仍利用可扩展的图像级标题监督。首先，它学会了为可能的组织提出细分面具。然后，它通过将标题中的每个单词与一个或几个预测的面具对齐来学习视觉语义对齐。我们发现蒙版表示是支持字幕学习图像分割的关键，从而可以扩大数据集和词汇大小。 OpenSeg大大优于pascal数据集上LSEG最近的开放式LSEG +19.9 MIOU的开放式方法。

Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part segmentation into one task. Previous works utilize separated approaches to handle thing, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework named Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we make the following contributions: Firstly, we design a meta-architecture that decouples part feature and things/stuff feature, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Secondly, we propose a new metric Part-Whole Quality (PWQ) to better measure such task from both pixel-region and part-whole perspectives. It can also decouple the error for part segmentation and panoptic segmentation. Thirdly, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross attention scheme to further boost part segmentation qualities. We design a new part-whole interaction method using masked cross attention. Finally, the extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results with a significant cost drop of 70% on GFlops and 50% on parameters. Our models can serve as a strong baseline and aid future research in PPS. Code will be available.

对比语言 - 图像预训练（剪辑）在开放词汇零拍摄图像识别方面取得了显着突破。许多最近的研究利用预先训练的剪辑模型进行图像级分类和操纵。在本文中，我们进一步探索了剪辑的电位，用于像素级致密预测，具体地在语义分割中。在没有注释和微调的情况下，我们的方法Denseclip会产生合理的分段结果，在各种数据集中的开放概念上产生了合理的分段结果。通过添加伪标签和自我培训，Denseclip +超越了SOTA转换零点语义分割方法，通过大幅边缘，例如，Pascal VOC / Pascal Context / Coco Sift的宣传课程从35.6 / 20.7 / 30.3到86.1 / 66.7 / 54.7。我们还在输入损坏下测试了Denseclip的稳健性，并评估其在识别细粒度物体和新颖概念中的能力。我们的发现表明，Denseclip可以作为致密预测任务的新可靠的监督源，以实现无批准的分割。

最近的进展表明，使用对比图像文本对的大规模预训练可以是从自然语言监督的高质量视觉表演学习的有前途的替代方案。从更广泛的监督来源受益，这种新的范例展示了对下游分类任务和数据集的令人印象深刻的可转移性。然而，从图像文本对中学习的知识转移到更复杂的密集预测任务的问题几乎没有访问过。在这项工作中，我们通过隐式和明确地利用来自剪辑的预先训练的知识来提出了一种新的密集预测框架。具体地，我们将剪辑中的原始图像文本匹配问题转换为像素文本匹配问题，并使用像素文本分数图来指导致密预测模型的学习。通过进一步使用图像中的上下文信息来提示语言模型，我们能够促进我们的模型来更好地利用预先接受训练的知识。我们的方法是模型 - 不可行的，它可以应用于任意密集的预测系统和各种预先训练的视觉底座，包括夹模型和想象成预先训练的模型。广泛的实验证明了我们对语义分割，对象检测和实例分段任务的方法的卓越性能。代码可在https://github.com/raoyongming/denseclip获得

We tackle open-world semantic segmentation, which aims at learning to segment arbitrary visual concepts in images, by using only image-text pairs without dense annotations. Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and adapting the learned image-level understanding to the segmentation task. However, these methods based on CL have a discrepancy since it only considers image-text level alignment in training time, while the segmentation task requires region-text level alignment at test time. In this paper, we propose a novel Text-grounded Contrastive Learning (TCL) framework to directly align a text and a region described by the text to address the train-test discrepancy. Our method generates a segmentation mask associated with a given text, extracts grounded image embedding from the masked region, and aligns it with text embedding via TCL. The framework addresses the discrepancy by letting the model learn region-text level alignment instead of image-text level alignment and encourages the model to directly improve the quality of generated segmentation masks. In addition, for a rigorous and fair comparison, we present a unified evaluation protocol with widely used 8 semantic segmentation datasets. TCL achieves state-of-the-art zero-shot segmentation performance with large margins in all datasets. Code is available at https://github.com/kakaobrain/tcl.

We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss, intending to train an alignment between the patch tokens of the vision encoder and the CLS token of the text encoder. With such an alignment, a model can identify regions of an image corresponding to a given text input, and therefore transfer seamlessly to the task of open vocabulary semantic segmentation without requiring any segmentation annotations during training. Using pre-trained CLIP encoders with PACL, we are able to set the state-of-the-art on the task of open vocabulary zero-shot segmentation on 4 different segmentation benchmarks: Pascal VOC, Pascal Context, COCO Stuff and ADE20K. Furthermore, we show that PACL is also applicable to image-level predictions and when used with a CLIP backbone, provides a general improvement in zero-shot classification accuracy compared to CLIP, across a suite of 12 image classification datasets.

We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decodert takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixel-level and token-level outputs in the same semantic space. With such a novel design, X-Decoder is the first work that provides a unified way to support all types of image segmentation and a variety of vision-language (VL) tasks. Further, our design enables seamless interactions across tasks at different granularities and brings mutual benefits by learning a common and rich pixel-level visual-semantic understanding space, without any pseudo-labeling. After pretraining on a mixed set of a limited amount of segmentation data and millions of image-text pairs, X-Decoder exhibits strong transferability to a wide range of downstream tasks in both zero-shot and finetuning settings. Notably, it achieves (1) state-of-the-art results on open-vocabulary segmentation and referring segmentation on eight datasets; (2) better or competitive finetuned performance to other generalist and specialist models on segmentation and VL tasks; and (3) flexibility for efficient finetuning and novel task composition (e.g., referring captioning and image editing). Code, demo, video, and visualization are available at https://x-decoder-vl.github.io.

通常通过培训用于固定的对象类的模型来解决图像分割。稍后包含附加类或更复杂的查询是昂贵的，因为它需要重新培训包含这些表达式的数据集上的模型。在这里，我们提出了一个系统，该系统可以基于测试时间的任意提示生成图像分割。提示可以是文本或图像。这种方法使我们能够为三个常见的分段任务创建一个统一的模型（训练一次），这具有不同的挑战：引用表达式分割，零拍分段和单次分割。我们构建在剪辑模型中作为骨干，我们使用基于变压器的解码器扩展，该解码器能够致密预测。在对PhraseCut数据集的扩展版本进行培训之后，我们的系统基于自由文本提示符或表达查询的附加图像生成图像的二进制分段映射。详细分析了基于图像的提示的不同变体。这种新型混合输入允许不仅针对上述三个分段任务的动态调整，而是可以制定文本或图像查询的任何二进制分段任务。最后，我们发现我们的系统适应涉及可承受能力或属性的广义查询。源代码：https://ecterlab.org/code/clipseg

In this work, we focus on instance-level open vocabulary segmentation, intending to expand a segmenter for instance-wise novel categories without mask annotations. We investigate a simple yet effective framework with the help of image captions, focusing on exploiting thousands of object nouns in captions to discover instances of novel classes. Rather than adopting pretrained caption models or using massive caption datasets with complex pipelines, we propose an end-to-end solution from two aspects: caption grounding and caption generation. In particular, we devise a joint Caption Grounding and Generation (CGG) framework based on a Mask Transformer baseline. The framework has a novel grounding loss that performs explicit and implicit multi-modal feature alignments. We further design a lightweight caption generation head to allow for additional caption supervision. We find that grounding and generation complement each other, significantly enhancing the segmentation performance for novel categories. We conduct extensive experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS). The results demonstrate the superiority of our CGG framework over previous OVIS methods, achieving a large improvement of 6.8% mAP on novel classes without extra caption data. Our method also achieves over 15% PQ improvements for novel classes on the OSPS benchmark under various settings.

Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task in computer vision. Mainstream approaches follow a multi-stage framework and suffer from high training costs. In this paper, we explore the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels and without any further training. To efficiently generate high-quality segmentation masks from CLIP, we propose a novel framework called CLIP-ES for WSSS. Our framework improves all three stages of WSSS with special designs for CLIP: 1) We introduce the softmax function into GradCAM and exploit the zero-shot ability of CLIP to suppress the confusion caused by non-target classes and backgrounds. Meanwhile, to take full advantage of CLIP, we re-explore text inputs under the WSSS setting and customize two text-driven strategies: sharpness-based prompt selection and synonym fusion. 2) To simplify the stage of CAM refinement, we propose a real-time class-aware attention-based affinity (CAA) module based on the inherent multi-head self-attention (MHSA) in CLIP-ViTs. 3) When training the final segmentation model with the masks generated by CLIP, we introduced a confidence-guided loss (CGL) to mitigate noise and focus on confident regions. Our proposed framework dramatically reduces the cost of training for WSSS and shows the capability of localizing objects in CLIP. Our CLIP-ES achieves SOTA performance on Pascal VOC 2012 and MS COCO 2014 while only taking 10% time of previous methods for the pseudo mask generation. Code is available at https://github.com/linyq2117/CLIP-ES.

我们呈现LSEG，这是一种用于语言驱动语义图像分割的新模型。 LSEG使用文本编码器来计算描述性输入标签（例如，“草”或“构建”）的嵌入式，以及基于变压器的图像编码器，该图像编码器计算输入图像的密度每个像素嵌入。图像编码器具有对比度目标，以将像素嵌入对准对应语义类的文本嵌入。文本嵌入式提供了一种灵活的标签表示，其中将语义相似的标签映射到嵌入空间中的类似区域（例如，“猫”和“毛茸茸”）。这允许LSEG概括到以前在测试时间的预先看不见的类别，而不会再培训或甚至需要单一的额外训练样本。我们展示了与现有的零点和少量拍摄语义分割方法相比，我们的方法实现了高竞争激烈的零射性能，甚至在提供固定标签集时符合传统分段算法的准确性。代码和演示可在https://github.com/isl-org/lang-seg获取。

语义细分是一项关键的计算机视觉任务，该任务已经积极研究了几十年。近年来，监督方法已达到前所未有的准确性，但是每个新的类别类别都需要许多像素级注释，这是非常耗时和昂贵的。另外，当前语义分割网络处理大量类别的能力是有限的。这意味着包含稀有类别类别的图像不太可能通过当前方法进行很好的分割。在本文中，我们提出了一种为每个对象创建语义细分掩码的新方法，而无需训练分割网络或查看任何分割掩模。我们的方法用作图像中存在的类类别的图像级标签;它们可以自动或手动获得。我们利用Vision语言嵌入模型（特别是Clip）来使用模型解释性方法为每个类创建粗略分割映射。我们使用测试时间增强技术来优化地图。此阶段的输出提供像素级伪标签，而不是监督方法所需的手动像素级标签。鉴于伪标签，我们利用单图像分割技术来获得高质量的输出分割掩模。我们的方法是定量和定性地示出的，以优于使用类似的监督数量的方法。我们的结果对于包含罕见类别的图像特别显着。

Open-vocabulary object detection, which is concerned with the problem of detecting novel objects guided by natural language, has gained increasing attention from the community. Ideally, we would like to extend an open-vocabulary detector such that it can produce bounding box predictions based on user inputs in form of either natural language or exemplar image. This offers great flexibility and user experience for human-computer interaction. To this end, we propose a novel open-vocabulary detector based on DETR -- hence the name OV-DETR -- which, once trained, can detect any object given its class name or an exemplar image. The biggest challenge of turning DETR into an open-vocabulary detector is that it is impossible to calculate the classification cost matrix of novel classes without access to their labeled images. To overcome this challenge, we formulate the learning objective as a binary matching one between input queries (class name or exemplar image) and the corresponding objects, which learns useful correspondence to generalize to unseen queries during testing. For training, we choose to condition the Transformer decoder on the input embeddings obtained from a pre-trained vision-language model like CLIP, in order to enable matching for both text and image queries. With extensive experiments on LVIS and COCO datasets, we demonstrate that our OV-DETR -- the first end-to-end Transformer-based open-vocabulary detector -- achieves non-trivial improvements over current state of the arts.

这项工作的目的是在训练过程中划分和名称图像区域，而无需访问像素级标签。为了解决这项任务，我们通过提炼两个基础模型的互补优势来构建细分器。第一个剪辑（Radford等，2021）具有将名称分配给图像内容的能力，但缺乏对象结构的可访问表示。第二个Dino（Caron等，2021）捕获了物体的空间范围，但对对象名称不了解。我们的方法称为名为Mask，开始使用剪辑来构建特定于类别的图像档案。这些图像用dino的类别 - 敏捷的对象检测器进行伪标记，然后使用夹档案标签通过类别特定的细分器进行完善。得益于精制面具的高质量，我们表明，在这些档案中训练有适当数据的培训的标准分割体系结构可为单对象和多对象图像带来令人印象深刻的语义细分能力。结果，我们提出的名字命名为在包括VOC2012，可可和大规模ImageNet-S数据集在内的五个基准上的一系列先前工作中表现出色。