Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Positive-Unlabeled (PU) learning aims to learn a model with rare positive samples and abundant unlabeled samples. Compared with classical binary classification, the task of PU learning is much more challenging due to the existence of many incompletely-annotated data instances. Since only part of the most confident positive samples are available and evidence is not enough to categorize the rest samples, many of these unlabeled data may also be the positive samples. Research on this topic is particularly useful and essential to many real-world tasks which demand very expensive labelling cost. For example, the recognition tasks in disease diagnosis, recommendation system and satellite image recognition may only have few positive samples that can be annotated by the experts. These methods mainly omit the intrinsic hardness of some unlabeled data, which can result in sub-optimal performance as a consequence of fitting the easy noisy data and not sufficiently utilizing the hard data. In this paper, we focus on improving the commonly-used nnPU with a novel training pipeline. We highlight the intrinsic difference of hardness of samples in the dataset and the proper learning strategies for easy and hard data. By considering this fact, we propose first splitting the unlabeled dataset with an early-stop strategy. The samples that have inconsistent predictions between the temporary and base model are considered as hard samples. Then the model utilizes a noise-tolerant Jensen-Shannon divergence loss for easy data; and a dual-source consistency regularization for hard data which includes a cross-consistency between student and base model for low-level features and self-consistency for high-level features and predictions, respectively.

The task of Few-shot learning (FSL) aims to transfer the knowledge learned from base categories with sufficient labelled data to novel categories with scarce known information. It is currently an important research question and has great practical values in the real-world applications. Despite extensive previous efforts are made on few-shot learning tasks, we emphasize that most existing methods did not take into account the distributional shift caused by sample selection bias in the FSL scenario. Such a selection bias can induce spurious correlation between the semantic causal features, that are causally and semantically related to the class label, and the other non-causal features. Critically, the former ones should be invariant across changes in distributions, highly related to the classes of interest, and thus well generalizable to novel classes, while the latter ones are not stable to changes in the distribution. To resolve this problem, we propose a novel data augmentation strategy dubbed as PatchMix that can break this spurious dependency by replacing the patch-level information and supervision of the query images with random gallery images from different classes from the query ones. We theoretically show that such an augmentation mechanism, different from existing ones, is able to identify the causal features. To further make these features to be discriminative enough for classification, we propose Correlation-guided Reconstruction (CGR) and Hardness-Aware module for instance discrimination and easier discrimination between similar classes. Moreover, such a framework can be adapted to the unsupervised FSL scenario.

带有嘈杂标签的训练深神经网络（DNN）实际上是具有挑战性的，因为不准确的标签严重降低了DNN的概括能力。以前的努力倾向于通过识别带有粗糙的小损失标准来减轻嘈杂标签的干扰的嘈杂数据来处理统一的denoising流中的零件或完整数据，而忽略了嘈杂样本的困难是不同的，因此是刚性和统一的。数据选择管道无法很好地解决此问题。在本文中，我们首先提出了一种称为CREMA的粗到精细的稳健学习方法，以分裂和串扰的方式处理嘈杂的数据。在粗糙水平中，干净和嘈杂的集合首先从统计意义上就可信度分开。由于实际上不可能正确对所有嘈杂样本进行分类，因此我们通过对每个样本的可信度进行建模来进一步处理它们。具体而言，对于清洁集，我们故意设计了一种基于内存的调制方案，以动态调整每个样本在训练过程中的历史可信度顺序方面的贡献，从而减轻了错误地分组为清洁集中的嘈杂样本的效果。同时，对于分类为嘈杂集的样品，提出了选择性标签更新策略，以纠正嘈杂的标签，同时减轻校正错误的问题。广泛的实验是基于不同方式的基准，包括图像分类（CIFAR，Clothing1M等）和文本识别（IMDB），具有合成或自然语义噪声，表明CREMA的优势和普遍性。

无监督的域适应性（UDA）旨在使在标记的源域上训练的模型适应未标记的目标域。在本文中，我们提出了典型的对比度适应（PROCA），这是一种无监督域自适应语义分割的简单有效的对比度学习方法。以前的域适应方法仅考虑跨各个域的阶级内表示分布的对齐，而阶层间结构关系的探索不足，从而导致目标域上的对齐表示可能不像在源上歧视的那样容易歧视。域了。取而代之的是，ProCA将类间信息纳入班级原型，并采用以班级为中心的分布对齐进行适应。通过将同一类原型与阳性和其他类原型视为实现以集体为中心的分配对齐方式的负面原型，Proca在经典领域适应任务上实现了最先进的性能，{\ em i.e. text {and} synthia $ \ to $ cityScapes}。代码可在\ href {https://github.com/jiangzhengkai/proca} {proca}获得代码

由生物学进化的动机，本文通过类比与经过验证的实践进化算法（EA）相比，解释了视觉变压器的合理性，并得出了两者都具有一致的数学表述。然后，我们受到有效的EA变体的启发，我们提出了一个新型的金字塔饮食式主链，该主链仅包含拟议的\ emph {ea-ea-lase transformer}（eat）块，该块由三个残留零件组成，\ ie，\ emph {多尺度区域聚集}（msra），\ emph {global and local互动}（GLI）和\ emph {feed-forward Network}（ffn）模块，以分别建模多尺度，交互和个人信息。此外，我们设计了一个与变压器骨架对接的\ emph {与任务相关的头}（TRH），以更灵活地完成最终信息融合，并\ emph {reviv} a \ emph {调制变形MSA}（MD-MSA），以动态模型模型位置。关于图像分类，下游任务和解释性实验的大量定量和定量实验证明了我们方法比最新方法（SOTA）方法的有效性和优越性。 \例如，我们的手机（1.8m），微小（6.1m），小（24.3m）和基地（49.0m）型号达到了69.4、78.4、83.1和83.9的83.9 TOP-1仅在Imagenet-1 K上接受NAIVE训练的TOP-1食谱; Eatformer微型/小型/基本武装面具-R-CNN获得45.4/47.4/49.0盒AP和41.4/42.9/44.2掩膜可可检测，超过当代MPVIT-T，SWIN-T，SWIN-T和SWIN-S，而SWIN-S则是0.6/ 1.4/0.5盒AP和0.4/1.3/0.9掩码AP分别使用较少的拖鞋；我们的Eatformer-small/base在Upernet上获得了47.3/49.3 MIOU，超过Swin-T/S超过2.8/1.7。代码将在\ url {https：//https：//github.com/zhangzjn/eatformer}上提供。

视频实例细分（VIS）是一项在视频中同时需要分类，细分和实例关联的任务。最近的VIS方法依靠复杂的管道来实现此目标，包括与ROI相关的操作或3D卷积。相比之下，我们通过添加额外的跟踪头提出了基于实例分割方法Condinst的简单有效的单阶段VIS框架。为了提高实例关联精度，提出了一种新型的双向时空对比度学习策略，用于跟踪跨帧的嵌入。此外，利用实例的时间一致性方案来产生时间连贯的结果。在YouTube-VIS-2019，YouTube-Vis-2021和OVIS-2021数据集上进行的实验验证了所提出方法的有效性和效率。我们希望所提出的框架可以作为许多其他实例级视频关联任务的简单而强大的替代方案。

在恢复低分辨率灰度图像的实际应用中，我们通常需要为目标设备运行三个单独的图像着色，超分辨率和Dows采样操作。但是，该管道对于独立进程是冗余的并且低效，并且可以共享一些内部特征。因此，我们提出了一种有效的范例来执行{s} {s} {c} olorization和{s} Uper分辨率（SCS），并提出了端到端的SCSNet来实现这一目标。该方法由两部分组成：用于学习颜色信息的彩色分支，用于采用所提出的即插即用\ EMPH {金字塔阀跨关注}（PVCATTN）模块来聚合源和参考图像之间的特征映射;和超分辨率分支集成颜色和纹理信息以预测使用设计的\ emph {连续像素映射}（CPM）模块的目标图像来预测连续放大率的高分辨率图像。此外，我们的SCSNet支持对实际应用更灵活的自动和参照模式。丰富的实验证明了我们通过最先进的方法生成真实图像的方法的优越性，例如，平均降低了1.8 $ \ Depararrow $和5.1 $ \ Downarrow $相比，与自动和参照模式的最佳分数相比，分别在拥有更少的参数（超过$ \ \倍$ 2 $ \ dovearrow $）和更快的运行速度（超过$ \ times $ 3 $ \ Uprarow $）。

弱监督对象本地化（WSOL）旨在仅通过使用图像级标签来学习对象本地化器。基于卷积神经网络（CNN）的技术通常导致突出显示物体的最辨别部分，同时忽略整个对象范围。最近，变压器架构已经部署到WSOL，以捕获具有自我关注机制和多层的Perceptron结构的远程特征依赖性。然而，变压器缺乏CNN所固有的局部感应偏差，因此可以恶化WSOL中的局部特征细节。在本文中，我们提出了一种基于变压器的新型框架，称为LCTR（局部连续性变压器），该框架被称为LCTR（局部连续性变压器），该框架在长期特征依赖项中提高全局特征的本地感知能力。为此，我们提出了一个关系的修补程序注意模块（RPAM），其考虑全球跨补丁信息。我们进一步设计了一个CUE挖掘模块（CDM），它利用本地特征来指导模型的学习趋势，以突出弱局部响应。最后，在两个广泛使用的数据集，即Cub-200-2011和ILSVRC上进行综合实验，以验证我们方法的有效性。