农作物残留物燃烧是世界许多地方的空气污染的主要来源，尤其是南亚。政策制定者，从业人员和研究人员都投资了衡量影响和制定干预措施以减少燃烧。但是，测量燃烧的影响或干预措施的有效性减少燃烧需要数据燃烧的位置。这些数据在成本和可行性方面都在现场收集具有挑战性。我们利用印度旁遮普邦旁遮普邦农作物残留物燃烧的地面监测的数据，以探索使用可访问的卫星图像是否可以更有效地检测到燃烧。具体而言，我们使用了具有高时间分辨率（最多每天）的3M Planetscope数据以及具有每周时间分辨率但光谱信息深度的公共可用Sentinel-2数据。在分析了不同光谱带和燃烧指数单独分离燃烧和未燃烧图的能力之后，我们构建了一个随机森林模型，这些模型确定提供了最大的分离性，并用地面验证的数据评估了模型性能。鉴于测量所带来的挑战，我们的总体模型精度为82％是有利的。基于此过程的见解，我们讨论了检测卫星图像中农作物残留物燃烧的技术挑战，以及衡量燃烧和政策干预措施的影响的挑战。

图像颜色协调算法旨在自动匹配在不同条件下捕获的前景图像的颜色分布和背景图像。以前的基于深度学习的模型忽略了两个对于实际应用至关重要的问题，即高分辨率（HR）图像处理和模型的可理解性。在本文中，我们提出了一个新型的深层综合颜色滤波器（DCCF）学习框架，用于高分辨率图像协调。具体而言，DCCF首先将原始输入图像列为其低分辨率（LR）对抗零件，然后以端到端的方式学习四个人类可理解的神经过滤器（即色相，饱和，饱和，价值和细心的渲染过滤器），最终以将这些过滤器应用于原始输入图像以获得统一的结果。从可理解的神经过滤器中受益，我们可以为用户提供一个简单而有效的处理程序，以便用户与Deep Model合作，以便在必要时很少努力获得所需的结果。广泛的实验证明了DCCF学习框架的有效性，并且它在IHARMONY4数据集上的最先进的后处理方法优于图像的全分辨率，分别在MSE和PSNR上实现了7.63％和1.69％的相对改进，从而超过了图像的全分辨率。

许多图像处理网络在整个输入图像上应用一组静态卷积核，这是自然图像的次优，因为它们通常由异质视觉模式组成。最近在分类，分割和图像恢复方面的工作已经证明，动态核优于局部图像统计数据的静态内核。然而，这些工作经常采用每像素卷积核，这引入了高存储器和计算成本。为了在没有显着开销的情况下实现空间变化的处理，我们呈现\ TextBF {Malle} Chable \ TextBF {CONV} olution（\ textbf {malleconv}），作为动态卷积的有效变体。 \我们的权重由能够在特定空间位置产生内容相关的输出的有效预测器网络动态地产生。与以前的作品不同，\我们从输入生成一组更小的空间变化内核，这会扩大网络的接收领域，并显着降低计算和内存成本。然后通过具有最小内存开销的高效切片和-Conver操作员将这些内核应用于全分辨率的特征映射。我们进一步使用MalleConv建立了高效的去噪网络，被创建为\ textbf {mallenet}。它实现了高质量的结果，没有非常深的架构，例如，它是8.91 $ \ times $的速度快于最好的去噪算法（Swinir），同时保持类似的性能。我们还表明，添加到标准的基于卷积的骨干的单个\我们可以贡献显着降低计算成本或以相似的成本提高图像质量。项目页面：https://yifanjiang.net/malleconv.html

深度学习（DL）模型为各种医学成像基准挑战提供了最先进的性能，包括脑肿瘤细分（BRATS）挑战。然而，局灶性病理多隔室分割（例如，肿瘤和病变子区）的任务特别具有挑战性，并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性，可以实现最不确定的地区的临床审查，从而建立信任并铺平临床翻译。最近，已经引入了许多不确定性估计方法，用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中，我们探索并评估在Brats 2019-2020任务期间开发的公制，以对不确定量化量化（Qu-Brats），并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制（1）奖励不确定性估计，对正确断言产生高置信度，以及在不正确的断言处分配低置信水平的估计数，（2）惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性，所有这些都参与了主要的Brats细分任务。总体而言，我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值，因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。

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.

Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework. Specifically, we propose a weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms. Distance constraints ensure that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others. We illustrate that resulting model shows improvements in supervised, zero-shot, generalized zero-shot, and large open set recognition, with up to 310K class vocabulary on Animal with Attributes and ImageNet datasets.

Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typically involves computing the minimal cost path through intermediate representations derived from segmentation masks. However, this methodology has significant limitations in the context of projective imaging of tree-structured 3D anatomical data such as coronary arteries, since there are often overlapping branches in the 2D projection. In this work, we propose a novel approach to predicting tree connectivity structure which reformulates the task as an optimization problem over individual steps of a recursive process. We design and train a two-stage model which leverages the UNet and Transformer architectures and introduces an image-based prompting technique. Our proposed method achieves compelling results on a pair of synthetic datasets, and outperforms a shortest-path baseline.

Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and ({even more importantly}) explainable predictions. Existing approaches typically explicate network outputs in a post-hoc fashion, under an implicit assumption that faithful explanations come from accurate predictions/classifications. We have an opposite claim that explanations boost (or even determine) classification. That is, end-to-end learning of explanation factors to augment discriminative representation extraction could be a more intuitive strategy to inversely assure fine-grained explainability, e.g., in those neuroimaging and neuroscience studies with high-dimensional data containing noisy, redundant, and task-irrelevant information. In this paper, we propose such an explainable geometric deep network dubbed as NeuroExplainer, with applications to uncover altered infant cortical development patterns associated with preterm birth. Given fundamental cortical attributes as network input, our NeuroExplainer adopts a hierarchical attention-decoding framework to learn fine-grained attentions and respective discriminative representations to accurately recognize preterm infants from term-born infants at term-equivalent age. NeuroExplainer learns the hierarchical attention-decoding modules under subject-level weak supervision coupled with targeted regularizers deduced from domain knowledge regarding brain development. These prior-guided constraints implicitly maximizes the explainability metrics (i.e., fidelity, sparsity, and stability) in network training, driving the learned network to output detailed explanations and accurate classifications. Experimental results on the public dHCP benchmark suggest that NeuroExplainer led to quantitatively reliable explanation results that are qualitatively consistent with representative neuroimaging studies.

Improving the visual quality of the given degraded observation by correcting exposure level is a fundamental task in the computer vision community. Existing works commonly lack adaptability towards unknown scenes because of the data-driven patterns (deep networks) and limited regularization (traditional optimization), and they usually need time-consuming inference. These two points heavily limit their practicability. In this paper, we establish a Practical Exposure Corrector (PEC) that assembles the characteristics of efficiency and performance. To be concrete, we rethink the exposure correction to provide a linear solution with exposure-sensitive compensation. Around generating the compensation, we introduce an exposure adversarial function as the key engine to fully extract valuable information from the observation. By applying the defined function, we construct a segmented shrinkage iterative scheme to generate the desired compensation. Its shrinkage nature supplies powerful support for algorithmic stability and robustness. Extensive experimental evaluations fully reveal the superiority of our proposed PEC. The code is available at https://rsliu.tech/PEC.