自我监督的方法在计算机视野领域表现出巨大的成功，包括在遥感和医学成像中的应用。最流行的基于损坏的方法，例如SIMCLR，MOCO，MOCO-V2，通过在图像上应用人为的增强来创建正对并将其与负面示例进行对比，从而使用同一图像的多个视图。尽管这些技术运行良好，但大多数这些技术都在ImageNet（以及类似的计算机视觉数据集）上进行了调整。尽管有一些尝试捕获积极样本中更丰富的变形集，但在这项工作中，我们探索了一种有希望的替代方法，可以在对比度学习框架内为遥感数据生成积极的示例。可以将来自同一位置的不同传感器捕获的图像可以被认为是同一场景的强烈增强实例，从而消除了探索和调整一套手工制作的强大增强的需求。在本文中，我们提出了一个简单的双编码框架，该框架已在Sentinel-1和Sentinel-2图像对的大型未标记数据集（〜1m）上进行了预训练。我们测试了两个遥感下游任务的嵌入：洪水分割和土地覆盖映射，并从经验上表明，从该技术中学到的嵌入优于通过积极的数据增强来收集积极示例的传统技术。

我们开发了一个基于深度学习的卷积回归模型，该模型估计了土壤顶部〜5 cm中的体积土壤水分含量。输入预测因子包括Sentinel-1（活动雷达），Sentinel-2（光学图像）和SMAP（被动雷达），以及来自GLDAS的土壤格林和建模土壤水分领域的地球物理变量。该模型在2015年至2021年期间对全球约1300个原位传感器的数据进行了训练和评估，并获得了0.727的平均每传感器相关性为0.727，UBRMSE为0.054，可用于在名义上生产土壤水分图。320m分辨率。这些结果是针对不同位置的其他13个土壤水分厂进行基准测试的，并使用消融研究来识别重要的预测因子。

我们对最近的自我和半监督ML技术进行严格的评估，从而利用未标记的数据来改善下游任务绩效，以河床分割的三个遥感任务，陆地覆盖映射和洪水映射。这些方法对于遥感任务特别有价值，因为易于访问未标记的图像，并获得地面真理标签通常可以昂贵。当未标记的图像（标记数据集之外）提供培训时，我们量化性能改进可以对这些遥感分割任务进行期望。我们还设计实验以测试这些技术的有效性，当测试集相对于训练和验证集具有域移位时。

Automatically estimating 3D skeleton, shape, camera viewpoints, and part articulation from sparse in-the-wild image ensembles is a severely under-constrained and challenging problem. Most prior methods rely on large-scale image datasets, dense temporal correspondence, or human annotations like camera pose, 2D keypoints, and shape templates. We propose Hi-LASSIE, which performs 3D articulated reconstruction from only 20-30 online images in the wild without any user-defined shape or skeleton templates. We follow the recent work of LASSIE that tackles a similar problem setting and make two significant advances. First, instead of relying on a manually annotated 3D skeleton, we automatically estimate a class-specific skeleton from the selected reference image. Second, we improve the shape reconstructions with novel instance-specific optimization strategies that allow reconstructions to faithful fit on each instance while preserving the class-specific priors learned across all images. Experiments on in-the-wild image ensembles show that Hi-LASSIE obtains higher quality state-of-the-art 3D reconstructions despite requiring minimum user input.

Code generation models have achieved impressive performance. However, they tend to be brittle as slight edits to a prompt could lead to very different generations; these robustness properties, critical for user experience when deployed in real-life applications, are not well understood. Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation. In this paper, we propose ReCode, a comprehensive robustness evaluation benchmark for code generation models. We customize over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. They are carefully designed to be natural in real-life coding practice, preserve the original semantic meaning, and thus provide multifaceted assessments of a model's robustness performance. With human annotators, we verified that over 90% of the perturbed prompts do not alter the semantic meaning of the original prompt. In addition, we define robustness metrics for code generation models considering the worst-case behavior under each type of perturbation, taking advantage of the fact that executing the generated code can serve as objective evaluation. We demonstrate ReCode on SOTA models using HumanEval, MBPP, as well as function completion tasks derived from them. Interesting observations include: better robustness for CodeGen over InCoder and GPT-J; models are most sensitive to syntax perturbations; more challenging robustness evaluation on MBPP over HumanEval.

Creativity is an indispensable part of human cognition and also an inherent part of how we make sense of the world. Metaphorical abstraction is fundamental in communicating creative ideas through nuanced relationships between abstract concepts such as feelings. While computer vision benchmarks and approaches predominantly focus on understanding and generating literal interpretations of images, metaphorical comprehension of images remains relatively unexplored. Towards this goal, we introduce MetaCLUE, a set of vision tasks on visual metaphor. We also collect high-quality and rich metaphor annotations (abstract objects, concepts, relationships along with their corresponding object boxes) as there do not exist any datasets that facilitate the evaluation of these tasks. We perform a comprehensive analysis of state-of-the-art models in vision and language based on our annotations, highlighting strengths and weaknesses of current approaches in visual metaphor Classification, Localization, Understanding (retrieval, question answering, captioning) and gEneration (text-to-image synthesis) tasks. We hope this work provides a concrete step towards developing AI systems with human-like creative capabilities.

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.

With the increasing use of Graph Neural Networks (GNNs) in critical real-world applications, several post hoc explanation methods have been proposed to understand their predictions. However, there has been no work in generating explanations on the fly during model training and utilizing them to improve the expressive power of the underlying GNN models. In this work, we introduce a novel explanation-directed neural message passing framework for GNNs, EXPASS (EXplainable message PASSing), which aggregates only embeddings from nodes and edges identified as important by a GNN explanation method. EXPASS can be used with any existing GNN architecture and subgraph-optimizing explainer to learn accurate graph embeddings. We theoretically show that EXPASS alleviates the oversmoothing problem in GNNs by slowing the layer wise loss of Dirichlet energy and that the embedding difference between the vanilla message passing and EXPASS framework can be upper bounded by the difference of their respective model weights. Our empirical results show that graph embeddings learned using EXPASS improve the predictive performance and alleviate the oversmoothing problems of GNNs, opening up new frontiers in graph machine learning to develop explanation-based training frameworks.

Deep learning approaches for spatio-temporal prediction problems such as crowd-flow prediction assumes data to be of fixed and regular shaped tensor and face challenges of handling irregular, sparse data tensor. This poses limitations in use-case scenarios such as predicting visit counts of individuals' for a given spatial area at a particular temporal resolution using raster/image format representation of the geographical region, since the movement patterns of an individual can be largely restricted and localized to a certain part of the raster. Additionally, current deep-learning approaches for solving such problem doesn't account for the geographical awareness of a region while modelling the spatio-temporal movement patterns of an individual. To address these limitations, there is a need to develop a novel strategy and modeling approach that can handle both sparse, irregular data while incorporating geo-awareness in the model. In this paper, we make use of quadtree as the data structure for representing the image and introduce a novel geo-aware enabled deep learning layer, GA-ConvLSTM that performs the convolution operation based on a novel geo-aware module based on quadtree data structure for incorporating spatial dependencies while maintaining the recurrent mechanism for accounting for temporal dependencies. We present this approach in the context of the problem of predicting spatial behaviors of an individual (e.g., frequent visits to specific locations) through deep-learning based predictive model, GADST-Predict. Experimental results on two GPS based trace data shows that the proposed method is effective in handling frequency visits over different use-cases with considerable high accuracy.

This paper addresses the problem of position estimation in UAVs operating in a cluttered environment where GPS information is unavailable. A model learning-based approach is proposed that takes in the rotor RPMs and past state as input and predicts the one-step-ahead position of the UAV using a novel spectral-normalized memory neural network (SN-MNN). The spectral normalization guarantees stable and reliable prediction performance. The predicted position is transformed to global coordinate frame which is then fused along with the odometry of other peripheral sensors like IMU, barometer, compass etc., using the onboard extended Kalman filter to estimate the states of the UAV. The experimental flight data collected from a motion capture facility using a micro-UAV is used to train the SN-MNN. The PX4-ECL library is used to replay the flight data using the proposed algorithm, and the estimated position is compared with actual ground truth data. The proposed algorithm doesn't require any additional onboard sensors, and is computationally light. The performance of the proposed approach is compared with the current state-of-art GPS-denied algorithms, and it can be seen that the proposed algorithm has the least RMSE for position estimates.