Remote sensing imagery provides comprehensive views of the Earth, where different sensors collect complementary data at different spatial scales. Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks with imagery from a range of spatial scales. Such models overlook scale-specific information in the data. In this paper, we present Scale-MAE, a pretraining method that explicitly learns relationships between data at different, known scales throughout the pretraining process. Scale-MAE pretrains a network by masking an input image at a known input scale, where the area of the Earth covered by the image determines the scale of the ViT positional encoding, not the image resolution. Scale-MAE encodes the masked image with a standard ViT backbone, and then decodes the masked image through a bandpass filter to reconstruct low/high frequency images at lower/higher scales. We find that tasking the network with reconstructing both low/high frequency images leads to robust multiscale representations for remote sensing imagery. Scale-MAE achieves an average of a $5.0\%$ non-parametric kNN classification improvement across eight remote sensing datasets compared to current state-of-the-art and obtains a $0.9$ mIoU to $3.8$ mIoU improvement on the SpaceNet building segmentation transfer task for a range of evaluation scales.

Are extralinguistic signals such as image pixels crucial for inducing constituency grammars? While past work has shown substantial gains from multimodal cues, we investigate whether such gains persist in the presence of rich information from large language models (LLMs). We find that our approach, LLM-based C-PCFG (LC-PCFG), outperforms previous multi-modal methods on the task of unsupervised constituency parsing, achieving state-of-the-art performance on a variety of datasets. Moreover, LC-PCFG results in an over 50% reduction in parameter count, and speedups in training time of 1.7x for image-aided models and more than 5x for video-aided models, respectively. These results challenge the notion that extralinguistic signals such as image pixels are needed for unsupervised grammar induction, and point to the need for better text-only baselines in evaluating the need of multi-modality for the task.

Action recognition models have achieved impressive results by incorporating scene-level annotations, such as objects, their relations, 3D structure, and more. However, obtaining annotations of scene structure for videos requires a significant amount of effort to gather and annotate, making these methods expensive to train. In contrast, synthetic datasets generated by graphics engines provide powerful alternatives for generating scene-level annotations across multiple tasks. In this work, we propose an approach to leverage synthetic scene data for improving video understanding. We present a multi-task prompt learning approach for video transformers, where a shared video transformer backbone is enhanced by a small set of specialized parameters for each task. Specifically, we add a set of ``task prompts'', each corresponding to a different task, and let each prompt predict task-related annotations. This design allows the model to capture information shared among synthetic scene tasks as well as information shared between synthetic scene tasks and a real video downstream task throughout the entire network. We refer to this approach as ``Promptonomy'', since the prompts model a task-related structure. We propose the PromptonomyViT model (PViT), a video transformer that incorporates various types of scene-level information from synthetic data using the ``Promptonomy'' approach. PViT shows strong performance improvements on multiple video understanding tasks and datasets.

Shape can specify key object constraints, yet existing text-to-image diffusion models ignore this cue and synthesize objects that are incorrectly scaled, cut off, or replaced with background content. We propose a training-free method, Shape-Guided Diffusion, which uses a novel Inside-Outside Attention mechanism to constrain the cross-attention (and self-attention) maps such that prompt tokens (and pixels) referring to the inside of the shape cannot attend outside the shape, and vice versa. To demonstrate the efficacy of our method, we propose a new image editing task where the model must replace an object specified by its mask and a text prompt. We curate a new ShapePrompts benchmark based on MS-COCO and achieve SOTA results in shape faithfulness, text alignment, and realism according to both quantitative metrics and human preferences. Our data and code will be made available at https://shape-guided-diffusion.github.io.

We demonstrate how language can improve geolocation: the task of predicting the location where an image was taken. Here we study explicit knowledge from human-written guidebooks that describe the salient and class-discriminative visual features humans use for geolocation. We propose the task of Geolocation via Guidebook Grounding that uses a dataset of StreetView images from a diverse set of locations and an associated textual guidebook for GeoGuessr, a popular interactive geolocation game. Our approach predicts a country for each image by attending over the clues automatically extracted from the guidebook. Supervising attention with country-level pseudo labels achieves the best performance. Our approach substantially outperforms a state-of-the-art image-only geolocation method, with an improvement of over 5% in Top-1 accuracy. Our dataset and code can be found at https://github.com/g-luo/geolocation_via_guidebook_grounding.

Prompt Tuning, conditioning on task-specific learned prompt vectors, has emerged as a data-efficient and parameter-efficient method for adapting large pretrained vision-language models to multiple downstream tasks. However, existing approaches usually consider learning prompt vectors for each task independently from scratch, thereby failing to exploit the rich shareable knowledge across different vision-language tasks. In this paper, we propose multitask vision-language prompt tuning (MVLPT), which incorporates cross-task knowledge into prompt tuning for vision-language models. Specifically, (i) we demonstrate the effectiveness of learning a single transferable prompt from multiple source tasks to initialize the prompt for each target task; (ii) we show many target tasks can benefit each other from sharing prompt vectors and thus can be jointly learned via multitask prompt tuning. We benchmark the proposed MVLPT using three representative prompt tuning methods, namely text prompt tuning, visual prompt tuning, and the unified vision-language prompt tuning. Results in 20 vision tasks demonstrate that the proposed approach outperforms all single-task baseline prompt tuning methods, setting the new state-of-the-art on the few-shot ELEVATER benchmarks and cross-task generalization benchmarks. To understand where the cross-task knowledge is most effective, we also conduct a large-scale study on task transferability with 20 vision tasks in 400 combinations for each prompt tuning method. It shows that the most performant MVLPT for each prompt tuning method prefers different task combinations and many tasks can benefit each other, depending on their visual similarity and label similarity. Code is available at https://github.com/sIncerass/MVLPT.

分散的多基金会计划一直是机器人技术研究的重要领域。该领域中有趣且有影响力的应用是在未结构化的道路环境中分散的车辆协调。例如，在十字路口中，在没有中央协调员的情况下，在相交路径的多个车辆上解除多种车辆是有用的。我们从常识中学到的是，要使车辆浏览这种未建筑的环境，驾驶员必须理解并符合附近驾驶员观察到的隐式“社会礼节”。为了研究这种隐式驾驶协议，我们收集了伯克利DeepDrive无人机数据集。该数据集包含1）一组航空视频记录未结构化驾驶，2）图像和注释的集合来训练车辆检测模型，3）一个用于说明典型用法的开发脚本套件。我们认为，该数据集是研究人类驱动因素和次要兴趣的分散多种计划的主要兴趣，用于遥感环境中的计算机视觉。

自我监督的表示学习的最新趋势集中在消除训练管道中的归纳偏见。但是，当可用数据有限时，归纳偏差在设置中可能很有用，或者提供对基础数据分布的更多见解。我们提出了空间注意（SPAN），该框架利用未标记的图像数据集中使用一致的空间和语义结构来指导视觉变压器的注意。 SPAN通过将注意力面罩从单独的变压器头正规化，以跟随语义区域的各个先验。这些先验可以从数据统计数据或域专家提供的单个标记样本中得出。我们研究了几种详细的现实情况，包括医学图像分析和视觉质量保证。我们发现，所产生的注意力面膜比从域 - 不合义预审进的掩码更容易解​​释。 SPAN可为肺和心脏分割产生58.7的地图改进。我们还发现，与结构域 - 不合稳定的预处理相比，我们的方法在将验证的模型转移到下游胸部疾病分类任务时会产生2.2个MAUC的改善。最后，我们表明，与域 - 不可屈服的预处理相比，跨越预处理会导致低数据表格中的下游分类性能更高。

在这项工作中，我们研究了生成图像模型的性能和评估如何受到其培训数据集的种族组成的影响。通过检查和控制各种培训数据集中的种族分布，我们能够观察不同培训分布对生成的图像质量和生成图像的种族分布的影响。我们的结果表明，生成的图像的种族组成成功地保留了培训数据。但是，我们观察到截断是一种用于在推断过程中生成更高质量图像的技术，加剧了数据中的种族失衡。最后，在检查图像质量与种族之间的关系时，我们发现给定种族的最高可感知的视觉质量图像来自该种族代表性很好的分布，并且注释者始终偏爱白人的生成图像，而不是黑人。

一个人如何在没有特定任务的固定或任何模型修改的情况下将预训练的视觉模型调整为新颖的下游任务？受到NLP提示的启发，本文研究了视觉提示：在测试时间和新输入图像时，给定的输入输出图像示例示例，目标是自动生成输出图像，与给定的示例一致。我们表明，将这个问题作为简单的图像插入，实际上只是填充了串联的视觉提示图像中的一个孔 - 只要已经对正确的数据训练了介入算法，就非常有效。我们在我们策划的新数据集上训练蒙面的自动编码器-88K未标记的数字来自ARXIV上的学术报纸来源。我们将视觉提示应用于这些预处理的模型，并在各种下游图像到图像任务上展示结果，包括前景分割，单个对象检测，着色，边缘检测等。