Stack Overflow是最受欢迎的编程社区之一，开发人员可以为他们遇到的问题寻求帮助。然而，如果没有经验的开发人员无法清楚地描述他们的问题，那么他们很难吸引足够的关注并获得预期的答案。我们提出了M $ _3 $ NSCT5，这是一种自动从给定代码片段生成多个帖子标题的新颖方法。开发人员可以使用生成的标题查找密切相关的帖子并完成其问题描述。 M $ _3 $ NSCT5使用Codet5骨干，这是一种具有出色语言理解和发电能力的预训练的变压器模型。为了减轻歧义问题，即在不同背景下可以将相同的代码片段与不同的标题保持一致，我们提出了最大的边缘多元核抽样策略，以一次产生多个高质量和不同的标题候选者，以便开发人员选择。我们构建了一个大规模数据集，其中包含890,000个问题帖子，其中涵盖了八种编程语言，以验证M $ _3 $ NSCT5的有效性。 BLEU和胭脂指标的自动评估结果表明，M $ _3 $ NSCT5的优势比六个最先进的基线模型。此外，具有值得信赖结果的人类评估也证明了我们对现实世界应用方法的巨大潜力。

旨在恢复图像中影子区域的原始强度，并使它们与剩余的非阴影区域兼容，而没有跟踪，删除阴影是一个非常具有挑战性的问题，使许多下游图像/视频相关的任务受益。最近，变形金刚通过捕获全局像素相互作用来显示它们在各种应用中的强大能力，并且这种能力在删除阴影时非常可取。然而，由于以下两个原因，应用变压器促进阴影去除是非平凡的：1）修补程序操作不适用于由于不规则的阴影形状而导致阴影去除； 2）阴影去除只需要从非阴影区域到阴影区域的单向交互，而不是图像中所有像素之间的共同双向相互作用。在本文中，我们提出了一种新型的跨区域变压器，即CRFormer，用于去除阴影，它与现有变压器的不同之处仅通过考虑从非阴影区域到阴影区域的像素相互作用而不将图像分为斑块。这是通过精心设计的区域感知的跨注意操作来实现的，该操作可以汇总以非阴影区域特征为条件的恢复的阴影区域特征。与其他最先进的方法相比，关于ISTD，AISTD，SRD和视频阴影删除数据集的广泛实验证明了我们方法的优势。

去耦时尚表示是指将空间和时间特征分解成尺寸无关的因素。尽管以前的基于RGB-D的运动识别方法通过紧密耦合的多模态时空表示来实现了有希望的性能，但由于紧密的时空缠绕的建模，它们仍然在小数据设置下遭受（i）优化困难;（ii）信息冗余通常包含与分类弱相关的大量边际信息; （iii）由晚期融合不足引起的多模态起峰型信息之间的低相互作用。为了缓解这些缺点，我们建议去除并循环基于RGB-D的运动识别的时空表示。具体而言，我们解开了学习时空表示的任务到3个子任务：（1）通过解耦的空间和时间建模网络学习高质量和维度独立特征。 （2）重新汇总解耦表示，以确定更强的时空依赖。 （3）引入跨型自适应后融合（CAPF）机制，用于从RGB-D数据捕获跨模态时空信息。这些新颖设计的无缝组合形成了强大的时空表示，而不是在四个公共运动数据集上的最先进的方法实现了更好的性能。我们的代码可在https://github.com/damo-cv/motionrgbd获得。

最近联合学习（FL）范式的潜在假设是本地模型通常与全局模型共享与全局模型相同的网络架构，这对于具有不同的硬件和基础架构的移动和IOT设备变得不切实际。可扩展的联合学习框架应该解决配备不同计算和通信功能的异构客户端。为此，本文提出了一种新的联合模型压缩框架，它将异构低级模型分配给客户端，然后将它们聚合到全局全级模型中。我们的解决方案使得能够培训具有不同计算复杂性的异构本地模型，并汇总单个全局模型。此外，FEDHM不仅降低了设备的计算复杂性，而且还通过使用低秩模型来降低通信成本。广泛的实验结果表明，我们提出的\ System在测试顶-1精度（平均精度4.6％的精度增益）方面优于现行修剪的液体方法，在各种异构流域下较小的型号尺寸（平均较小为1.5倍） 。

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. We give an $(\epsilon_{\text{dp}}, \delta)$-differentially private algorithm which, given $n$ samples of Lipschitz loss functions, obtains near-optimal optimization error and makes $\min(n, n^2\epsilon_{\text{dp}}^2 d^{-1}) + \min(n^{4/3}\epsilon_{\text{dp}}^{1/3}, (nd)^{2/3}\epsilon_{\text{dp}}^{-1})$ queries to the gradients of these functions. In the regime $d \le n \epsilon_{\text{dp}}^{2}$, where privacy comes at no cost in terms of the optimal loss up to constants, our algorithm uses $n + (nd)^{2/3}\epsilon_{\text{dp}}^{-1}$ queries and improves recent advancements of [KLL21, AFKT21]. In the moderately low-dimensional setting $d \le \sqrt n \epsilon_{\text{dp}}^{3/2}$, our query complexity is near-linear.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.