形状通知如何将对象掌握，无论是如何以及如何。因此，本文介绍了一种基于分割的架构，用于将用深度摄像机进行分解为多个基本形状的对象，以及用于机器人抓握的后处理管道。分段采用深度网络，称为PS-CNN，在具有6个类的原始形状和使用模拟引擎生成的合成数据上培训。每个原始形状都设计有参数化掌握家族，允许管道识别每个形状区域的多个掌握候选者。掌握是排序的排名，选择用于执行的第一个可行的。对于无任务掌握单个对象，该方法达到94.2％的成功率将其放置在顶部执行掌握方法中，与自上而下和SE（3）基础相比。涉及变量观点和杂波的其他测试展示了设置的鲁棒性。对于面向任务的掌握，PS-CNN实现了93.0％的成功率。总体而言，结果支持该假设，即在抓地管道内明确地编码形状原语应该提高掌握性能，包括无任务和任务相关的掌握预测。

当代掌握检测方法采用深度学习，实现传感器和物体模型不确定性的鲁棒性。这两个主导的方法设计了掌握质量评分或基于锚的掌握识别网络。本文通过将其视为图像空间中的关键点检测来掌握掌握检测的不同方法。深网络检测每个掌握候选者作为一对关键点，可转换为掌握代表= {x，y，w，{\ theta}} t，而不是转角点的三态或四重奏。通过将关键点分组成对来降低检测难度提高性能。为了促进捕获关键点之间的依赖关系，将非本地模块结合到网络设计中。基于离散和连续定向预测的最终过滤策略消除了错误的对应关系，并进一步提高了掌握检测性能。此处提出的方法GKNET在康奈尔和伸缩的提花数据集上的精度和速度之间实现了良好的平衡（在41.67和23.26 fps的96.9％和98.39％）之间。操纵器上的后续实验使用4种类型的抓取实验来评估GKNet，反映不同滋扰的速度：静态抓握，动态抓握，在各种相机角度抓住，夹住。 GKNet优于静态和动态掌握实验中的参考基线，同时表现出变化的相机观点和中度杂波的稳健性。结果证实了掌握关键点是深度掌握网络的有效输出表示的假设，为预期的滋扰因素提供鲁棒性。

A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods mainly focus on source-specific and stationary observational data. Such learning strategies assume that all observational data are already available during the training phase and from only one source. This practical concern of accessibility is ubiquitous in various academic and industrial applications. That's what it boiled down to: in the era of big data, we face new challenges in causal inference with observational data, i.e., the extensibility for incrementally available observational data, the adaptability for extra domain adaptation problem except for the imbalance between treatment and control groups, and the accessibility for an enormous amount of data. In this position paper, we formally define the problem of continual treatment effect estimation, describe its research challenges, and then present possible solutions to this problem. Moreover, we will discuss future research directions on this topic.

Text-based speech editing allows users to edit speech by intuitively cutting, copying, and pasting text to speed up the process of editing speech. In the previous work, CampNet (context-aware mask prediction network) is proposed to realize text-based speech editing, significantly improving the quality of edited speech. This paper aims at a new task: adding emotional effect to the editing speech during the text-based speech editing to make the generated speech more expressive. To achieve this task, we propose Emo-CampNet (emotion CampNet), which can provide the option of emotional attributes for the generated speech in text-based speech editing and has the one-shot ability to edit unseen speakers' speech. Firstly, we propose an end-to-end emotion-selectable text-based speech editing model. The key idea of the model is to control the emotion of generated speech by introducing additional emotion attributes based on the context-aware mask prediction network. Secondly, to prevent the emotion of the generated speech from being interfered by the emotional components in the original speech, a neutral content generator is proposed to remove the emotion from the original speech, which is optimized by the generative adversarial framework. Thirdly, two data augmentation methods are proposed to enrich the emotional and pronunciation information in the training set, which can enable the model to edit the unseen speaker's speech. The experimental results that 1) Emo-CampNet can effectively control the emotion of the generated speech in the process of text-based speech editing; And can edit unseen speakers' speech. 2) Detailed ablation experiments further prove the effectiveness of emotional selectivity and data augmentation methods. The demo page is available at https://hairuo55.github.io/Emo-CampNet/

In this work, we study the black-box targeted attack problem from the model discrepancy perspective. On the theoretical side, we present a generalization error bound for black-box targeted attacks, which gives a rigorous theoretical analysis for guaranteeing the success of the attack. We reveal that the attack error on a target model mainly depends on empirical attack error on the substitute model and the maximum model discrepancy among substitute models. On the algorithmic side, we derive a new algorithm for black-box targeted attacks based on our theoretical analysis, in which we additionally minimize the maximum model discrepancy(M3D) of the substitute models when training the generator to generate adversarial examples. In this way, our model is capable of crafting highly transferable adversarial examples that are robust to the model variation, thus improving the success rate for attacking the black-box model. We conduct extensive experiments on the ImageNet dataset with different classification models, and our proposed approach outperforms existing state-of-the-art methods by a significant margin. Our codes will be released.

Event-based simulations of Spiking Neural Networks (SNNs) are fast and accurate. However, they are rarely used in the context of event-based gradient descent because their implementations on GPUs are difficult. Discretization with the forward Euler method is instead often used with gradient descent techniques but has the disadvantage of being computationally expensive. Moreover, the lack of precision of discretized simulations can create mismatches between the simulated models and analog neuromorphic hardware. In this work, we propose a new exact error-backpropagation through spikes method for SNNs, extending Fast \& Deep to multiple spikes per neuron. We show that our method can be efficiently implemented on GPUs in a fully event-based manner, making it fast to compute and precise enough for analog neuromorphic hardware. Compared to the original Fast \& Deep and the current state-of-the-art event-based gradient-descent algorithms, we demonstrate increased performance on several benchmark datasets with both feedforward and convolutional SNNs. In particular, we show that multi-spike SNNs can have advantages over single-spike networks in terms of convergence, sparsity, classification latency and sensitivity to the dead neuron problem.

The error Backpropagation algorithm (BP) is a key method for training deep neural networks. While performant, it is also resource-demanding in terms of computation, memory usage and energy. This makes it unsuitable for online learning on edge devices that require a high processing rate and low energy consumption. More importantly, BP does not take advantage of the parallelism and local characteristics offered by dedicated neural processors. There is therefore a demand for alternative algorithms to BP that could improve the latency, memory requirements, and energy footprint of neural networks on hardware. In this work, we propose a novel method based on Direct Feedback Alignment (DFA) which uses Forward-Mode Automatic Differentiation to estimate backpropagation paths and learn feedback connections in an online manner. We experimentally show that Directional DFA achieves performances that are closer to BP than other feedback methods on several benchmark datasets and architectures while benefiting from the locality and parallelization characteristics of DFA. Moreover, we show that, unlike other feedback learning algorithms, our method provides stable learning for convolution layers.

Copy-Paste is a simple and effective data augmentation strategy for instance segmentation. By randomly pasting object instances onto new background images, it creates new training data for free and significantly boosts the segmentation performance, especially for rare object categories. Although diverse, high-quality object instances used in Copy-Paste result in more performance gain, previous works utilize object instances either from human-annotated instance segmentation datasets or rendered from 3D object models, and both approaches are too expensive to scale up to obtain good diversity. In this paper, we revisit Copy-Paste at scale with the power of newly emerged zero-shot recognition models (e.g., CLIP) and text2image models (e.g., StableDiffusion). We demonstrate for the first time that using a text2image model to generate images or zero-shot recognition model to filter noisily crawled images for different object categories is a feasible way to make Copy-Paste truly scalable. To make such success happen, we design a data acquisition and processing framework, dubbed "X-Paste", upon which a systematic study is conducted. On the LVIS dataset, X-Paste provides impressive improvements over the strong baseline CenterNet2 with Swin-L as the backbone. Specifically, it archives +2.6 box AP and +2.1 mask AP gains on all classes and even more significant gains with +6.8 box AP +6.5 mask AP on long-tail classes.

Traditional learning-based approaches to student modeling (e.g., predicting grades based on measured activities) generalize poorly to underrepresented/minority student groups due to biases in data availability. In this paper, we propose a Multi-Layer Personalized Federated Learning (MLPFL) methodology which optimizes inference accuracy over different layers of student grouping criteria, such as by course and by demographic subgroups within each course. In our approach, personalized models for individual student subgroups are derived from a global model, which is trained in a distributed fashion via meta-gradient updates that account for subgroup heterogeneity while preserving modeling commonalities that exist across the full dataset. To evaluate our methodology, we consider case studies of two popular downstream student modeling tasks, knowledge tracing and outcome prediction, which leverage multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums) in model training. Experiments on three real-world datasets from online courses demonstrate that our approach obtains substantial improvements over existing student modeling baselines in terms of increasing the average and decreasing the variance of prediction quality across different student subgroups. Visual analysis of the resulting students' knowledge state embeddings confirm that our personalization methodology extracts activity patterns which cluster into different student subgroups, consistent with the performance enhancements we obtain over the baselines.

The tradeoff between performance and inference speed is critical for practical applications. Architecture reparameterization obtains better tradeoffs and it is becoming an increasingly popular ingredient in modern convolutional neural networks. Nonetheless, its quantization performance is usually too poor to deploy (e.g. more than 20% top-1 accuracy drop on ImageNet) when INT8 inference is desired. In this paper, we dive into the underlying mechanism of this failure, where the original design inevitably enlarges quantization error. We propose a simple, robust, and effective remedy to have a quantization-friendly structure that also enjoys reparameterization benefits. Our method greatly bridges the gap between INT8 and FP32 accuracy for RepVGG. Without bells and whistles, the top-1 accuracy drop on ImageNet is reduced within 2\% by standard post-training quantization.