已经提出了多个草图数据集，以了解人们如何绘制3D对象。但是，这样的数据集通常是小规模的，并且覆盖了一小部分对象或类别。此外，这些数据集包含大多来自专家用户的徒手草图，因此很难比较专家和新手用户的图纸，而这种比较对于告知对任何一个用户组的基于草图的界面更为有效的接口至关重要。这些观察结果激发了我们分析具有和没有足够绘图技能的人的不同程度的素描3D对象。我们邀请了70个新手用户和38位专家用户素描136 3D对象，这些对象是从多个视图中呈现的362张图像。这导致了3,620个徒手多视图草图的新数据集，在某些视图下，它们在其相应的3D对象上注册。我们的数据集比现有数据集大的数量级。我们在三个级别（即在空间和时间特征下以及跨越创建者组的内部和范围内）分析了三个级别的收集数据。我们发现，专业人士和新手的图纸在本质和外在的中风级别上显示出显着差异。我们在两个应用程序中演示了数据集的有用性：（i）徒手式的草图合成，（ii）将其作为基于草图的3D重建的潜在基准。我们的数据集和代码可在https://chufengxiao.github.io/differsketching/上获得。

联合学习（FL）支持地理分布式设备的培训模型。然而，传统的FL系统采用集中式同步策略，提高了高通信压力和模型泛化挑战。 FL的现有优化未能加速异构设备的培训或遭受差的通信效率。在本文中，我们提出了一个支持在异构设备上分散的异步训练的框架的Hadfl。使用本地数据的异质性感知本地步骤本地培训设备。在每个聚合循环中，基于执行模型同步和聚合的概率来选择它们。与传统的FL系统相比，HADFL可以减轻中心服务器的通信压力，有效地利用异构计算能力，并且可以分别实现比Pytorch分布式训练方案分别的最大加速度为3.15倍，而不是Pytorch分布式训练方案，几乎没有损失收敛准确性。

探索和建立具有电生理特征和高计算效率的人工神经网络是计算机视觉领域的流行主题。受主要视觉皮层的工作机制的启发，脉冲耦合神经网络（PCNN）可以表现出同步振荡，难治期和指数衰减的特征。然而，电生理证据表明，当外部周期性信号刺激时，神经元表现出高度复杂的非线性动力学。这种混乱现象，也称为“蝴蝶效应”，无法用所有PCNN模型来解释。在这项工作中，我们分析了防止PCNN模型模仿真实主要视觉皮层的主要障碍。我们认为神经元激发是一个随机过程。然后，我们提出了一个新型的神经网络，称为连续耦合神经网络（CCNN）。理论分析表明，CCNN的动态行为与PCNN不同。数值结果表明，CCNN模型在直流刺激下表现出周期性的行为，并在交流刺激下表现出混沌行为，这与实际神经元的结果一致。此外，分析了CCNN模型的图像和视频处理机制。图像分割的实验结果表明，CCNN模型的性能要比视觉皮层神经网络模型的最先进。

Theoretical properties of bilevel problems are well studied when the lower-level problem is strongly convex. In this work, we focus on bilevel optimization problems without the strong-convexity assumption. In these cases, we first show that the common local optimality measures such as KKT condition or regularization can lead to undesired consequences. Then, we aim to identify the mildest conditions that make bilevel problems tractable. We identify two classes of growth conditions on the lower-level objective that leads to continuity. Under these assumptions, we show that the local optimality of the bilevel problem can be defined via the Goldstein stationarity condition of the hyper-objective. We then propose the Inexact Gradient-Free Method (IGFM) to solve the bilevel problem, using an approximate zeroth order oracle that is of independent interest. Our non-asymptotic analysis demonstrates that the proposed method can find a $(\delta, \varepsilon)$ Goldstein stationary point for bilevel problems with a zeroth order oracle complexity that is polynomial in $d, 1/\delta$ and $1/\varepsilon$.

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.

Stance detection refers to the task of extracting the standpoint (Favor, Against or Neither) towards a target in given texts. Such research gains increasing attention with the proliferation of social media contents. The conventional framework of handling stance detection is converting it into text classification tasks. Deep learning models have already replaced rule-based models and traditional machine learning models in solving such problems. Current deep neural networks are facing two main challenges which are insufficient labeled data and information in social media posts and the unexplainable nature of deep learning models. A new pre-trained language model chatGPT was launched on Nov 30, 2022. For the stance detection tasks, our experiments show that ChatGPT can achieve SOTA or similar performance for commonly used datasets including SemEval-2016 and P-Stance. At the same time, ChatGPT can provide explanation for its own prediction, which is beyond the capability of any existing model. The explanations for the cases it cannot provide classification results are especially useful. ChatGPT has the potential to be the best AI model for stance detection tasks in NLP, or at least change the research paradigm of this field. ChatGPT also opens up the possibility of building explanatory AI for stance detection.

In the field of cross-modal retrieval, single encoder models tend to perform better than dual encoder models, but they suffer from high latency and low throughput. In this paper, we present a dual encoder model called BagFormer that utilizes a cross modal interaction mechanism to improve recall performance without sacrificing latency and throughput. BagFormer achieves this through the use of bag-wise interactions, which allow for the transformation of text to a more appropriate granularity and the incorporation of entity knowledge into the model. Our experiments demonstrate that BagFormer is able to achieve results comparable to state-of-the-art single encoder models in cross-modal retrieval tasks, while also offering efficient training and inference with 20.72 times lower latency and 25.74 times higher throughput.

Body Mass Index (BMI), age, height and weight are important indicators of human health conditions, which can provide useful information for plenty of practical purposes, such as health care, monitoring and re-identification. Most existing methods of health indicator prediction mainly use front-view body or face images. These inputs are hard to be obtained in daily life and often lead to the lack of robustness for the models, considering their strict requirements on view and pose. In this paper, we propose to employ gait videos to predict health indicators, which are more prevalent in surveillance and home monitoring scenarios. However, the study of health indicator prediction from gait videos using deep learning was hindered due to the small amount of open-sourced data. To address this issue, we analyse the similarity and relationship between pose estimation and health indicator prediction tasks, and then propose a paradigm enabling deep learning for small health indicator datasets by pre-training on the pose estimation task. Furthermore, to better suit the health indicator prediction task, we bring forward Global-Local Aware aNd Centrosymmetric Encoder (GLANCE) module. It first extracts local and global features by progressive convolutions and then fuses multi-level features by a centrosymmetric double-path hourglass structure in two different ways. Experiments demonstrate that the proposed paradigm achieves state-of-the-art results for predicting health indicators on MoVi, and that the GLANCE module is also beneficial for pose estimation on 3DPW.

Image manipulation localization aims at distinguishing forged regions from the whole test image. Although many outstanding prior arts have been proposed for this task, there are still two issues that need to be further studied: 1) how to fuse diverse types of features with forgery clues; 2) how to progressively integrate multistage features for better localization performance. In this paper, we propose a tripartite progressive integration network (TriPINet) for end-to-end image manipulation localization. First, we extract both visual perception information, e.g., RGB input images, and visual imperceptible features, e.g., frequency and noise traces for forensic feature learning. Second, we develop a guided cross-modality dual-attention (gCMDA) module to fuse different types of forged clues. Third, we design a set of progressive integration squeeze-and-excitation (PI-SE) modules to improve localization performance by appropriately incorporating multiscale features in the decoder. Extensive experiments are conducted to compare our method with state-of-the-art image forensics approaches. The proposed TriPINet obtains competitive results on several benchmark datasets.

Domain adaptation aims to transfer the knowledge acquired by models trained on (data-rich) source domains to (low-resource) target domains, for which a popular method is invariant representation learning. While they have been studied extensively for classification and regression problems, how they apply to ranking problems, where the data and metrics have a list structure, is not well understood. Theoretically, we establish a domain adaptation generalization bound for ranking under listwise metrics such as MRR and NDCG. The bound suggests an adaptation method via learning list-level domain-invariant feature representations, whose benefits are empirically demonstrated by unsupervised domain adaptation experiments on real-world ranking tasks, including passage reranking. A key message is that for domain adaptation, the representations should be analyzed at the same level at which the metric is computed, as we show that learning invariant representations at the list level is most effective for adaptation on ranking problems.