来自LIDAR或相机传感器的3D对象检测任务对于自动驾驶至关重要。先锋尝试多模式融合的尝试补充了稀疏的激光雷达点云，其中包括图像的丰富语义纹理信息，以额外的网络设计和开销为代价。在这项工作中，我们提出了一个名为SPNET的新型语义传递框架，以通过丰富的上下文绘画的指导来提高现有基于激光雷达的3D检测模型的性能，在推理过程中没有额外的计算成本。我们的关键设计是首先通过训练语义绘制的教师模型来利用地面真实标签中潜在的指导性语义知识，然后引导纯LIDAR网络通过不同的粒度传播模块来学习语义绘制的表示：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类：类别：类别：类别：类别：类别：类别：类别： - 通过，像素的传递和实例传递。实验结果表明，所提出的SPNET可以与大多数现有的3D检测框架无缝合作，其中AP增益为1〜5％，甚至在KITTI测试基准上实现了新的最新3D检测性能。代码可在以下网址获得：https：//github.com/jb892/sp​​net。

低成本单眼的3D对象检测在自主驾驶中起着基本作用，而其精度仍然远非令人满意。在本文中，我们挖掘了3D对象检测任务，并将其重构为对象本地化和外观感知的子任务，这有​​利于整个任务的互惠信息的深度挖掘。我们介绍了一个名为DFR-Net的动态特征反射网络，其中包含两种新的独立模块：（i）首先将任务特征分开的外观定位特征反射模块（ALFR），然后自相互反映互核特征; （ii）通过自学习方式自适应地重建各个子任务的培训过程的动态内部交易模块（DIT）。关于挑战基蒂数据集的广泛实验证明了DFR网的有效性和泛化。我们在基蒂测试集中的所有单眼3D对象探测器中排名第一（直到2021年3月16日）。所提出的方法在许多尖端的3D检测框架中也容易在较忽略的成本下以忽略的成本来播放。该代码将公开可用。

由于LIDAR传感器捕获的精确深度信息缺乏准确的深度信息，单眼3D对象检测是一个关键而挑战的自主驾驶任务。在本文中，我们提出了一种立体引导的单目3D对象检测网络，称为SGM3D，其利用立体图像提取的鲁棒3D特征来增强从单眼图像中学到的特征。我们创新地研究了多粒度域适配模块（MG-DA）以利用网络的能力，以便仅基于单手套提示产生立体模拟功能。利用粗均衡特征级以及精细锚级域适配，以引导单眼分支。我们介绍了一个基于IOO匹配的对齐模块（iou-ma），用于立体声和单眼域之间的对象级域适应，以减轻先前阶段中的不匹配。我们对最具挑战性的基蒂和Lyft数据集进行了广泛的实验，并实现了新的最先进的性能。此外，我们的方法可以集成到许多其他单眼的方法中以提高性能而不引入任何额外的计算成本。

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs. While much of the literature has focused on discounted MDPs, robust average-reward MDPs remain largely unexplored. In this paper, we focus on robust average-reward MDPs, where the goal is to find a policy that optimizes the worst-case average reward over an uncertainty set. We first take an approach that approximates average-reward MDPs using discounted MDPs. We prove that the robust discounted value function converges to the robust average-reward as the discount factor $\gamma$ goes to $1$, and moreover, when $\gamma$ is large, any optimal policy of the robust discounted MDP is also an optimal policy of the robust average-reward. We further design a robust dynamic programming approach, and theoretically characterize its convergence to the optimum. Then, we investigate robust average-reward MDPs directly without using discounted MDPs as an intermediate step. We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.

Medical image segmentation (MIS) is essential for supporting disease diagnosis and treatment effect assessment. Despite considerable advances in artificial intelligence (AI) for MIS, clinicians remain skeptical of its utility, maintaining low confidence in such black box systems, with this problem being exacerbated by low generalization for out-of-distribution (OOD) data. To move towards effective clinical utilization, we propose a foundation model named EvidenceCap, which makes the box transparent in a quantifiable way by uncertainty estimation. EvidenceCap not only makes AI visible in regions of uncertainty and OOD data, but also enhances the reliability, robustness, and computational efficiency of MIS. Uncertainty is modeled explicitly through subjective logic theory to gather strong evidence from features. We show the effectiveness of EvidenceCap in three segmentation datasets and apply it to the clinic. Our work sheds light on clinical safe applications and explainable AI, and can contribute towards trustworthiness in the medical domain.

Vertical Federated Learning (VFL) is widely utilized in real-world applications to enable collaborative learning while protecting data privacy and safety. However, previous works show that parties without labels (passive parties) in VFL can infer the sensitive label information owned by the party with labels (active party) or execute backdoor attacks to VFL. Meanwhile, active party can also infer sensitive feature information from passive party. All these pose new privacy and security challenges to VFL systems. We propose a new general defense method which limits the mutual information between private raw data, including both features and labels, and intermediate outputs to achieve a better trade-off between model utility and privacy. We term this defense Mutual Information Regularization Defense (MID). We theoretically and experimentally testify the effectiveness of our MID method in defending existing attacks in VFL, including label inference attacks, backdoor attacks and feature reconstruction attacks.

Video semantic segmentation (VSS) is beneficial for dealing with dynamic scenes due to the continuous property of the real-world environment. On the one hand, some methods alleviate the predicted inconsistent problem between continuous frames. On the other hand, other methods employ the previous frame as the prior information to assist in segmenting the current frame. Although the previous methods achieve superior performances on the independent and identically distributed (i.i.d) data, they can not generalize well on other unseen domains. Thus, we explore a new task, the video generalizable semantic segmentation (VGSS) task that considers both continuous frames and domain generalization. In this paper, we propose a class-wise non-salient region generalized (CNSG) framework for the VGSS task. Concretely, we first define the class-wise non-salient feature, which describes features of the class-wise non-salient region that carry more generalizable information. Then, we propose a class-wise non-salient feature reasoning strategy to select and enhance the most generalized channels adaptively. Finally, we propose an inter-frame non-salient centroid alignment loss to alleviate the predicted inconsistent problem in the VGSS task. We also extend our video-based framework to the image-based generalizable semantic segmentation (IGSS) task. Experiments demonstrate that our CNSG framework yields significant improvement in the VGSS and IGSS tasks.

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction.

We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decodert takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixel-level and token-level outputs in the same semantic space. With such a novel design, X-Decoder is the first work that provides a unified way to support all types of image segmentation and a variety of vision-language (VL) tasks. Further, our design enables seamless interactions across tasks at different granularities and brings mutual benefits by learning a common and rich pixel-level visual-semantic understanding space, without any pseudo-labeling. After pretraining on a mixed set of a limited amount of segmentation data and millions of image-text pairs, X-Decoder exhibits strong transferability to a wide range of downstream tasks in both zero-shot and finetuning settings. Notably, it achieves (1) state-of-the-art results on open-vocabulary segmentation and referring segmentation on eight datasets; (2) better or competitive finetuned performance to other generalist and specialist models on segmentation and VL tasks; and (3) flexibility for efficient finetuning and novel task composition (e.g., referring captioning and image editing). Code, demo, video, and visualization are available at https://x-decoder-vl.github.io.