We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associated with protected attributes. Prior research has primarily focused on mitigating one kind of bias by incorporating complex fairness-driven constraints into optimization objectives or designing additional layers that focus on specific protected attributes. We introduce a simple and generic bias mitigation approach that prevents models from learning relationships between protected attributes and output variable by reducing mutual information between them. We demonstrate that our approach is effective in reducing bias with little or no drop in accuracy. We also show that the models trained with our learning framework become causally fair and insensitive to the values of protected attributes. Finally, we validate our approach by studying feature interactions between protected and non-protected attributes. We demonstrate that these interactions are significantly reduced when applying our bias mitigation.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

We present the design, development, and evaluation of HREyes: biomimetic communication devices which use light to communicate information and, for the first time, gaze direction from AUVs to humans. First, we introduce two types of information displays using the HREye devices: active lucemes and ocular lucemes. Active lucemes communicate information explicitly through animations, while ocular lucemes communicate gaze direction implicitly by mimicking human eyes. We present a human study in which our system is compared to the use of an embedded digital display that explicitly communicates information to a diver by displaying text. Our results demonstrate accurate recognition of active lucemes for trained interactants, limited intuitive understanding of these lucemes for untrained interactants, and relatively accurate perception of gaze direction for all interactants. The results on active luceme recognition demonstrate more accurate recognition than previous light-based communication systems for AUVs (albeit with different phrase sets). Additionally, the ocular lucemes we introduce in this work represent the first method for communicating gaze direction from an AUV, a critical aspect of nonverbal communication used in collaborative work. With readily available hardware as well as open-source and easily re-configurable programming, HREyes can be easily integrated into any AUV with the physical space for the devices and used to communicate effectively with divers in any underwater environment with appropriate visibility.

在本文中，我们提出了一个基于运动的机器人通信框架，该框架能够在自动水下车辆（AUV）和人类潜水员之间进行非语言交流。我们为AUV到AUV通信设计一种手势语言，可以通过观察对话的潜水员轻松理解与典型的射频，光或基于音频的AUV通信来理解。为了让AUV在视觉上从另一个AUV中理解一个手势，我们提出了一个深层网络（RRCommnet），该网络利用了自我发挥的机制来学会通过提取最大歧视性时空特征来学会识别每个消息。我们将该网络培训在不同的模拟和现实世界中。在模拟和闭水机器人试验中，我们的实验评估表明，所提出的RRCommnet体系结构能够在模拟数据上平均准确性为88-94％，在真实数据上平均准确性为88-94％（真实数据的平均精度为88-94％）取决于所使用的模型的版本）。此外，通过与人类参与者进行消息转录研究，我们还表明，人类可以理解所提出的语言，总体转录精度为88％。最后，我们讨论了嵌入式GPU硬件上rrCommnet的推理运行时，以便在现场的AUV上实时使用。

人类和自主水下车辆（AUV）之间的直接沟通是人体机器人互动（HRI）研究中相对缺乏缺陷的地区，尽管许多任务（例如监视，检查和救援）需要密切的潜水机器人协作。该领域的许多核心功能需要进一步研究以改善机器人能力以易于相互作用。其中一个是自治机器人接近和定位自己相对于潜水员的挑战，以启动和促进相互作用。次优AUV定位可能导致质量差的相互作用，导致潜水员的过度认知和物理负荷。在本文中，我们介绍了一种用于AUV的新方法，以自主导航和实现潜水相对定位以开始交互。我们的方法仅基于单眼视觉，不需要全局本地化，并计算效率。我们展示了我们的算法以及在模拟和物理AUV上的所述算法的实现，以受控池中的闭水测试形式进行广泛的评估。我们的结果分析表明，所提出的单眼视觉算法可靠，完全在AUV上完全操作。

在本文中，我们介绍了基于变化自动编码器（VAES）的卫星数据在卫星数据中改变检测的重量轻，无人监督的方法，具体用途。灾害管理等诸如诸如卫星观测的快速可用性的灾害。传统上，在将所有数据转移到地面后，在地面上执行数据分析 - 向地面站进行。因此，对下行链路功能的约束会影响任何下游应用程序。相比之下，Ravaen直接在卫星上预处理采样的数据，并标志改变了下行链路的优先级，缩短了响应时间。我们验证了我们的系统对由时间赛事的时间系列组成的效果 - 我们计划与本出版物一起发布 - 证明拉韦突出了像素明智的基准。最后，我们在资源限制硬件上测试了我们的方法，以评估计算和内存限制。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

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.