机载激光扫描（ALS）点云的分类是遥感和摄影测量场的关键任务。尽管最近基于深度学习的方法取得了令人满意的表现，但他们忽略了接受场的统一性，这使得ALS点云分类对于区分具有复杂结构和极端规模变化的区域仍然具有挑战性。在本文中，为了配置多受感受性的场特征，我们提出了一个新型的接受场融合和分层网络（RFFS-NET）。以新颖的扩张图卷积（DGCONV）及其扩展环形扩张卷积（ADCONV）作为基本的构建块，使用扩张和环形图融合（Dagfusion）模块实现了接受场融合过程，该模块获得了多受感染的场特征代表通过捕获带有各种接收区域的扩张和环形图。随着计算碱基的计算基础，使用嵌套在RFFS-NET中的多级解码器进行的接收场的分层，并由多层接受场聚集损失（MRFALOSS）驱动，以驱动网络驱动网络以学习在具有不同分辨率的监督标签的方向。通过接受场融合和分层，RFFS-NET更适应大型ALS点云中具有复杂结构和极端尺度变化区域的分类。在ISPRS Vaihingen 3D数据集上进行了评估，我们的RFFS-NET显着优于MF1的基线方法5.3％，而MIOU的基线方法的总体准确性为82.1％，MF1的总准确度为71.6％，MIOU的MF1和MIOU为58.2％。此外，LASDU数据集和2019 IEEE-GRSS数据融合竞赛数据集的实验显示，RFFS-NET可以实现新的最新分类性能。

点云的语义分割通过密集预测每个点的类别来产生对场景的全面理解。由于接收场的一致性，点云的语义分割对于多受感受性场特征的表达仍然具有挑战性，这会导致对具有相似空间结构的实例的错误分类。在本文中，我们提出了一个植根于扩张图特征聚集（DGFA）的图形卷积网络DGFA-NET，该图由通过金字塔解码器计算出的多基质聚集损失（Maloss）引导。为了配置多受感受性字段特征，将建议的扩张图卷积（DGCONV）作为其基本构建块，旨在通过捕获带有各种接收区域的扩张图来汇总多尺度特征表示。通过同时考虑用不同分辨率的点集作为计算碱基的点集惩罚接收场信息，我们引入了由Maloss驱动的金字塔解码器，以了解接受田间的多样性。结合这两个方面，DGFA-NET显着提高了具有相似空间结构的实例的分割性能。 S3DIS，ShapenetPart和Toronto-3D的实验表明，DGFA-NET优于基线方法，实现了新的最新细分性能。

Despite some successful applications of goal-driven navigation, existing deep reinforcement learning-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-art baselines. Demonstration videos are available at \colorb{https://youtu.be/93LGlGvaN0c.

Molecular representation learning is crucial for the problem of molecular property prediction, where graph neural networks (GNNs) serve as an effective solution due to their structure modeling capabilities. Since labeled data is often scarce and expensive to obtain, it is a great challenge for GNNs to generalize in the extensive molecular space. Recently, the training paradigm of "pre-train, fine-tune" has been leveraged to improve the generalization capabilities of GNNs. It uses self-supervised information to pre-train the GNN, and then performs fine-tuning to optimize the downstream task with just a few labels. However, pre-training does not always yield statistically significant improvement, especially for self-supervised learning with random structural masking. In fact, the molecular structure is characterized by motif subgraphs, which are frequently occurring and influence molecular properties. To leverage the task-related motifs, we propose a novel paradigm of "pre-train, prompt, fine-tune" for molecular representation learning, named molecule continuous prompt tuning (MolCPT). MolCPT defines a motif prompting function that uses the pre-trained model to project the standalone input into an expressive prompt. The prompt effectively augments the molecular graph with meaningful motifs in the continuous representation space; this provides more structural patterns to aid the downstream classifier in identifying molecular properties. Extensive experiments on several benchmark datasets show that MolCPT efficiently generalizes pre-trained GNNs for molecular property prediction, with or without a few fine-tuning steps.

Adder Neural Network (AdderNet) provides a new way for developing energy-efficient neural networks by replacing the expensive multiplications in convolution with cheaper additions (i.e.l1-norm). To achieve higher hardware efficiency, it is necessary to further study the low-bit quantization of AdderNet. Due to the limitation that the commutative law in multiplication does not hold in l1-norm, the well-established quantization methods on convolutional networks cannot be applied on AdderNets. Thus, the existing AdderNet quantization techniques propose to use only one shared scale to quantize both the weights and activations simultaneously. Admittedly, such an approach can keep the commutative law in the l1-norm quantization process, while the accuracy drop after low-bit quantization cannot be ignored. To this end, we first thoroughly analyze the difference on distributions of weights and activations in AdderNet and then propose a new quantization algorithm by redistributing the weights and the activations. Specifically, the pre-trained full-precision weights in different kernels are clustered into different groups, then the intra-group sharing and inter-group independent scales can be adopted. To further compensate the accuracy drop caused by the distribution difference, we then develop a lossless range clamp scheme for weights and a simple yet effective outliers clamp strategy for activations. Thus, the functionality of full-precision weights and the representation ability of full-precision activations can be fully preserved. The effectiveness of the proposed quantization method for AdderNet is well verified on several benchmarks, e.g., our 4-bit post-training quantized adder ResNet-18 achieves an 66.5% top-1 accuracy on the ImageNet with comparable energy efficiency, which is about 8.5% higher than that of the previous AdderNet quantization methods.

Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in many tasks. Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data. However, traditional transfer learning methods often fail to generalize the pre-trained knowledge to the target task due to domain discrepancy. Self-supervised learning on graphs can increase the generalizability of graph features since self-supervision concentrates on inherent graph properties that are not limited to a particular supervised task. We propose a novel knowledge transfer strategy by integrating meta-learning with self-supervised learning to deal with the heterogeneity and scarcity of fMRI data. Specifically, we perform a self-supervised task on the source domain and apply meta-learning, which strongly improves the generalizability of the model using the bi-level optimization, to transfer the self-supervised knowledge to the target domain. Through experiments on a neurological disorder classification task, we demonstrate that the proposed strategy significantly improves target task performance by increasing the generalizability and transferability of graph-based knowledge.

We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics. While visual semantics focus on how humans build concepts when using vision to perceive a target reality, lexical semantics focus on how humans build concepts of the same target reality through the use of language. The lack of coincidence between visual and lexical semantics, in turn, has a major impact on CV systems in the form of the Semantic Gap Problem (SGP). The paper, while extensively exemplifying the lack of coincidence as above, introduces a general, domain-agnostic methodology to enforce alignment between visual and lexical semantics.

In this paper, we present ExtremeBERT, a toolkit for accelerating and customizing BERT pretraining. Our goal is to provide an easy-to-use BERT pretraining toolkit for the research community and industry. Thus, the pretraining of popular language models on customized datasets is affordable with limited resources. Experiments show that, to achieve the same or better GLUE scores, the time cost of our toolkit is over $6\times$ times less for BERT Base and $9\times$ times less for BERT Large when compared with the original BERT paper. The documentation and code are released at https://github.com/extreme-bert/extreme-bert under the Apache-2.0 license.

Airport runway segmentation can effectively reduce the accident rate during the landing phase, which has the largest risk of flight accidents. With the rapid development of deep learning, related methods have good performance on segmentation tasks and can be well adapted to complex scenes. However, the lack of large-scale, publicly available datasets in this field makes the development of methods based on deep learning difficult. Therefore, we propose a Benchmark for Airport Runway Segmentation, named BARS. Meanwhile, a semi-automatic annotation pipeline is designed to reduce the workload of annotation. BARS has the largest dataset with the richest categories and the only instance annotation in the field. The dataset, which is collected using the X-Plane simulation platform, contains 10,002 images and 29,347 instances with three categories. We evaluate eight representative instance segmentation methods on BARS and analyze their performance. Based on the characteristic of the airport runway with a regular shape, we propose a plug-and-play smoothing post-processing module (SPPM) and a contour point constraint loss (CPCL) function to smooth segmentation results for mask-based and contour-based methods, respectively. Furthermore, a novel evaluation metric named average smoothness (AS) is developed to measure smoothness. The experiments show that existing instance segmentation methods can achieve prediction results with good performance on BARS. SPPM and CPCL can improve the average accuracy by 0.9% and 1.13%, respectively. And the average smoothness enhancements for SPPM and CPCL are more than 50% and 28%, respectively.

This paper studies 3D dense shape correspondence, a key shape analysis application in computer vision and graphics. We introduce a novel hybrid geometric deep learning-based model that learns geometrically meaningful and discretization-independent features with a U-Net model as the primary node feature extraction module, followed by a successive spectral-based graph convolutional network. To create a diverse set of filters, we use anisotropic wavelet basis filters, being sensitive to both different directions and band-passes. This filter set overcomes the over-smoothing behavior of conventional graph neural networks. To further improve the model's performance, we add a function that perturbs the feature maps in the last layer ahead of fully connected layers, forcing the network to learn more discriminative features overall. The resulting correspondence maps show state-of-the-art performance on the benchmark datasets based on average geodesic errors and superior robustness to discretization in 3D meshes. Our approach provides new insights and practical solutions to the dense shape correspondence research.