Object detection from point clouds is a fundamental task for 3D scene understanding and has a wide range of applications in the field of multimedia data processing and analysis, such as autonomous driving and virtual interaction. The IoU evaluates the overlap between the two bounding boxes to ensure consistency across network optimization and testing, becoming a recognized regression loss in the field of 3D object detection. However, there is a kind of error coupling between the IoU and the angle, i.e., the IoU does not decrease as the angle error increases and vice versa. This problem leads to sub-optimal solutions for the neural network model, which severely hampers the improvement of 3D object detection accuracy. In this paper, a novel 4DIoU method is introduced for detecting 3D objects from point clouds, which provides a comprehensive rethinking of IoU computation by integrating angular information as an additional dimension. 4DIoU not only solves the problem of error coupling between IoU and angular but also facilitates neural network optimization using angle information. Furthermore, to solve the different impacts of various object shapes on IoU variations, a special 4DIoU called TV4DIoU is proposed to fuse shape information based on three orthogonal projection views, which can adaptively learn the information of objects with different shapes. In addition, to enhance the generalization of the 4DIoU method, a high-flexibility anchor encoding method and a cyclic consistent computation formula for angular errors are designed to make 4DIoU a plug-and-play module for both anchor-based and anchor-free frameworks. Extensive evaluations conducted on the nuScenes, Waymo, and KITTI datasets have confirmed the effectiveness of the proposed method.

Timely anomaly detection of multivariate time series (MTS) is of vital importance for managing large-scale software systems. However, many deep learning-based MTS anomaly detection models require long-term MTS training data to achieve optimal performance, which often conflicts with the frequent pattern changes observed in software systems. Moreover, the training overhead of vast MTS in large-scale software systems is unacceptably high. To address these issues, we design OmniTransfer , a model-agnostic framework that combines weighted hierarchical agglomerative clustering with an adaptive transfer learning strategy, making many state-of-the-art (SOTA) MTS anomaly detection models efficient and effective. Extensive experiments using real-world data from a large web content service provider and a network operator show that OmniTransfer significantly reduces the model initialization time by 46.49% and the training cost by 74.51%, while maintaining high accuracy in detecting anomalies.

The IoU metric directly measures the overlap between two boxes, maintaining consistency in model optimization and testing stages. It has emerged as a highly regarded regression loss in the field of 3D object detection. However, the optimization of IoU often leads to an increased angular error. This erroneous coupling phenomenon renders the model susceptible to settling into sub-optimal solutions, which have not been extensively analyzed and addressed, significantly impeding further advancements in the accuracy of 3D object detection. In this paper, a novel concept "4DIoU" is introduced for 3D object detection, where the angle information is integrated as an additional dimension in the IoU calculation, and a new formula for measuring angle correlation is proposed. The 4DIoU not only resolves the erroneous coupling between IoU and angles but also capitalizes on angle information to enhance network optimization. Furthermore, a new encoding and decoding paradigm is proposed, which is more compatible with 4DIoU for object detection in point clouds. Extensive experiments on nuScenes, Waymo and KITTI datasets demonstrate the effectiveness of our method. The plug-and-play design of our approach proves to be highly versatile.

The development of detection methods for oriented object detection remains a challenging task. A considerable obstacle is the wide variation in the shape (e.g., aspect ratio) of objects. Sample selection in general object detection has been widely studied as it plays a crucial role in the performance of the detection method and has achieved great progress. However, existing sample selection strategies still overlook some issues: (1) most of them ignore the object shape information; (2) they do not make a potential distinction between selected positive samples; and (3) some of them can only be applied to either anchor-free or anchor-based methods and cannot be used for both of them simultaneously. In this paper, we propose novel flexible shape-adaptive selection (SA-S) and shape-adaptive measurement (SA-M) strategies for oriented object detection, which comprise an SA-S strategy for sample selection and SA-M strategy for the quality estimation of positive samples. Specifically, the SA-S strategy dynamically selects samples according to the shape information and characteristics distribution of objects. The SA-M strategy measures the localization potential and adds quality information on the selected positive samples. The experimental results on both anchor-free and anchor-based baselines and four publicly available oriented datasets (DOTA, HRSC2016, UCAS-AOD, and ICDAR2015) demonstrate the effectiveness of the proposed method.

3D object detection is a challenging task because point clouds are characterized by sparsity and irregularity. Most state-of-the-art detectors recognize objects individually without considering the rich context relationships of objects at different levels. In this paper, we propose an end-to-end vote-based multi-level context attention network. Specifically, a Patch-Context-Module is designed to extract multi-level context features among point patches. Meanwhile, because low-level features contain fine location description information, a Spatial-Context-Module is adopted to combine low-level spatial and semantic features. Furthermore, a Fusion Sampling and Aggregation module is proposed to consider additional semantic information of each vote point, thereby increasing the ratio of positive points and improving detection performance. Finally, the Class-IoU-Guide NMS with an adaptive threshold is implemented to suppress false detection at the inference time. Experiments on the ScanNetV2 and SUN RGB-D datasets demonstrated that our proposed method out-performs current state-of-the-art approaches.

We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning. MMRotate implements 18 state-of-the-art algorithms and supports the three most frequently used angle definition methods. To facilitate future research and industrial applications of rotated object detection-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of rotated object detection. MMRotate is publicly released at https://github.com/open-mmlab/mmrotate.

Multi-class object detection in remote sensing images plays an important role in many applications but remains a challenging task because of scale imbalance and arbitrary orientations of the objects with extreme aspect ratios. In this paper, the Asymmetric Feature Pyramid Network (AFPN), Dynamic Feature Alignment (DFA) module, and Area-IoU regression loss are proposed on the basis of a one-stage cascaded detection method for the detection of multi-class objects with arbitrary orientations in remote sensing images. The designed asymmetric convolutional block is embedded into the AFPN for handling objects with extreme aspect ratios and improving the space representation with ignorable increases in calculation. The DFA module is proposed to dynamically align mismatched features, which are caused by the deviation between predefined anchors and arbitrarily oriented predicted boxes. The refined Area-IoU regression loss, which reconciles two new regression loss functions, the area-guided regression loss and IoU-guided regression loss, is proposed to simultaneously solve the scale imbalance problem and angle sensitivity problem. Experiments on three publicly available datasets, DOTA, HRSC2016, and ICDAR2015, show the effectiveness of the proposed method.

Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc. Differing from the dominant regression-based approaches for orientation estimation, this paper explores a relatively less-studied methodology based on classification. The hope is to inherently dismiss the boundary discontinuity issue as encountered by the regression-based detectors. We propose new techniques to push its frontier in two aspects: i) new encoding mechanism: the design of two Densely Coded Labels (DCL) for angle classification, to replace the Sparsely Coded Label (SCL) in existing classification-based detectors, leading to three times training speed increase as empirically observed across benchmarks, further with notable improvement in detection accuracy; ii) loss re-weighting: we propose Angle Distance and Aspect Ratio Sensitive Weighting (ADARSW), which improves the detection accuracy especially for square-like objects, by making DCL-based detectors sensitive to angular distance and object’s aspect ratio. Extensive experiments and visual analysis on large-scale public datasets for aerial images i.e. DOTA, UCAS-AOD, HRSC2016, as well as scene text dataset ICDAR2015 and MLT, show the effectiveness of our approach. The source code is available at $\color{Red}{\text{DCL}}$ and is also integrated in our open source rotation detection benchmark:$\color{Red}{\text{RotationDetection}}$.

Detection of multi-class rotated objects is a challenging task in optical remote sensing images because of large-scale variations, arbitrary orientations and complex backgrounds, etc. Most of the state-of-the-art object detectors for natural images, that use horizontal bounding boxes, are not suitable for oriented objects in remote sensing images. In this paper, we propose an end-to-end cascade detector that can effectively detect rotated objects in complex remote sensing images. Specifically, a feature fusion block is designed to capture features with more details. Meanwhile, a supervised spatial attention mechanism is adopted to improve performance in detecting objects with complex backgrounds by weakening noise and enhancing object regions. Finally, to obtain more accurate object position, a cascade of multi-step detection subnet is implemented to refine anchors. Experiments using a publicly available remote sensing dataset DOTA show that our object detector achieves superior performance over other state-of-the-art approaches.

Global context modeling has been used to achieve better performance in various computer-vision-related tasks, such as classification, detection, segmentation and multimedia retrieval applications. However, most of the existing global mechanisms display problems regarding convergence during training. In this paper, we propose a novel gated global module (GGM) that is lightweight and yet effective in terms of achieving better integration of global information in relation to feature representation. Regarding the original structure of the network as a local block, our module infers global information in parallel with local information, and then a gate function is applied to generate global guidance which is applied to the output of the local module to capture representative information. The proposed GGM can be easily integrated with common CNN architectures and is training friendly. We used a classification task as an example to verify the effectiveness of the proposed GGM, and extensive experiments on ImageNet and CIFAR demonstrated that our method can be widely applied and is conducive to integrating global information into common networks.

Impressive progresses have been achieved in object detection for images by convolution neural networks. However, a robust multi-class object detection method is still one of the great challenges for remote sensing images. Due to the great diversity of scale, orientation, density and background of objects, most advanced object detection algorithms in natural scenes usually suffer a sharp decline in remote sensing images detection. To solve these problems, we proposed a Single-stage Multi-class Object Detection (SMOD) method, aiming at remote sensing images, which can be trained from scratch and detect multi-class objects quickly and precisely. The proposed method introduces a novel Feature Reuse and Attention (FRA) structure as a key module of feature extraction backbone, which combines SE Attention module and dense Feature Reuse connection. Especially, a multiclass detection structure is proposed to learn from multi-scale, multi-level feature map and get effective attention representation for multi-class remote sensing object detection. SMOD can be trained from scratch without pre-trained network stably and converge well simply by employing batch normalization throughout the network. Experiments show that our trainingfrom-scratch method can obtain better performance compared with some state-of-art algorithms on public multi-class remote sensing dataset AIIA2018-6.

In 2011, the IEEE VisWeek conferences inaugurated a symposium on Biological Data Visualization. Like other domain-oriented Vis symposia, this symposium's purpose was to explore the unique characteristics and requirements of visualization within the domain, and to enhance both the Visualization and Bio/Life-Sciences communities by pushing Biological data sets and domain understanding into the Visualization community, and well-informed Visualization solutions back to the Biological community. Amongst several other activities, the BioVis symposium created a data analysis and visualization contest. Unlike many contests in other venues, where the purpose is primarily to allow entrants to demonstrate tour-de-force programming skills on sample problems with known solutions, the BioVis contest was intended to whet the participants' appetites for a tremendously challenging biological domain, and simultaneously produce viable tools for a biological grand challenge domain with no extant solutions. For this purpose expression Quantitative Trait Locus (eQTL) data analysis was selected. In the BioVis 2011 contest, we provided contestants with a synthetic eQTL data set containing real biological variation, as well as a spiked-in gene expression interaction network influenced by single nucleotide polymorphism (SNP) DNA variation and a hypothetical disease model. Contestants were asked to elucidate the pattern of SNPs and interactions that predicted an individual's disease state. 9 teams competed in the contest using a mixture of methods, some analytical and others through visual exploratory methods. Independent panels of visualization and biological experts judged entries. Awards were given for each panel's favorite entry, and an overall best entry agreed upon by both panels. Three special mention awards were given for particularly innovative and useful aspects of those entries. And further recognition was given to entries that correctly answered a bonus question about how a proposed "gene therapy" change to a SNP might change an individual's disease status, which served as a calibration for each approaches' applicability to a typical domain question. In the future, BioVis will continue the data analysis and visualization contest, maintaining the philosophy of providing new challenging questions in open-ended and dramatically underserved Bio/Life Sciences domains.