On the source position and duration of a solar type III radio burst observed by LOFAR

The flux of solar type III radio bursts have a time profile of rising and decay phases at a given frequency, which has been actively studied since the 1970s. Several factors that may influence the duration of a type III radio burst have been proposed. In this work, to study the dominant cause of the duration, we investigate the source positions of the front edge, the peak, and the tail edge in the dynamic spectrum of a single and clear type III radio burst. The duration of this type III burst at a given frequency is about 3 s for decameter wave. The beam-formed observations by the LOw-Frequency ARray are used, which can provide the radio source positions and the dynamic spectra at the same time. We find that, for this burst, the source positions of the front edge, the peak, and the tail edge split with each other spatially. The radial speed of the electrons exciting the front edge, the peak, and the tail edge is 0.42c, 0.25c, and 0.16c, respectively. We estimate the influences of the corona density fluctuation and the electron velocity dispersion on the duration, and the scattering effect by comparison with a few short-duration bursts from the same region. The analysis yields that, in the frequency range of 30–41 MHz, the electron velocity dispersion is the dominant factor that determines the time duration of type III radio bursts with long duration, while scattering may play an important role in the duration of short bursts

Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection

Few-shot object detection, expecting detectors to detect novel classes with a few instances, has made conspicuous progress. However, the prototypes extracted by existing meta-learning based methods still suffer from insufficient representative information and lack awareness of query images, which cannot be adaptively tailored to different query images. Firstly, only the support images are involved for extracting prototypes, resulting in scarce perceptual information of query images. Secondly, all pixels of all support images are treated equally when aggregating features into prototype vectors, thus the salient objects are overwhelmed by the cluttered background. In this paper, we propose an Information-Coupled Prototype Elaboration (ICPE) method to generate specific and representative prototypes for each query image. Concretely, a conditional information coupling module is introduced to couple information from the query branch to the support branch, strengthening the query-perceptual information in support features. Besides, we design a prototype dynamic aggregation module that dynamically adjusts intra-image and inter-image aggregation weights to highlight the salient information useful for detecting query images. Experimental results on both Pascal VOC and MS COCO demonstrate that our method achieves state-of-the-art performance in almost all settings.Comment: Accepted by AAAI202

OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping

Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scene by solely focusing on lane perception tasks. Observing that human drivers rely on both lanes and traffic signals to operate their vehicles safely, we present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. The objective of the presented dataset is to advance research in understanding the structure of road scenes by examining the relationship between perceived entities, such as traffic elements and lanes. Leveraging existing datasets, OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. It comprises three primary sub-tasks, including the 3D lane detection inherited from OpenLane, accompanied by corresponding metrics to evaluate the model's performance. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.Comment: Accepted by NeurIPS 2023 Track on Datasets and Benchmarks | OpenLane-V2 Dataset: https://github.com/OpenDriveLab/OpenLane-V