75 research outputs found
LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving
A map, as crucial information for downstream applications of an autonomous
driving system, is usually represented in lanelines or centerlines. However,
existing literature on map learning primarily focuses on either detecting
geometry-based lanelines or perceiving topology relationships of centerlines.
Both of these methods ignore the intrinsic relationship of lanelines and
centerlines, that lanelines bind centerlines. While simply predicting both
types of lane in one model is mutually excluded in learning objective, we
advocate lane segment as a new representation that seamlessly incorporates both
geometry and topology information. Thus, we introduce LaneSegNet, the first
end-to-end mapping network generating lane segments to obtain a complete
representation of the road structure. Our algorithm features two key
modifications. One is a lane attention module to capture pivotal region details
within the long-range feature space. Another is an identical initialization
strategy for reference points, which enhances the learning of positional priors
for lane attention. On the OpenLane-V2 dataset, LaneSegNet outperforms previous
counterparts by a substantial gain across three tasks, \textit{i.e.}, map
element detection (+4.8 mAP), centerline perception (+6.9 DET), and the
newly defined one, lane segment perception (+5.6 mAP). Furthermore, it obtains
a real-time inference speed of 14.7 FPS. Code is accessible at
https://github.com/OpenDriveLab/LaneSegNet.Comment: Accepted in ICLR 202
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
Multiple FLC haplotypes defined by independent cis-regulatory variation underpin life history diversity in Arabidopsis thaliana
Relating molecular variation to phenotypic diversity is a central goal in evolutionary biology. In Arabidopsis thaliana, FLOWERING LOCUS C (FLC) is a major determinant of variation in vernalization—the acceleration of flowering by prolonged cold. Here, through analysis of 1307 A. thaliana accessions, we identify five predominant FLC haplotypes defined by noncoding sequence variation. Genetic and transgenic experiments show that they are functionally distinct, varying in FLC expression level and rate of epigenetic silencing. Allelic heterogeneity at this single locus accounts for a large proportion of natural variation in vernalization that contributes to adaptation of A. thaliana
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
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