182 research outputs found
Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection
Object detection on visible (RGB) and infrared (IR) images, as an emerging
solution to facilitate robust detection for around-the-clock applications, has
received extensive attention in recent years. With the help of IR images,
object detectors have been more reliable and robust in practical applications
by using RGB-IR combined information. However, existing methods still suffer
from modality miscalibration and fusion imprecision problems. Since transformer
has the powerful capability to model the pairwise correlations between
different features, in this paper, we propose a novel Calibrated and
Complementary Transformer called Former to address these two
problems simultaneously. In Former, we design an Inter-modality
Cross-Attention (ICA) module to obtain the calibrated and complementary
features by learning the cross-attention relationship between the RGB and IR
modality. To reduce the computational cost caused by computing the global
attention in ICA, an Adaptive Feature Sampling (AFS) module is introduced to
decrease the dimension of feature maps. Because Former performs
in the feature domain, it can be embedded into existed RGB-IR object detectors
via the backbone network. Thus, one single-stage and one two-stage object
detector both incorporating our Former are constructed to
evaluate its effectiveness and versatility. With extensive experiments on the
DroneVehicle and KAIST RGB-IR datasets, we verify that our method can fully
utilize the RGB-IR complementary information and achieve robust detection
results. The code is available at
https://github.com/yuanmaoxun/Calibrated-and-Complementary-Transformer-for-RGB-Infrared-Object-Detection.git
Channel Adaptive DL based Joint Source-Channel Coding without A Prior Knowledge
Significant progress has been made in wireless Joint Source-Channel Coding
(JSCC) using deep learning techniques. The latest DL-based image JSCC methods
have demonstrated exceptional performance across various signal-to-noise ratio
(SNR) levels during transmission, while also avoiding cliff effects. However,
current channel adaptive JSCC methods rely heavily on channel prior knowledge,
which can lead to performance degradation in practical applications due to
channel mismatch effects. This paper proposes a novel approach for image
transmission, called Channel Blind Joint Source-Channel Coding (CBJSCC). CBJSCC
utilizes Deep Learning techniques to achieve exceptional performance across
various signal-to-noise ratio (SNR) levels during transmission, without relying
on channel prior information. We have designed an Inverted Residual Attention
Bottleneck (IRAB) module for the model, which can effectively reduce the number
of parameters while expanding the receptive field. In addition, we have
incorporated a convolution and self-attention mixed encoding module to
establish long-range dependency relationships between channel symbols. Our
experiments have shown that CBJSCC outperforms existing channel adaptive
DL-based JSCC methods that rely on feedback information. Furthermore, we found
that channel estimation does not significantly benefit CBJSCC, which provides
insights for the future design of DL-based JSCC methods. The reliability of the
proposed method is further demonstrated through an analysis of the model
bottleneck and its adaptability to different domains, as shown by our
experiments
Precise Orbit and Clock Products of Galileo, BDS and QZSS from MGEX Since 2018: Comparison and PPP Validation
In recent years, the development of new constellations including Galileo, BeiDou Navigation Satellite System (BDS) and Quasi-Zenith Satellite System (QZSS) have undergone dramatic changes. Since January 2018, about 30 satellites of the new constellations have been launched and most of the new satellites have been included in the precise orbit and clock products provided by the Multi Global Navigation Satellite System (Multi-GNSS) Experiment (MGEX). Meanwhile, critical issues including antenna parameters, yaw-attitude models and solar radiation pressure models have been continuously refined for these new constellations and updated into precise MGEX orbit determination and precise clock estimation solutions. In this context, MGEX products since 2018 are herein assessed by orbit and clock comparisons among individual analysis centers (ACs), satellite laser ranging (SLR) validation and precise point positioning (PPP) solutions. Orbit comparisons showed 3D agreements of 3β5 cm for Galileo, 8β9 cm for BDS-2 inclined geosynchronous orbit (IGSO), 12β18 cm for BDS-2 medium earth orbit (MEO) satellites, 24 cm for BDS-3 MEO and 11β16 cm for QZSS IGSO satellites. SLR validations demonstrated an orbit accuracy of about 3β4 cm for Galileo and BDS-2 MEO, 5β6 cm for BDS-2 IGSO, 4β6 cm for BDS-3 MEO and 5β10 cm for QZSS IGSO satellites. Clock products from different ACs generally had a consistency of 0.1β0.3 ns for Galileo, 0.2β0.5 ns for BDS IGSO/MEO and 0.2β0.4 ns for QZSS satellites. The positioning errors of kinematic PPP in Galileo-only mode were about 17β19 mm in the north, 13β16 mm in the east and 74β81 mm in the up direction, respectively. As for BDS-only PPP, positioning accuracies of about 14, 14 and 49 mm could be achieved in kinematic mode with products from Wuhan University applied
Gene Mapping of a Mutant Mungbean (Vigna radiata L.) Using New Molecular Markers Suggests a Gene Encoding a YUC4-like Protein Regulates the Chasmogamous Flower Trait
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