11 accepted papers
Vision-language navigation (VLN) requires intelligent agents to navigate environments by interpreting linguistic instructions alongside visual observations, serving as a cornerstone task in Embodied AI. Current VLN research for unmanned aerial vehicles (UAVs) relies on detailed, pre-specified instru…
Existing one-stream trackers have attracted widespread attention. However, they are not applicable in real-time aerial robot tracking systems due to substantial computational overhead, especially when dynamic templates are introduced. To address this issue, we propose a novel Dynamic Compact Consens
AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent network self-driving holds the key, it heavily relies on expert experience and…
High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due…
Unsupervised domain adaptive object detection is a challenging perception task where object detectors are adapted from a label-rich source domain to an unlabeled target domain, playing a vital role in autonomous driving and robot navigation. Since the camera settings, weather, and light conditions v
Image-level weakly supervised semantic segmentation is a challenging task. As classification networks tend to capture notable object features and are insensitive to overactivation, class activation map (CAM) is too sparse and rough to guide segmentation network training. Inspired by the fact that er…
Orthogonal time frequency space (OTFS) as an emerging modulation technique in the 5G and beyond era exploits full time-frequency diversity and is robust against doubly-selective channels in high mobility scenarios. In this work, we consider an OTFS modulation based automotive joint radar-communicati…
Deep learning based image denoising methods have been extensively investigated. In this paper, attention mechanism enhanced kernel prediction networks (AME-KPNs) are proposed for burst image denoising, in which, nearly cost-free attention modules are adopted to first refine the feature maps and to f…
Instance segmentation is one of the fundamental vision tasks. Recently, fully convolutional instance segmentation methods have drawn much attention as they are often simpler and more efficient than two-stage approaches like Mask R-CNN. To date, almost all such approaches fall behind the two-stage Ma…
The hardware implementation of large-scale multi-antenna systems requires power-efficient power amplifiers (PAs). However, the existing beamforming designs often cause a large peak-to-average power ratio and have to rely on power-inefficient PAs. In this paper, we propose a unified power-efficient b…
In this paper, we study the beam-based training design for the single-user (SU) hybrid massive antenna system based on outage probability performance. First, an interleaved training design is proposed where the feedback is concatenated with the training procedure to monitor the training status and t…