14 citations · 15 across the 9 of their papers we have counts for
11 papers
MVPruner: Dynamic Token Pruning for Accelerating Multi-view Vision-Language Models in Autonomous Driving
Nan Yang, Zhanwen Liu, Linfeng Zhang +4
Vision-Language Models (VLMs) improve generalization and interpretability in autonomous driving but suffer from efficiency issues due to long visual token sequences, particularly i…
PSTTS: A Plug-and-Play Token Selector for Efficient Event-based Spatio-temporal Representation Learning
Xiangmo Zhao, Nan Yang, Yang Wang +1
Mainstream event-based spatio-temporal representation learning methods typically process event streams by converting them into sequences of event frames, achieving remarkable perfo…
Focus Through Motion: RGB-Event Collaborative Token Sparsification for Efficient Object Detection
Nan Yang, Yang Wang, Zhanwen Liu +3
Existing RGB-Event detection methods process the low-information regions of both modalities (background in images and non-event regions in event data) uniformly during feature extr…
Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios
Zhanwen Liu, Yujing Sun, Yang Wang +3
The dynamic range limitation of conventional RGB cameras reduces global contrast and causes loss of high-frequency details such as textures and edges in complex traffic environment…
Bidirectional Image-Event Guided Fusion Framework for Low-Light Image Enhancement
Zhanwen Liu, Huanna Song, Yang Wang +3
Under extreme low-light conditions, frame-based cameras suffer from severe detail loss due to limited dynamic range. Recent studies have introduced event cameras for event-guided l…
DPMambaIR: All-in-One Image Restoration via Degradation-Aware Prompt State Space Model
Zhanwen Liu, Sai Zhou, Yuchao Dai +3
All-in-One image restoration aims to address multiple image degradation problems using a single model, offering a more practical and versatile solution compared to designing dedica…
SMamba: Sparse Mamba for Event-based Object Detection
Nan Yang, Yang Wang, Zhanwen Liu +3
Transformer-based methods have achieved remarkable performance in event-based object detection, owing to the global modeling ability. However, they neglect the influence of non-eve…
Multi-scale Temporal Fusion Transformer for Incomplete Vehicle Trajectory Prediction
Zhanwen Liu, Chao Li, Yang Wang +4
Motion prediction plays an essential role in autonomous driving systems, enabling autonomous vehicles to achieve more accurate local-path planning and driving decisions based on pr…
MSTF: Multiscale Transformer for Incomplete Trajectory Prediction
Zhanwen Liu, Chao Li, Nan Yang +4
Motion forecasting plays a pivotal role in autonomous driving systems, enabling vehicles to execute collision warnings and rational local-path planning based on predictions of the…
Boosting Visual Recognition in Real-world Degradations via Unsupervised Feature Enhancement Module with Deep Channel Prior
Zhanwen Liu, Yuhang Li, Yang Wang +3
The environmental perception of autonomous vehicles in normal conditions have achieved considerable success in the past decade. However, various unfavourable conditions such as fog…
Enhancing Traffic Object Detection in Variable Illumination with RGB-Event Fusion
Zhanwen Liu, Nan Yang, Yang Wang +3
Traffic object detection under variable illumination is challenging due to the information loss caused by the limited dynamic range of conventional frame-based cameras. To address…