19 citations · 20 across the 14 of their papers we have counts for
14 papers
MGRL-RSCC: Multi-Granularity Reward Reinforcement Learning for Fine-Grained Remote Sensing Change Captioning
Futian Wang, Mengqi Wang, Xiao Wang +4
Remote Sensing Change Captioning (RSCC), which aims to generate accurate and detailed linguistic descriptions of ground object variations from bi-temporal remote sensing images, is…
Parameter-Dynamic Adaptive Fusion and Calibration Network for RGBT Tracking
Zhaoding Ding, Chenglong Li, Jiandong Jin +2
Existing RGBT trackers typically employ fusion functions with fixed parameters across different targets and scenarios. Although dynamic-architecture methods improve fusion flexibil…
T2I-VeRW: Part-level Fine-grained Perception for Text-to-Image Vehicle Retrieval
Xiao Wang, Ziwen Wang, Weizhe Kong +5
Vehicle Re-identification (Re-ID) aims to retrieve the most similar image to a given query from images captured by non-overlapping cameras. Extending vehicle Re-ID from image-only…
Vehicle-centric Perception via Multimodal Structured Pre-training
Wentao Wu, Xiao Wang, Chenglong Li +2
Vehicle-centric perception plays a crucial role in many intelligent systems, including large-scale surveillance systems, intelligent transportation, and autonomous driving. Existin…
Segment Any Vehicle: Semantic and Visual Context Driven SAM and A Benchmark
Xiao Wang, Ziwen Wang, Wentao Wu +4
With the rapid advancement of autonomous driving, vehicle perception, particularly detection and segmentation, has placed increasingly higher demands on algorithmic performance. Pr…
CM3AE: A Unified RGB Frame and Event-Voxel/-Frame Pre-training Framework
Wentao Wu, Xiao Wang, Chenglong Li +4
Event cameras have attracted increasing attention in recent years due to their advantages in high dynamic range, high temporal resolution, low power consumption, and low latency. S…
FDDet: Frequency-Decoupling for Boundary Refinement in Temporal Action Detection
Xinnan Zhu, Yicheng Zhu, Tixin Chen +2
Temporal action detection aims to locate and classify actions in untrimmed videos. While recent works focus on designing powerful feature processors for pre-trained representations…
Large Language Model Guided Progressive Feature Alignment for Multimodal UAV Object Detection
Wentao Wu, Chenglong Li, Xiao Wang +2
Existing multimodal UAV object detection methods often overlook the impact of semantic gaps between modalities, which makes it difficult to achieve accurate semantic and spatial al…
XiHeFusion: Harnessing Large Language Models for Science Communication in Nuclear Fusion
Xiao Wang, Qingquan Yang, Fuling Wang +12
Nuclear fusion is one of the most promising ways for humans to obtain infinite energy. Currently, with the rapid development of artificial intelligence, the mission of nuclear fusi…
Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset
Xiao Wang, Yu Jin, Wentao Wu +4
Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid mov…
VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models
Wentao Wu, Fanghua Hong, Xiao Wang +2
Existing vehicle detectors are usually obtained by training a typical detector (e.g., YOLO, RCNN, DETR series) on vehicle images based on a pre-trained backbone (e.g., ResNet, ViT)…
Pre-training on High Definition X-ray Images: An Experimental Study
Xiao Wang, Yuehang Li, Wentao Wu +5
Existing X-ray based pre-trained vision models are usually conducted on a relatively small-scale dataset (less than 500k samples) with limited resolution (e.g., 224 224).…
State Space Model for New-Generation Network Alternative to Transformers: A Survey
Xiao Wang, Shiao Wang, Yuhe Ding +13
In the post-deep learning era, the Transformer architecture has demonstrated its powerful performance across pre-trained big models and various downstream tasks. However, the enorm…
Structural Information Guided Multimodal Pre-training for Vehicle-centric Perception
Xiao Wang, Wentao Wu, Chenglong Li +4
Understanding vehicles in images is important for various applications such as intelligent transportation and self-driving system. Existing vehicle-centric works typically pre-trai…