19 citations · 24 across the 12 of their papers we have counts for
12 papers
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…
Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition
Weizhe Kong, Xiao Wang, Ruichong Gao +5
Pedestrian Attribute Recognition (PAR) is an indispensable task in human-centered research and has made great progress in recent years with the development of deep neural networks.…
DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model
Zhicheng Zhao, Jinquan Yan, Chenglong Li +2
Optical remote sensing image dehazing presents significant challenges due to its extensive spatial scale and highly non-uniform haze distribution, which traditional single-image de…
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…
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)…
Pedestrian Attribute Recognition: A New Benchmark Dataset and A Large Language Model Augmented Framework
Jiandong Jin, Xiao Wang, Qian Zhu +2
Pedestrian Attribute Recognition (PAR) is one of the indispensable tasks in human-centered research. However, existing datasets neglect different domains (e.g., environments, times…
An Empirical Study of Mamba-based Pedestrian Attribute Recognition
Xiao Wang, Weizhe Kong, Jiandong Jin +5
Current strong pedestrian attribute recognition models are developed based on Transformer networks, which are computationally heavy. Recently proposed models with linear complexity…
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…
CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras
Yabin Zhu, Xiao Wang, Chenglong Li +5
Existing datasets for RGB-DVS tracking are collected with DVS346 camera and their resolution () is low for practical applications. Actually, only visible cameras ar…
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…
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion
Xiao Wang, Jiandong Jin, Chenglong Li +3
Existing pedestrian attribute recognition (PAR) algorithms adopt pre-trained CNN (e.g., ResNet) as their backbone network for visual feature learning, which might obtain sub-optima…