260 citations · 277 across the 12 of their papers we have counts for
12 papers
Watch Where You Move: Region-aware Dynamic Aggregation and Excitation for Gait Recognition
Binyuan Huang, Yongdong Luo, Xianda Guo +4
Deep learning-based gait recognition has achieved great success in various applications. The key to accurate gait recognition lies in considering the unique and diverse behavior pa…
TUNI: Unifying Pre-training and Fine-tuning with Modality-Aware Mutual Learning and Rectification for RGB-T Semantic Segmentation
Xiaodong Guo, Xianda Guo, Tong Liu +4
RGB-thermal (RGB-T) semantic segmentation improves the environmental perception of autonomous platforms in challenging conditions. Prevailing RGB-T segmentation frameworks suffer f…
MaskFuser: Masked Fusion of Joint Multi-Modal Tokenization for End-to-End Autonomous Driving
Yiqun Duan, Xianda Guo, Zheng Zhu +3
Current multi-modality driving frameworks normally fuse representation by utilizing attention between single-modality branches. However, the existing networks still suppress the dr…
OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline
Xianda Guo, Chenming Zhang, Juntao Lu +5
Stereo matching aims to estimate the disparity between matching pixels in a stereo image pair, which is important to robotics, autonomous driving, and other computer vision tasks.…
Multi-Prompt with Depth Partitioned Cross-Modal Learning
Yingjie Tian, Yiqi Wang, Xianda Guo +2
In recent years, soft prompt learning methods have been proposed to fine-tune large-scale vision-language pre-trained models for various downstream tasks. These methods typically c…
CompletionFormer: Depth Completion with Convolutions and Vision Transformers
Zhang Youmin, Guo Xianda, Poggi Matteo +3
Given sparse depths and the corresponding RGB images, depth completion aims at spatially propagating the sparse measurements throughout the whole image to get a dense depth predict…
DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition
Ming Wang, Xianda Guo, Beibei Lin +5
Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. Compared with other biometric technologies, gait recognition is mo…
Adjacent-view Transformers for Supervised Surround-view Depth Estimation
Xianda Guo, Wenjie Yuan, Yunpeng Zhang +5
Depth estimation has been widely studied and serves as the fundamental step of 3D perception for robotics and autonomous driving. Though significant progress has been made in monoc…
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation
Yiqun Duan, Xianda Guo, Zheng Zhu
Monocular depth estimation is a challenging task that predicts the pixel-wise depth from a single 2D image. Current methods typically model this problem as a regression or classifi…
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer
Chaoqiang Zhao, Youmin Zhang, Matteo Poggi +6
Self-supervised monocular depth estimation is an attractive solution that does not require hard-to-source depth labels for training. Convolutional neural networks (CNNs) have recen…
Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline
Xianda Guo, Zheng Zhu, Tian Yang +6
Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. Even though growing efforts have been devoted to cross-view recognit…
GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework
Ming Wang, Beibei Lin, Xianda Guo +5
Many gait recognition methods first partition the human gait into N-parts and then combine them to establish part-based feature representations. Their gait recognition performance…