12 citations · 21 across the 9 of their papers we have counts for
9 papers
Bi-directional Adapter for Multi-modal Tracking
Bing Cao, Junliang Guo, Pengfei Zhu +1
Due to the rapid development of computer vision, single-modal (RGB) object tracking has made significant progress in recent years. Considering the limitation of single imaging sens…
Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning
Yan Fan, Yu Wang, Pengfei Zhu +1
Continual learning (CL) has shown promising results and comparable performance to learning at once in a fully supervised manner. However, CL strategies typically require a large nu…
ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
Yichen Bai, Zongbo Han, Changqing Zhang +3
Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples, especially discovering challenging outliers from auxiliary outl…
Semantic Equivariant Mixup
Zongbo Han, Tianchi Xie, Bingzhe Wu +2
Mixup is a well-established data augmentation technique, which can extend the training distribution and regularize the neural networks by creating ''mixed'' samples based on the la…
Tuning Pre-trained Model via Moment Probing
Mingze Gao, Qilong Wang, Zhenyi Lin +3
Recently, efficient fine-tuning of large-scale pre-trained models has attracted increasing research interests, where linear probing (LP) as a fundamental module is involved in expl…
Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion
Yiming Sun, Bing Cao, Pengfei Zhu +1
Infrared and visible image fusion aims to integrate comprehensive information from multiple sources to achieve superior performances on various practical tasks, such as detection,…
HS-Diffusion: Semantic-Mixing Diffusion for Head Swapping
Qinghe Wang, Lijie Liu, Miao Hua +5
Image-based head swapping task aims to stitch a source head to another source body flawlessly. This seldom-studied task faces two major challenges: 1) Preserving the head and body…
Class-Specific Semantic Reconstruction for Open Set Recognition
Hongzhi Huang, Yu Wang, Qinghua Hu +1
Open set recognition enables deep neural networks (DNNs) to identify samples of unknown classes, while maintaining high classification accuracy on samples of known classes. Existin…
Single Object Tracking Research: A Survey
Ruize Han, Wei Feng, Qing Guo +1
Visual object tracking is an important task in computer vision, which has many real-world applications, e.g., video surveillance, visual navigation. Visual object tracking also has…