24 citations · 104 across the 24 of their papers we have counts for
8 papers · 1 filter
Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation
Lechun You, Zhonghua Wu, Weide Liu +5
Current methods for 3D semantic segmentation propose training models with limited annotations to address the difficulty of annotating large, irregular, and unordered 3D point cloud…
LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement
Siwen Jiao, Yangyi Fang, Baoyun Peng +2
Recent advancements in Visual Language Models (VLMs) have made them crucial for visual question answering (VQA) in autonomous driving, enabling natural human-vehicle interactions.…
A Timely Survey on Vision Transformer for Deepfake Detection
Zhikan Wang, Zhongyao Cheng, Jiajie Xiong +4
In recent years, the rapid advancement of deepfake technology has revolutionized content creation, lowering forgery costs while elevating quality. However, this progress brings for…
CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection
Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli +1
Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using…
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing
Hugo Chan-To-Hing, Bharadwaj Veeravalli
Self-supervised frameworks for representation learning have recently stirred up interest among the remote sensing community, given their potential to mitigate the high labeling cos…
2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection
Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli +1
Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remain…
Object-Aware Self-supervised Multi-Label Learning
Xu Kaixin, Liu Liyang, Zhao Ziyuan +2
Multi-label Learning on Image data has been widely exploited with deep learning models. However, supervised training on deep CNN models often cannot discover sufficient discriminat…
Deeply Supervised Active Learning for Finger Bones Segmentation
Ziyuan Zhao, Xiaoyan Yang, Bharadwaj Veeravalli +1
Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segm…