A hybrid deep learning framework for automatic detection of brain tumours using different modalities
Nowadays, deep convolutional neural networks (DCNNs) are the focus of substantial
research for classification and detection applications in medical image processing …
research for classification and detection applications in medical image processing …
Adaptive on-device model update for responsive video analytics in adverse environments
Y Kong, P Yang, Y Cheng - … on Circuits and Systems for Video …, 2024 - ieeexplore.ieee.org
While advanced lightweight models excel at real-time inference on resource-constrained
end cameras in general scenarios, they often face limitations in adverse environments …
end cameras in general scenarios, they often face limitations in adverse environments …
Cross-modal independent matching network for image-text retrieval
X Ke, B Chen, X Yang, Y Cai, H Liu, W Guo - Pattern Recognition, 2025 - Elsevier
Image-text retrieval serves as a bridge connecting vision and language. Mainstream modal
cross matching methods can effectively perform cross-modal interactions with high …
cross matching methods can effectively perform cross-modal interactions with high …
Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark
G Wu, Y Zhang, L Deng, J Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Anomaly detection in complex industrial processes plays a pivotal role in ensuring efficient,
stable, and secure operation. Existing anomaly detection methods primarily focus on …
stable, and secure operation. Existing anomaly detection methods primarily focus on …
Modality-Consistent Prompt Tuning with Optimal Transport
Prompt tuning has been successfully used in leveraging the knowledge of Large-scale
Vision-Language Pre-trained (VLP) models on downstream tasks. Most existing prompt …
Vision-Language Pre-trained (VLP) models on downstream tasks. Most existing prompt …