Search Results for author:
Found 8 papers, 7 papers with code
Supervised Fine-tuning in turn Improves Visual Foundation Models
Image-text training like CLIP has dominated the pretraining of vision foundation models in recent years.
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers
Although extensively studied for unimodal models, the acceleration for multimodal models, especially the vision-language Transformers, is relatively under-explored.
UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
Real-world data contains a vast amount of multimodal information, among which vision and language are the two most representative modalities.
Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models
This manuscript goes deep into the research of the Dropout algorithm, which is commonly used in neural networks to alleviate the overfitting problem.
Masked Generative Distillation
The current distillation algorithm usually improves students' performance by imitating the output of the teacher.
Multi-encoder parse-decoder network for sequential medical image segmentation
We aim on enhancing medical image segmentation by using spatial continuity information in a proposed Multi-Encoder Parse-Decoder Network (MEPDNet) based on the fact that most of the medical images are sampled continuously.
Deep Learning based Virtual Point Tracking for Real-Time Target-less Dynamic Displacement Measurement in Railway Applications
To tackle this issue, we propose virtual point tracking for real-time target-less dynamic displacement measurement, incorporating deep learning techniques and domain knowledge.
Designing a lightweight 1D convolutional neural network with Bayesian optimization for wheel flat detection using carbody accelerations:
In comparison to the state-of-the-art lightweight CNNs, LightWFNet is validated for WF detection by using carbody accelerations with much lower computational costs.
