2023-2024 Half Year Summary
A work summary for the first semester of the 2023-2024 academic year.
In the past half year, Ecust-MMAI laboratory has closely focused on the research and application of Multimodal AI technology, and has made significant progress and achievements. The team members worked together, not only publishing multiple high-quality academic papers and patents, but also successfully collaborating on projects with multiple industries, further promoting the implementation and application of MMAI technology.
Here lists some papers produced by the laboratory. Click on the link to view a detailed introduction and feel free to give feedback or guidance.
- FDTNet: Enhancing frequency-aware representation for prohibited object detection from X-ray images via dual-stream transformers
- ACnerf: enhancement of neural radiance field by alignment and correction of pose to reconstruct new views from a single x-ray
- Local attention and long-distance interaction of rPPG for deepfake detection
- TransDD: A transformer-based dual-path decoder for improving the performance of thoracic diseases classification using chest X-ray
- SMIFormer: Learning Spatial Feature Representation for 3D Object Detection from 4D Imaging Radar via Multi-View Interactive Transformers
- MC-DC: An MLP-CNN Based Dual-path Complementary Network for Medical Image Segmentation
- Dynamic facial expression recognition based on spatial key-points optimized region feature fusion and temporal self-attention