Solution Path
Solution path research encompasses diverse fields, focusing on finding optimal or effective solutions across various problem domains, from computer vision and natural language processing to robotics and differential equations. Current research emphasizes developing robust and efficient algorithms, including transformer-based models and physics-informed neural networks, to address challenges like data heterogeneity, occlusion, and model interpretability. These advancements are crucial for improving the accuracy, reliability, and explainability of solutions in numerous applications, ranging from autonomous driving and medical diagnosis to material science and environmental monitoring.
Papers
The Solution for the 5th GCAIAC Zero-shot Referring Expression Comprehension Challenge
Longfei Huang, Feng Yu, Zhihao Guan, Zhonghua Wan, Yang Yang
The Solution for the sequential task continual learning track of the 2nd Greater Bay Area International Algorithm Competition
Sishun Pan, Xixian Wu, Tingmin Li, Longfei Huang, Mingxu Feng, Zhonghua Wan, Yang Yang
The Solution for Language-Enhanced Image New Category Discovery
Haonan Xu, Dian Chao, Xiangyu Wu, Zhonghua Wan, Yang Yang
The Solution for the AIGC Inference Performance Optimization Competition
Sishun Pan, Haonan Xu, Zhonghua Wan, Yang Yang