Computer Vision Model
Computer vision models aim to enable computers to "see" and interpret images, enabling applications ranging from medical diagnosis to autonomous driving. Current research emphasizes improving model robustness, addressing biases and ethical concerns in datasets, and enhancing explainability through techniques like class activation maps and contextual analysis. This field is crucial for advancing various scientific disciplines and practical applications, with ongoing efforts focused on improving accuracy, efficiency, and fairness across diverse datasets and tasks.
Papers
AI-Based Teat Shape and Skin Condition Prediction for Dairy Management
Yuexing Hao, Tiancheng Yuan, Yuting Yang, Aarushi Gupta, Matthias Wieland, Ken Birman, Parminder S. Basran
Human-Guided Image Generation for Expanding Small-Scale Training Image Datasets
Changjian Chen, Fei Lv, Yalong Guan, Pengcheng Wang, Shengjie Yu, Yifan Zhang, Zhuo Tang
KPCA-CAM: Visual Explainability of Deep Computer Vision Models using Kernel PCA
Sachin Karmani, Thanushon Sivakaran, Gaurav Prasad, Mehmet Ali, Wenbo Yang, Sheyang Tang
Segmenting Wood Rot using Computer Vision Models
Roland Kammerbauer, Thomas H. Schmitt, Tobias Bocklet
Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images
Thomas H. Schmitt, Maximilian Bundscherer, Tobias Bocklet