Can SMoES-trained modality routing generalize to other multimodal benchmarks (e.g., DocVQA, InfographicVQA) un

Accurate morphological classification of white blood cells (WBCs) is an important step in the diagnosis of leukemia, a disease in which nonfunctional blast cells accumulate in the bone marrow. Recently, deep convolutional neural networks (CNNs) have been successfully used to classify leukocytes by training them on single-cell images from a specific domain. Most CNN models assume that the distributions of the training and test data are similar, i.e., the data are independently and identically distributed. Therefore, they are not robust to different staining procedures, magnifications, resolutio

Research goal: Can SMoES-trained modality routing generalize to other multimodal benchmarks (e.g., DocVQA, InfographicVQA) under domain shift, and how do accuracy and latency trade-offs differ from chart-specific distribution shifts?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.