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

Abstract

Abstract: 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 Question

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?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance