We consider the problem of detecting and localizing an submatrix with larger-than-usual entries inside a large, noisy matrix. This problem arises from analysis of data in genetics, bioinformatics, and social sciences. We consider that entries of the data matrix are independently following distributions from a natural exponential family, which generalizes the common Gaussian assumptions in the literature. Distribution-free methods of detection and size-adaptive methods of both detection and localization problems are studied with their asymptotic behaviors illustrated.