Traditional methods are based on block-wise regression. This framework, Enhanced Blockwise Classification (**EBC**), however, is based on the idea that aims to classify the count value within each block into several pre-defined bins. The enhancement comes from 3 aspects: discretization policy, label correction and loss function. 

Notice that the original block-wise classification concept was introduced by Liu *et al.* in *Counting Objects by Blockwise Classification*.

In stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analysing time series that appear to be long-memory processes (diverging correlation time, e.g. power-law decaying autocorrelation function) or 1/f noise.

The obtained exponent is similar to the Hurst exponent, except that DFA may also be applied to signals whose underlying statistics (such as mean and variance) or dynamics are non-stationary (changing with time). It is related to measures based upon spectral techniques such as autocorrelation and Fourier transform.

DFA (Random Walk)

Mosaic organization of DNA nucleotides

CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification

A brand new Foundation Model framework for deep learning in the future.

DAHSF

Digestion Algorithm in Hierarchical Symbolic Forests: A Fast Text Normalization Algorithm and Semantic Parsing Framework for Specific Scenarios and Lightweight Deployment

Source	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
Year
Data Source	Papers with Code archive (CC BY-SA)