## 2 Research Area and Data
### The Proposed Methodology
To tackle the challenge of disparate input dimensions, we segment the image into discrete input pixels, thereby ensuring the model's flexibility. The PGM module applies convolutional operations across each input channel, which sets the stage for the subsequent stages of feature extraction and classification. This method enables the model to more capably manage inputs of different dimensions and to efficiently extract and categorize features:  
\[x=X_{1},X_{2}\dots,X_{n} \tag{1}\]  
where n represents the number of groups in the grouped convolution. By doing so, grouped convolution more effectively captures local information features. Based on this principle, we further constructed a pixel embedding module that operates in a grouped manner, thereby forming the PGM module:  
\[x^{\prime}=BN(x)=\gamma\frac{X-\mu}{\alpha}+\beta \tag{2}\]  
where \(\gamma\) represents a learnable scale parameter, which is adjusted during the training process through gradient descent. It is used to maintain the representational power of the data after normalization and can restore the network's nonlinear expressions. Moreover, \(\beta\) is a learnable shift parameter, which is also learned during the training process. It is used to translate the normalized data, ensuring that the data retains its original representational power after normalization. Furthermore, \(\alpha\) is the variance of all samples in the batch. Similar to the mean, it is calculated by taking the standard deviation of each batch of data and then squaring it, which is used for standardizing the data. Finally, \(\mu\) is the mean of all samples in the batch. In batch normalization, we calculate the mean of each batch of data, which is used for subsequent normalization steps:  
\[LeakyRelu(x)=max(0,x) \tag{3}\]  
The PGM module processes the input data \(x\) through grouped convolution, followed by the application of a Batch Normalization (BN) layer, where \(\mu\) and \(\alpha\) represent the mean and standard deviation of the batch, respectively. By learning parameters \(\gamma\) and \(\beta\) to scale and shift the features, PGM achieves normalization and activation of the features, thereby enhancing the model's classification performance and generalization capabilities:  
\[PGM(x)=LeakyRelu(BNF_{1x1}^{g}(x)) \tag{4}\]  
In the SSF module, the SSF module dynamically filters out the tokens with the strongest relevance to the classification task by calculating the correlation of each token. The calculation formula for the Relevance Matrix \(R_{ij}\) is as follows:  
\[R_{ij}=sim(T_{i},C_{j}) \tag{5}\]  
where \(T_{i}\) represents the feature vector of the i-th token, \(C_{j}\) represents the label vector of the j-th classification task, and sim denotes the similarity function, commonly using cosine similarity or dot product similarity. The SSF module selects tokens with values greater than a set threshold \(\theta\) from the relevance matrix. Specifically, the filtering process can be expressed as:  
\[T_{s}elected=sim(T_{i}|R_{ij}>\theta) \tag{6}\]  
where \(T_{s}elected\) represents the set of selected tokens. The SSF module performs feature selection at different scales to capture feature information of varying scales. Assuming there are M scales, the feature selection at each scale can be represented as:  
\[F_{m}=Select(T,m) \tag{7}\]  
where \(F_{m}\) represents the feature set at the m-th scale, and Select denotes the feature selection function.