## 4 Experiments and Analysis
### Definition of Loss with Dynamic Weight Adjustment  
During the training process, we observed that certain classes within the ModelNet dataset exhibited significant inter-class similarity. This phenomenon posed substantial challenges for the model, as it struggled to distinguish between visually similar classes, leading to poor classification performance for these hard-to-separate categories. Specifically, the model tended to focus disproportionately on easier-to-classify samples, which exacerbated the misclassification of difficult samples and further skewed the learning process.  
To address this issue, we introduced a dynamic weight adjustment strategy into the training pipeline. The primary objective of this strategy is to adaptively reweight the contribution of each class in the loss function based on its classification difficulty. By dynamically increasing the weights of harder-to-classify classes, the model is encouraged to allocate more capacity to learning discriminative features for these challenging categories. This approach mitigates the negative impact of inter-class similarity and ensures that the model pays appropriate attention to both easy and difficult classes throughout the training process. To address class imbalance and dynamically adjust the importance of each class during training, we introduced a weight adjustment mechanism based on the classification difficulty of each class. Let \(d_{i}\) denote the classification difficulty of class \(i\), where a higher value of \(d_{i}\) indicates a more challenging class. The weight \(w_{i}\) for class \(i\) is computed as follows:  
\[w_{i}=\frac{1}{\left(d_{i}+\xi\right)^{\frac{1}{\xi}}} \tag{8}\]  
where \(\xi\) is a small constant added to avoid division by zero, and \(\tau\) is a hyperparameter that controls the sensitivity of the weight adjustment to class difficulty.  
The weight function reduces the influence of easy-to-classify classes and increases the focus on harder-to-classify classes by applying a temperature scaling factor to the difficulty. This ensures that the model is guided to focus more on difficult classes during training.  
To maintain a balanced learning rate across all classes, the weights are normalized such that the sum of all class weights equals the total number of classes N. The normalized weight \(w_{i}\) for each class is calculated by  
\[w_{i}^{\prime}=\frac{w_{i}}{\sum\limits_{j=1}^{N}w_{j}}\times N \tag{9}\]  
Figure 5: Illustration of sequential-view capturing schemes.  
This normalization ensures that the relative importance of each class is maintained while adjusting for any imbalance in class representation. The final loss function used for training is the weighted cross-entropy loss, which incorporates the dynamically adjusted and normalized class weights. The weighted cross-entropy loss for a given class \(i\) is defined as  
\[\mathcal{L}(y,\hat{y})=-\sum_{i=1}^{N}w_{i}^{\prime}\cdot y_{i}\cdot\log(\hat{y }_{i}) \tag{10}\]  
where \(y_{i}\) is the ground truth label for class \(i\), \(\hat{y}\) is the predicted probability for class \(i\), and \(w^{\prime}\) is the normalized weight for class \(i\). By applying this weighted loss function, the model places greater emphasis on the harder-to-classify classes, as indicated by the dynamically adjusted weights. This mechanism helps the model focus on improving performance in classes where it is most likely to make errors, thereby enhancing overall classification accuracy.