Action speak louder than words .

题目待定

Currently, research on filter pruning primarily focuses on image classification tasks. While these methods have demonstrated remarkable performance in classification, their direct application to semantic segmentation has proven less effective. We attribute this limitation to two key factors: (1) Unlike classification, which mainly relies on semantic information, semantic segmentation requires rich spatial details. Existing pruning methods tend to overlook these spatial cues, making it difficult for pruned models to distinguish adjacent pixels and predict precise object boundaries. (2) The encoder-decoder architecture of segmentation models is inherently more complex. However, current pruning approaches lack an effective mechanism for determining layer-wise pruning ratios in segmentation models, leading to excessive pruning of critical layers and the loss of essential semantic or spatial information.

Inspired by DecoupleSegNet and related works, we decouple semantic context from spatial information by applying Fourier Transform to feature maps, enabling separate assessments in the frequency domain. High-frequency components contain fine-grained details such as textures and edges, which are crucial for delineating object boundaries, whereas low-frequency components capture large-scale structures and global context, aiding in object classification. Based on this decomposition, we rank the importance of different channels within each layer and apply pruning accordingly.

Furthermore, leveraging concepts from subgraph approximation in graph theory, we assess the relative importance of different layers and dynamically allocate pruning ratios to adapt to the hierarchical structure of segmentation models. (MORE DETAILS maybe.) Since this is an NP-hard problem, conventional algorithms cannot provide an optimal solution in polynomial time. To overcome this, we propose an efficient pruning algorithm to accelerate the pruning process. Extensive experimental results demonstrate that our approach consistently achieves state-of-the-art performance across multiple semantic segmentation datasets and diverse model architectures.

Bone Age Prediction using a Convolutional Neural Network-based Regression Algorithm employing Attention-Directing and Cluster

Bone age assessment (BAA) is a critical research topic in pediatric radiology, with growing interest in developing automated BAA methods. This study proposes a bone age prediction model integrating cluster analysis and convolutional neural network (CNN) regression, further enhanced by a multi-scale attention mechanism to construct a “divide-and-focus” dual-driven deep learning framework.