# TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection
## 3 Materials and Methods
### Te-Fpn
Respectively, Lightweight Feature-wise Attention was used to mitigate the confounding effect after feature fusion, rather than enhancing the features themselves. Figure 3: (**a**) The schematic diagram illustrates our proposed bottom-up architecture for the feature fusion module, enhancing texture information. (**b**) The diagram illustrates the schematic of the Lightweight Feature-wise Attention. Figure 2: Overall structure diagram of TE-FPN. The proposed approach introduces an architecture called enhancing textural information with a bottom-up architecture (ETA), which allows the input of low-level features to each level of the feature hierarchy. The lightweight Feature-wise Attention (LFA) was employed to extract channel weights using the channel attention module, which were then used to generate the final integrated features. The Feature-wise Attention (FWA) leverages multiple contextual information to acquire channel weights and enhance the features of the final output feature map. (**a**) Schematic diagram illustrating the architecture of enhancing textural information with a bottom-up approach. (**b**) Schematic diagram illustrating the enhanced features using Feature-wise Attention. **Feature-wise Attention**. During the target detection task, the detector heavily relies on the edge and textural information of the instances in the image to accurately delineate the instances. However, the multi-scale feature fusion can introduce a blending effect, leading to discontinuity in the fused feature map features. This can result in the detector acquiring incorrect edge and texture information of the instances, ultimately affecting the accuracy of instance localization and detection tasks. To mitigate the influence of the blending effect on the model, we introduced a novel attention module called 'Feature-wise Attention' in its lightweight version, specifically designed to address the impact of the blending effect on model performance. In this work, we replaced the ROI module with the SRS for feature fusion across different scales in the feature pyramid. Additionally, we incorporated the standard Feature-wise Attention to enhance the features in the final output. The Feature-wise Attention (FWA) is illustrated in Figure 4. In this mechanism, we first utilized global average pooling, global soft pooling, and global maximum pooling to obtain three different spatial contexts. These contexts captured various aspects of the feature map. Next, each context was processed through an MLP layer with shared parameters. Finally, the resulting feature vectors were combined using element-wise summation followed by a sigmoid function. The channel attention in FWA focused on identifying significant features within the graph. Global average pooling provides feedback for every pixel point on the feature map, while global maximum pooling focuses on gradients by considering only the areas with the highest response. On the other hand, soft pooling [44] produces diverse gradient feedback for different pixel points during gradient backpropagation. Global average pooling tends to capture overall image features, global maximum pooling emphasizes instance edge information, and global soft pooling captures the overall texture information of the instance. By incorporating the global soft pooling contextual information and assigning higher weights to each pixel point of the instance, we enhanced the texture information of the instance in the image. The Feature-wise Attention mechanism can be summarized as follows:  
\[ \begin{split} FWA(F)&=\sigma(mlp(Avgpool(x))+mlp( Maxpool(x)),\\ &\quad+mlp(Softpool(x)))\end{split}  \tag{3}\]  
where \(FWA\) denotes the attention mechanism and \(\sigma\) denotes the sigmoid function. By adding \(FWA\), the textural information of the instances in the image is enhanced to obtain better localization.