# TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection
## 2 Related Work
However, both types of detectors are influenced by post-processing steps such as compressing redundant prediction results, anchor frame design, and heuristics for assigning target frames to anchor frames [29]. In contrast, DETR [5] achieves an end-to-end target detector by directly predicting without relying on intermediate methods. **Transformer**. Transformer was initially introduced as a Seq2 Seq [30] model designed for machine translation. Subsequent studies have demonstrated that a pre-trained Transformer-based model (PTM) [31] can achieve state-of-the-art performance on various tasks. Consequently, the Transformer has emerged as the preferred architecture for NLP tasks. Besides the NLP domain, the Transformer has gained significant adoption in areas such as computer vision, audio processing, etc. [32]. The Non-local Network [33] was the first to employ the self-attentive mechanism in the field of computer vision, achieving successful target detection. Several frameworks have been proposed in recent years [18; 34; 35] to enhance the Transformer and optimize it from various perspectives. Visual transformers [36] incorporate the Transformer into a CNN, enhancing the CNN network by allocating semantic information of the input image to different channels and closely correlating them through encoder blocks (referred to as VT blocks). VT blocks are employed as an alternative to partial convolution to improve the semantic modeling capacity of CNN networks. SWIN-T [18] introduced Shifted Window based Self-Attention, significantly reducing the computational complexity of the transformer when processing images. Funnel Transformer [37] employs a funnel-like encoder architecture that incorporates pooling along the sequence dimension to progressively decrease the length of the hidden sequence and then employs upsampling for reconstruction, effectively reducing FLOP and memory consumption. When employing the transformer in the computer vision (CV) domain, the feature space resolution is constrained, and the network encounters challenges in convergence during training. To address these issues, [PERSON] et al. [4] introduced Deformable DETR, which accelerates model convergence by directing the attention module to concentrate on a subset of key sampling points surrounding the reference. **Attention mechanism. ** Attention plays a crucial role in human perception of external information, as humans selectively concentrate on the most salient parts when processing information in a scene to enhance the capture of relevant information [38]. RAM [39] integrates deep neural networks with an attention mechanism, enabling end-to-end updating of the entire network by iteratively predicting significant regions. This marks the first implementation of an attention mechanism in CNN networks. Numerous subsequent works have adopted comparable attention strategies. STN [40] predicts the spatial transformation by incorporating a sub-network that identifies significant regions in the input. SE-Net [41] introduces a compact module that enhances inter-channel relationships by utilizing global average pooling to compute attention across channels. GSoP-Net [42] addresses the limitation of using global average pooling alone in SENet for collecting contextual information, which restricts the modeling capacity of the attention mechanism. To overcome this, GSoP-Net proposes the global second-order pooling (GSoP) block to capture higher-order statistics while incorporating global contextual information. CBAM [43] incorporates global maximum pooling in addition to global average pooling, boosting the attention mechanism's response to maximum gradient feedback. Furthermore, the combination of spatial attention and channel attention demonstrates superior performance compared to channel attention alone. Adding spatial attention to channel attention verifies that using both is better than using channel attention alone.