# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 2 Related Works
### Appearance-Based Person Re-ID  
Video-based person Re-ID is a complex task that requires the identification of individuals across varying camera views. This challenge involves the fusion of spatial and temporal features. Recent developments in deep learning architectures have enhanced performance substantially, significantly surpassing classical methods.  
Convolutional Neural Networks (CNNs), as exemplified in studies by [PERSON] et al. [15] and [PERSON] et al. [16], are mainly used to extract appearance features from video sequences. However, they may occasionally overlook crucial discriminative spatial and temporal details. To capture sequence-level representations, Recurrent Neural Networks (RNNs) were utilized by [PERSON] et al. [17] to preserve information from previous frames. Furthermore, 3D-CNNs were used by [PERSON] et al. [18] to derive a holistic capture of both spatial and temporal cues.  
Attention-driven models, such as those presented by [PERSON] et al. [19], integrate spatial and temporal attention mechanisms to derive latent individual representations. Addressing these limitations, in [20] [PERSON] et al. proposed the innovative Salient-to-Broad Module (SBM) and the Integration-and-Distribution Module (IDM), which expand attention regions within video frames to enhance frame-level representations. Despite these advances, such methods still face challenges in real-world conditions.  
Graph-based approaches have gained prominence in video-based person Re-ID, as highlighted in research by [PERSON] et al. [21]. These techniques excel at uncovering complex interconnections within data, a crucial aspect of enhancing discriminative feature representation. A prime example of this approach is the MGH framework proposed by [PERSON] et al. [7]. This multi-granular hypergraph learning framework uses spatial and temporal cues extracted from tracklets, employing a hypergraph neural network. The model incorporates an attention mechanism during the learning process to focus on node-level feature aggregation, which leads to more distinct graph representations.  
Furthermore, Pyramid Spatial-Temporal Aggregation (PSTA) [9] identifies spatial correlations within frames and uses information on temporal consistency. This method highlights discriminative features while suppressing irrelevant ones.  
Recently, Vision Transformers (ViTs), as proposed by [PERSON] et al. [22], represent a major advancement. Architectures like PiT, introduced by [PERSON] et al. [10], are noteworthy for their innovative use of transformers with pyramidal structures that efficiently process information across various patches, offering a nuanced understanding of visual data.  
Despite these technological advances, some practical challenges remain unresolved. A notable difficulty is the accurate differentiation of individuals who share similar clothes, an issue that frequently arises in real-world scenarios and adds complexity to the task of effective person Re-ID. This challenge necessitates that current methods go beyond simple pattern recognition and develop more sophisticated approaches to extract and analyze unique identifiers from a person's appearance that are not solely dependent on clothing.