# RANDnet: Vehicle Re-Identification with Relation Attention and Nuance-Disparity Masks
###### Abstract  
Vehicle re-identification (vehicle ReID) is designed to recognize all instances of a specific vehicle across various camera viewpoints, facing significant challenges such as high similarity among different vehicles from the same viewpoint and substantial variance for the same vehicle across different viewpoints. In this paper, we introduce the RAND network, which is equipped with relation attention mechanisms, nuance, and disparity masks to tackle these issues effectively. The disparity mask specifically targets the automatic suppression of irrelevant foreground and background noise, while the nuance mask reveals less obvious, sub-discriminative regions to enhance the overall feature robustness. Additionally, our relation attention module, which incorporates an advanced transformer architecture, significantly reduces intra-class distances, thereby improving the accuracy of vehicle identification across diverse viewpoints. The performance of our approach has been thoroughly evaluated on widely recognized datasets such as VeRi-776 and VehicleID, where it demonstrates superior effectiveness and competes robustly with other leading methods. vehicle re-identification; relation attention; nuance mask; disparity mask; representation learning +
Footnote †: journal: Medical  
high-order attention module to leverage complex information within the attention mechanism, and [PERSON] et al. [10] proposed stacking relations of feature information to effectively capture both global structure and local details. These innovations highlight the balance between global scope and localized attention to detail. However, these methodologies, despite their effectiveness, do not fully resolve all issues. As shown in Figure 1, approaches emphasizing local features--such as stickers on the front window or the view of the interior through it [11]--aid in differentiating between similar vehicle models but may become ineffective due to occlusions or changes in viewpoint because these features do not always appear in images from all viewpoints. Attempts [12; 13] have been made to counter the loss of critical features by increasing the weight of viewpoint-independent features or concatenating an extra feature vector representing the vehicle direction. However, these solutions either require additional hard-to-obtain information or their discriminative capability for learned features is often insufficient to differentiate similar vehicle models. For instance, DFnet [12] uses directional labels to learn specific orientation-related features and attr-net [13] focuses on body color or vehicle model attributes as viewpoint-independent features. Most of these methods use IBNnet [14] as the backbone network. Addressing these challenges necessitates a framework capable of mitigating background and foreground occlusions; discerning multiple discriminative regions; and learning adaptive, viewpoint-related structural information without extra labeling. In response, we propose a vehicle ReID network endowed with relation attention mechanisms and nuanced and disparity masks. The proposed network is based on the following phenomenon: \"Simple negative classes\", which differ markedly in vehicle model and color from the target and tend to activate at unrelated aspects such as background or foreground occlusions during training. In contrast, \"hard negative classes\", which share similar vehicle models, focus on car emblems, headlights, or tire regions. Through the intersection of class activation maps from both simple and hard negative identities, we discern vehicle background and sub-discriminative regions, encapsulated in the disparity mask and nuance mask, respectively. Furthermore, our relation attention module, leveraging an enhanced transformer architecture, facilitates self-attention across vehicle component information, distilled into patches from each \(c\times 1\times 1\) feature map unit, fostering a network understanding of the inter-relations among vehicle components.