ﻻ يوجد ملخص باللغة العربية
Transformers are more and more popular in computer vision, which treat an image as a sequence of patches and learn robust global features from the sequence. However, a suitable vehicle re-identification method should consider both robust global features and discriminative local features. In this paper, we propose a graph interactive transformer (GiT) for vehicle re-identification. On the whole, we stack multiple GiT blocks to build a competitive vehicle re-identification model, in where each GiT block employs a novel local correlation graph (LCG) module to extract discriminative local features within patches and uses a transformer layer to extract robust global features among patches. In detail, in the current GiT block, the LCG module learns local features from local and global features resulting from the LCG module and transformer layer of the previous GiT block. Similarly, the transformer layer learns global features from the global features generated by the transformer layer of the previous GiT block and the new local features outputted via the LCG module of the current GiT block. Therefore, LCG modules and transformer layers are in a coupled status, bringing effective cooperation between local and global features. This is the first work to combine graphs and transformers for vehicle re-identification to the best of our knowledge. Extensive experiments on three large-scale vehicle re-identification datasets demonstrate that our method is superior to state-of-the-art approaches. The code will be available soon.
Existing vehicle re-identification methods commonly use spatial pooling operations to aggregate feature maps extracted via off-the-shelf backbone networks. They ignore exploring the spatial significance of feature maps, eventually degrading the vehic
Vehicle re-identification plays a crucial role in the management of transportation infrastructure and traffic flow. However, this is a challenging task due to the large view-point variations in appearance, environmental and instance-related factors.
Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. The technical challenges require the algorithms must be robust in different views, resol
The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning viewpoint-invariant representations. In this paper, we propose to solve
Vehicle re-identification (reID) often requires recognize a target vehicle in large datasets captured from multi-cameras. It plays an important role in the automatic analysis of the increasing urban surveillance videos, which has become a hot topic i