Vehicle re-identification (Re-ID) is a challenging task that aims to recognize the same vehicle across different non-overlapping cameras. Existing attention mechanism-based methods for vehicle Re-ID ...often suffer from significant intra-class variation and inter-class variation due to various factors such as illumination, occlusion, viewpoint, etc. In this paper, we propose a novel network architecture for vehicle Re-ID, named Dimensional Decoupling Strategy and Non-local Relationship Network (DMNR-Net), which uses three modules to extract complementary features: global feature extraction module, non-local relationship capture module(NRCM), and dimensional decoupling module (DDS). The global feature extraction module captures complete and coarse-grained features from the whole image; the NRCM module extracts saliency information from feature maps in both spatial and channel dimensions; and the DDS decouples spatial and channel features into two branches to extract fine-grained features and focus on specific subspaces. We conduct extensive experiments on two popular publicly datasets, VeRi-776 and VehicleID, to evaluate the effectiveness of our method. The experimental results show that our DMNR-Net outperforms state-of-the-art methods by a large margin on both datasets.
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Vehicle re-identification across multiple cameras is one of the main problems of intelligent transportation systems (ITSs). Since the differences in the appearance between different vehicles of the ...same model are small and the appearance of the same vehicle changes drastically from different viewpoints, vehicle re-identification is a challenging task. In this paper, we propose a model called multi-receptive field soft attention part learning (MRF-SAPL). The MRF-SAPL model learns semantically diverse vehicle part-level features under different receptive fields through multiple local branches, alleviating the problem of small differences in vehicle appearance. To align vehicle parts from different images, this study uses soft attention to adaptively locate the positions of the parts on the final feature map generated by a local branch and maintain the continuity of the internal semantics of the parts. In addition, to obtain parts with different semantic patterns, we propose a new loss function that punishes overlapping regions, forcing the positions of different parts on the same feature map to not overlap each other as much as possible. Extensive ablation experiments demonstrate the effectiveness of our part-level feature learning method MRF-SAPL, and our model achieves state-of-the-art performance on two benchmark datasets.
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The goal of vehicle re-identification is to identify the same vehicle from multiple cameras, which is a challenging task. There are many solutions to this problem, among which the self-attention ...mechanism is very popular. It can capture the long-range dependence in an image, thereby suppressing the irrelevant features. Most of the existing designs are based on isolated pairwise query-key interactions to refine a node. They implicitly mine attention patterns without explicitly modeling node weights. In this paper, we propose a global relational attention mechanism, which makes full use of the global dependence of a node to learn and infer its weight value. Global dependence can measure the importance of nodes more robustly and efficiently. To capture more discriminative features, we propose a maximum suppression constraint to adaptively adjust weight values to expand the range of attention. In addition, we design a pair of effective attention modules based on the proposed attention mechanism, that focus on mining the discriminative features related to vehicle identities from the spatial and channel dimensions. We conduct a large number of experiments on the VeRi-776 and VehicleID datasets, and the experimental results demonstrate the effectiveness of our method.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Vehicle Re-identification (Re-ID) refers to finding the same vehicle shot by other cameras from a given vehicle image library, which can also be regarded as a sub-problem of image retrieval. It plays ...an important role in intelligent transportation and smart cities. The key of vehicle Re-ID is to extract discriminative vehicle features. To better extract such features from the vehicle image to improve the recognition accuracy, we propose a three-branch adaptive attention network-Global Relational Attention and Multi-granularity Feature Learning (GRMF) to improve feature representation and discrimination. First, we divide the network into three branches, extracting different and useful features from three perspectives: spatial location, channel information, and local information. Second, we propose two effective global relational attention modules, which capture the global structural information for better attention learning. Specifically, to determine the importance level of a node, we use the global relationship between the node and all other nodes to infer the attention weight of the node directly. Third, according to the characteristics of the vehicle re-identification task, we introduce a suitable local partition strategy. It not only can simply capture subtle local information but also solve the problem of misalignment and within-part consistency disruption to a great extent. Extensive experiments demonstrate the effectiveness of our approach, and we achieve state-of-the-art results on two challenging datasets, including VeRi776 and VehicleID.
A compact and portable quartz-enhanced photoacoustic spectroscopy gas sensor was developed for four-component gas detection using two off-beam acoustic microresonators The two AMRs were placed in ...parallel on opposing sides of a quartz tuning fork for photoacoustic signal enhancement. Four distributed feedback (DFB) lasers were connected to the four ends of the two off-beam AMRs using a fiber collimator for photoacoustic signal excitation. Four-component gas sensing was achieved via time-division multiplexing of the distributed feedback laser driver currents. The four-component gas sensing scheme was used to detect acetylene (C2H2) at 1,532.83 nm, methane (CH4) at 1,653.722 nm, water vapor (H2O) at 1,368.597 nm and carbon dioxide (CO2) at 1,577.787 nm for feasibility testing. Minimum detection limits of 3.6 ppmv for C2H2, 34.71 ppmv for CH4, 1.09 ppmv for H2O, and 341.18 ppmv for CO2 were obtained, and the linear responses reached 0.9982, 0.9969, 0.99843 and 0.99591 for C2H2, CH4, H2O and CO2, respectively, at 1.5 s intervals.
Learning fine-grained discriminative information is essential to address the challenges of small inter-class differences and large intra-class differences in vehicle re-identification (Re-ID). ...Attentional mechanism is often used to capture important global information in images rather than fine-grained discriminative information. Studies have shown that the multi-axis interaction of information can enhance the feature representation ability of networks. This paper explores how to use the multi-axis interaction of information to facilitate more effective learning of attention and how to capture important detailed information in local regions. We propose a multi-axis interactive multidimensional attention network (MIMA-Net) for vehicle Re-ID. The network allows information to interact on multiple axes and calibrates the weight distribution of features from multiple dimensions to learn subtle discriminative information in vehicle parts/regions. The window-channel attention module (W-CAM) in MIMA-Net facilitates the learning of channel attention by interacting first across locations and then across channels, while the channel group-spatial attention module (CG-SAM) facilitates the learning of spatial attention by interacting first across channels and then across locations. These two modules perform window partitioning in a priori manner and channel semantic aggregation in an adaptive manner to learn discriminative semantic features in parts, respectively. These two approaches complement each other to strengthen the feature representation ability of MIMA-Net. Extensive experiments on three large public datasets, VeRi-776, VehicleID, and VERI-Wild, verify the effectiveness of our MIMA-Net and show that our method achieves state-of-the-art performance.
•Utilizing multi-axis interaction of information to promote attention learning.•Learning subtle semantics via multidimensional calibration of weights.•A priori and adaptive ways are combined to learn part-level features.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Vehicle re-identification (Re-ID) plays an important role in intelligent transportation systems. To solve the key problem of small inter-class difference, we propose a vehicle Re-ID network based on ...grouping aggregation attention and cross-part interaction (GACP), which uses a channel context local interaction attention block (CCLI) and a cross-part interaction module (CPIM) to extract global and local discriminative features, respectively. The CCLI constructs the context information of each channel and performs local interaction to realize the communication of local and long-range information, thus effectively infer the weights of channels. In addition, the CCLI further reduces the number of parameters and improves the attention effect through a grouping aggregation module and an attention enhancement constraint. The CPIM enhances the correlation between vehicle parts obtained by rigid segmentation on feature maps to mine robust local information. Extensive experiments on two popular datasets VeRi776 and VehicleID demonstrate the effectiveness of the proposed method.
•Local and long-range dependency is leveraged to efficiently infer weights.•Enhancing long-range dependence between parts to learn robust local features.•Solving small inter-class differences by global and local information.
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Vehicle re-identification (Re-ID) aims to find the image of the same vehicle in different cameras. One of the reasons that this task remains challenging is that different vehicles of the same type ...and color look very similar in appearance. In recent years, attention mechanisms have been widely used in vehicle Re-ID, including some mechanisms that use the relationships between nodes to infer attention. However, such methods are vulnerable to interference by some noise information in the image, especially partial information from other vehicles. For this reason, we propose in this paper a global reference attention mechanism for attention learning by utilizing the relationships between nodes and the global reference node, where the global reference node is built by all nodes in the image. At the same time, we propose a Global Reference Attention Network (GRA-Net) based on the mechanism to mine a large number of discriminative features useful for vehicle re-identification, thus easing the difficulty of distinguishing between different vehicles of similar appearance. Specifically, to extract more discriminative features, we adopt a multi-branch neural network and embed different global reference attention modules in each branch to compose our GRA-Net. Extensive experiments valiyear the effectiveness of GRA-Net and show that our approach achieves state-of-the-art performance on two massive vehicle Re-ID datasets.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Capturing global and subtle discriminative information using attention mechanisms is essential to address the challenge of inter-class high similarity for vehicle re-identification (Re-ID) task. ...Mixing self-information of nodes or modeling context based on pairwise dependencies between nodes are the core ideas of current advanced attention mechanisms. This paper aims to explore how to utilize both dependency context and self-context in an efficient way to facilitate attention to learn more effectively. We propose a heterogeneous context interaction (HCI) attention mechanism that infers the weights of nodes from the interactions of global dependency contexts and local self-contexts to enhance the effect of attention learning. To reduce computational complexity, global dependency contexts are modeled by aggregating number-compressed pairwise dependencies, and the interactions of heterogeneous contexts are restricted to a certain range. Based on this mechanism, we propose a heterogeneous context interaction network (HCI-Net), which uses channel heterogeneous context interaction module (CHCI) and spatial heterogeneous context interaction module (SHCI), and introduces a rigid partitioning strategy to extract important global and fine-grained features. In addition, we design a non-similarity constraint (NSC) that forces the HCI-Net to learn diverse subtle discriminative information. The experiment results on two large datasets, VeRi-776 and VehicleID, show that our proposed HCI-Net achieves the state-of-the-art performance. In particular, the mean average precision (mAP) reaches 83.8% on VeRi-776 dataset.
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