Attention Networks For Person Re-Identification Research Articles

Relation-aware non-local attention network for person re-identification

Relation-aware non-local attention network for person re-identification

Local-aware global attention network for person re-identification based on body and hand images

Learning representative, robust and discriminative information from images is essential for effective person re-identification (Re-Id). In this paper, we propose a compound approach for end-to-end discriminative deep feature learning for person Re-Id based on both body and hand images. We carefully design the Local-Aware Global Attention Network (LAGA-Net), a multi-branch deep network architecture consisting of one branch for spatial attention, one branch for channel attention, one branch for global feature representations and another branch for local feature representations. The attention branches focus on the relevant features of the image while suppressing the irrelevant backgrounds. The global and local branches intends to capture global context and fine-grained information, respectively. A set of ablation study shows that each component contributes to the increased performance of the LAGA-Net. Extensive evaluations on four popular body-based person Re-Id benchmarks and two publicly available hand datasets demonstrate that our proposed method consistently outperforms existing state-of-the-art methods.

An efficient feature pyramid attention network for person re-identification

For person re-identification, occlusion, appearance similarity and background clutter have always been challenges. In order to effectively address the challenges, we propose an efficient feature pyramid attention network (FPA-Net), which combines visual features from different levels to focus on both detail features and information. Specifically, we embed a pair of attention mechanisms that complement each other in the backbone network to focus on the discriminant features of person areas. In addition, we designed a novel feature pyramid structure, which propagates the feature information from the cross-level through the top feature to the bottom feature and from the bottom feature to the top feature to supplement the detail information of the feature. Finally, we integrate features form different scales through a lightweight transition block to generate more discriminant features. Our method performed experimental analysis on four datasets: Market-1501, DukeMTMC-ReID, CUHK03(Detected) and MSMT17. A large number of experimental results prove that the performance of the method is significantly better than the existing state-of-the-art methods.

An efficient multi-scale channel attention network for person re-identification

At present, occlusion and similar appearance pose serious challenges to the task of person re-identification. In this work, we propose an efficient multi-scale channel attention network (EMCA) to learn robust and more discriminative features to solve these problems. Specifically, we designed a novel cross-channel attention module (CCAM) in EMCA and placed it after different layers in the backbone. The CCAM includes local cross-channel interaction (LCI) and channel weight integration (CWI). LCI focuses on both the maximum pooling features and the average pooling features to generate channel weights through convolutional layers, respectively. CWI combines the two channel weights to generate richer and more discriminant channel weights. Experiments on four popular person Re-ID datasets (Market-1501, DukeMTMC-ReID, CUHK-03 (detected) and MSMT17) show that the performance of our EMCA is consistently significantly superior to the existing state-of-the-art methods.

PMA-Net: A parallelly mixed attention network for person re-identification

The objective of person re-identification (Re-ID) is to match unmistakable people across different settings and camera views. Although the use of convolutional neural networks (CNN) has been effective, they lose much significant data and ignore the correlation between the overall and partial features brought about by downsampling operators (e.g., pooling). This compromises the retrieval performance based on the Euclidean distance. We propose a parallelly mixed attention network (PMA-Net) to demonstrate the complementarity of spatial and channel information for Re-ID. First, we integrate self-attention with depthwise convolution in a parallel design, extracting different dimensions of information. Second, we propose two interactive branches to entwine spatial and channel information while extracting features. Then, we add a new additional embedding to incorporate non-visual inputs and reduce the feature bias toward camera variances. Finally, we perform additional maximizing the minimal pairwise angles (MMA) regularization on the features of PMA, which successfully encourages the angular diversity of feature vectors. We confirm the effectiveness of PMA-Net through an extensive set of ablation studies. The results of three benchmarks show the superiority of our model over current mAP/top1 accuracies of 91.3%/96.1% on Market-1501, 83.3%/91.7% on DukeMTMC-ReID, and 66.2%/85.1% on MSMT17.

S$^{2}$-Net:Semantic and Saliency Attention Network for Person Re-Identification

S$^{2}$-Net:Semantic and Saliency Attention Network for Person Re-Identification

Pose matters: Pose guided graph attention network for person re-identification

Person re-Identification (reID), aiming at retrieving a person across different cameras, has been playing a more and more important role in the construction of smart city and social security. For deep-learning-based reID methods, it has been proved that using local feature together with global feature could help to give robust representation for person retrieval. Human pose information can provide the locations of human skeleton to effectively guide the network to pay more attention to these key areas, and can also help to reduce the noise distractions from background or occlusions. Based on human pose, a Pose Guided Graph Attention (PGGA) network is proposed in this paper, which is a multi-branch architecture consisting of one branch for global feature and two branches for local key-point features. A graph attention convolution layer is carefully designed to re-assign the contribution weight of each extracted local feature by modeling the similarity relations. The experimental results demonstrate the effectiveness of our approach on discriminative feature learning. Our model achieves the state-of-the-art performance on several mainstream evaluation datasets. A plenty of ablation studies and different kinds of comparison experiments are conducted to prove the effectiveness of this work, including the tests on occluded datasets and cross-domain datasets. Moreover, we further design supplementary tests in practical scenario to indicate the advantage of our work in real-word applications.

Multi-granularity cross attention network for person re-identification

Multi-granularity cross attention network for person re-identification

Fine-grained alignment network and local attention network for person re-identification

Fine-grained alignment network and local attention network for person re-identification

Dual Branch Attention Network for Person Re-Identification.

As a sub-direction of image retrieval, person re-identification (Re-ID) is usually used to solve the security problem of cross camera tracking and monitoring. A growing number of shopping centers have recently attempted to apply Re-ID technology. One of the development trends of related algorithms is using an attention mechanism to capture global and local features. We notice that these algorithms have apparent limitations. They only focus on the most salient features without considering certain detailed features. People’s clothes, bags and even shoes are of great help to distinguish pedestrians. We notice that global features usually cover these important local features. Therefore, we propose a dual branch network based on a multi-scale attention mechanism. This network can capture apparent global features and inconspicuous local features of pedestrian images. Specifically, we design a dual branch attention network (DBA-Net) for better performance. These two branches can optimize the extracted features of different depths at the same time. We also design an effective block (called channel, position and spatial-wise attention (CPSA)), which can capture key fine-grained information, such as bags and shoes. Furthermore, based on ID loss, we use complementary triplet loss and adaptive weighted rank list loss (WRLL) on each branch during the training process. DBA-Net can not only learn semantic context information of the channel, position, and spatial dimensions but can integrate detailed semantic information by learning the dependency relationships between features. Extensive experiments on three widely used open-source datasets proved that DBA-Net clearly yielded overall state-of-the-art performance. Particularly on the CUHK03 dataset, the mean average precision (mAP) of DBA-Net achieved 83.2%.

Pose transfer generation with semantic parsing attention network for person re-identification

Pose variation as one of the key factors prevents the network from learning a robust person re-identification (Re-ID) model. We in this paper propose a novel Semantic Parsing Attention Network (SPAN) to transfer person pose from the source to another. SPAN is constructed with several Semantic Parsing Attention Blocks. Each block focuses on a local transfer of the human manifold, which can attend to put the sample condition patches to the corresponding location of the target image. The introduction of the binary segmentation mask and the semantic parsing map is not only significant for the seamless stitching of the foreground and the background, but also decreases the computation load considerably. Compared with other methods, our network can characterize better body shape as well as keeping clothing attributes during the pose transfer. And our synthesized image can obtain better appearance and shape consistency related to the source image. Experimental results are provided to show the superiority of our network on both qualitative and quantitative results on Market-1501 and DeepFashion datasets. Furthermore, extensive experiments are also conducted on person Re-ID systems trained with the augmented data, where our network has the ability to improve the person Re-ID accuracy.

Collaborative Attention Network for Person Re-identification

The quality of visual feature representation has always been a key factor in many computer vision tasks. In the person re-identification (Re-ID) problem, combining global and local features to improve model performance is becoming a popular method, because previous works only used global features alone, which is very limited at extracting discriminative local patterns from the obtained representation. Some existing works try to collect local patterns explicitly slice the global feature into several local pieces in a handcrafted way. By adopting the slicing and duplication operation, models can achieve relatively higher accuracy but we argue that it still does not take full advantage of partial patterns because the rule and strategy local slices are defined. In this paper, we show that by firstly over-segmenting the global region by the proposed multi-branch structure, and then by learning to combine local features from neighbourhood regions using the proposed Collaborative Attention Network (CAN), the final feature representation for Re-ID can be further improved. The experiment results on several widely-used public datasets prove that our method outperforms many existing state-of-the-art methods.

Double-Resolution Attention Network for Person Re-Identification

Double-Resolution Attention Network for Person Re-Identification

Feature Refinement and Filter Network for Person Re-Identification

In the task of person re-identification, the attention mechanism and fine-grained information have been proved to be effective. However, it has been observed that models often focus on the extraction of features with strong discrimination, and neglect other valuable features. The extracted fine-grained information may include redundancies. In addition, current methods lack an effective scheme to remove background interference. Therefore, this paper proposes the feature refinement and filter network to solve the above problems from three aspects: first, by weakening the high response features, we aim to identify highly valuable features and extract the complete features of persons, thereby enhancing the robustness of the model; second, by positioning and intercepting the high response areas of persons, we eliminate the interference arising from background information and strengthen the response of the model to the complete features of persons; finally, valuable fine-grained features are selected using a multi-branch attention network for person re-identification to enhance the performance of the model. Our extensive experiments on the benchmark Market-1501, DukeMTMC-reID, CUHK03 and MSMT17 person re-identification datasets demonstrate that the performance of our method is comparable to that of state-of-the-art approaches.

Harmonious attention network for person re-identification via complementarity between groups and individuals

Person re-identification (Re-ID) is of important capability for artificial intelligence and human–computer interaction. The main challenge of person Re-ID lies in limited data to precisely capture a wide range of appearance variations over multiple viewpoints. Furthermore, compared to the Re-ID between the single person, the person groups contain information about the relationship between pedestrians that can potentially help identify certain identities. The Re-ID combining groups and individuals remain to be a promising task under rare study. In this paper, we propose a harmonious attention network for person re-identification, in which we jointly consider the complementarity between person groups and individuals. Concretely, first we propose a two-stream attentive network (TSAN) to respectively learn the information from the person groups and individuals. TSAN consists of a spatial–temporal fusion network for the group Re-ID, as well as a deep network for the traditionally individual person Re-ID. To jointly consider the contributions of the groups and individuals, then we propose a novel re-ranking algorithm (GIRK) based on the learned features to associate the group and individual information. We also propose a new group Re-ID dataset DukeGroupVid to evaluate the performance of our approach. Comprehensive experimental results on the proposed dataset and other Re-ID datasets demonstrate the effectiveness of our model.

Cross-Correlated Attention Networks for Person Re-Identification

Deep neural networks need to make robust inference in the presence of occlusion, background clutter, pose and viewpoint variations -to name a few- when the task of person re-identification is considered. Attention mechanisms have recently proven to be successful in handling the aforementioned challenges to some degree. However previous designs fail to capture inherent inter-dependencies between the attended features; leading to restricted interactions between the attention blocks. In this paper, we propose a new attention module called Cross-Correlated Attention (CCA); which aims to overcome such limitations by maximizing the information gain between different attended regions. Moreover, we also propose a novel deep network that makes use of different attention mechanisms to learn robust and discriminative representations of person images. The resulting model is called the Cross-Correlated Attention Network (CCAN). Extensive experiments demonstrate that the CCAN comfortably outperforms current state-of-the-art algorithms by a tangible margin.Modeling the inherentspatial relations between different attended regions within the deep architecture. Joint end-to-end cross correlated attention and representational learning. State-of-the-art results in terms of mAP and Rank-1 accuracies across several challenging datasets.

A part-based attention network for person re-identification

Person re-identification (re-id) is the task of recognizing images of the same pedestrian captured by different cameras with non-overlapping views. Person re-id is a challenging task due to the existence of large view variations, such as spatial misalignment, background clutter and human poses change. In this paper, we handle these challenges from the following two aspects: utilizing attention mechanism to alleviate misalignment problem and exploiting the complementary effects of global-local features for more stable pedestrian descriptors. Specifically, we first present a part-based attention model consisting of a channel attention block and a spatial attention block to sequentially refine the convolutional descriptors of person body parts. The channel and spatial attention blocks weight the channels and positions of body-part feature maps to spot the informative channels and regions, respectively. Then global full-body and local body-part of the refined feature maps are pooled into global and local representations, which are jointly trained using identity classification loss. We conduct extensive experiments on four standard benchmark datasets including Market1501, CUHK03, DukeMTMC-reID, and CUHK01, and the experimental results demonstrate the effectiveness of the presented method.

Cross-level reinforced attention network for person re-identification

Attention mechanism is a simple and effective method to enhance discriminative performance of person re-identification (Re-ID). Most of previous attention-based works have difficulty in eliminating the negative effects of meaningless information. In this paper, a universal module, named Cross-level Reinforced Attention (CLRA), is proposed to alleviate this issue. Firstly, we fuse features of different semantic levels using adaptive weights. The fused features, containing richer spatial and semantic information, can better guide the generation of subsequent attention module. Then, we combine hard and soft attention to improve the ability to extract important information in spatial and channel domains. Through the CLRA, the network can aggregate and propagate more discriminative semantic information. Finally, we integrate the CLRA with Harmonious Attention CNN (HA-CNN) and form a novel Cross-level Reinforced Attention CNN (CLRA-CNN) to optimize person Re-ID. Experiment results on several public benchmarks show that the proposed method achieves state-of-the-art performance.

Curriculum Enhanced Supervised Attention Network for Person Re-Identification

Though deep learning methods in Person Re-ID have increased its performance, the field is still confronted with great challenges such as pedestrian misalignment due to the inferior pedestrian detector. To solve such problems, we propose Curriculum Enhanced Supervised Attention Network (CE-SAN): Firstly, an attention module is trained under supervision, which helps the network further emphasize the key information and associate the local and global branch together, leading to a better exploitation of the discriminative features. Secondly, a curriculum design is adopted to divide the dataset into subsets according to the distribution density of the training samples, enabling the network to learn gradually to increase the capability. Moreover, The CE-SAN is easy to be plugged in most of the backbones with high generalization ability. To prove the CE-SAN's superiority, experiments are conducted on three datasets, CE-SAN achieves competitive performance with the state-of-the-arts on Market-1501 and DukeMTMC-reID. Particularly, on the most challenging MSMT17, it outperforms the state-of-the-art methods by 5.2 $\%$ in Rank-1 and 6.5 $\%$ in mAP.

Complementation-Reinforced Attention Network for Person Re-Identification

Fine-grained information has been proved helpful for person re-identification, and multi-head attention mechanism offers a feasible solution for this. However, we observe severe redundancy among the multiple branches, which might make the learned representation over-emphasize certain discriminative regions and correspondingly ignore other potentially informative regions. Therefore, we tackle this issue by two aspects yielding the so-called Complementation-Reinforced Attention Network (CRAN). One is the redundancy among branches, and we propose to impose complementing constraints among multiple attention heads. The constraints are two-fold: on the one hand, it encourages each branch to attend to complementary attention regions; on the other hand, it enforces orthogonality among the learned features of different regions in the embedding space. The other is the redundancy among query positions for each attention head. So we simplify the attention block by sparsifying the query positions. Besides, in order to achieve efficient retrieval, we propose an adaptive feature fusion method for dimensional reduction. Compared with the commonly used feature ensemble, our method effectively reduces the dimensionality while keeping the discriminative ability. We demonstrate the effectiveness of our method on MSMT17, Market-1501, DukeMTMC-reID, and CUHK03 datasets.