Person re-identification is a prevalent technology deployed on intelligent surveillance.There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a...Person re-identification is a prevalent technology deployed on intelligent surveillance.There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution,yet such models are not applicable to the open world.In real world,the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera inconsistent.When low-resolution(LR)images in the query set are matched with high-resolution(HR)images in the gallery set,it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR images.To address the above issues,we present a dualstream coupling network with wavelet transform(DSCWT)for the cross-resolution person re-identification task.Firstly,we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images,which is applied to restore the lost detail information of LR images.Then,we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various resolutions.Extensive qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.展开更多
Recent years have witnessed the great success of self-supervised learning(SSL)in recommendation systems.However,SSL recommender models are likely to suffer from spurious correlations,leading to poor generalization.To ...Recent years have witnessed the great success of self-supervised learning(SSL)in recommendation systems.However,SSL recommender models are likely to suffer from spurious correlations,leading to poor generalization.To mitigate spurious correlations,existing work usually pursues ID-based SSL recommendation or utilizes feature engineering to identify spurious features.Nevertheless,ID-based SSL approaches sacrifice the positive impact of invariant features,while feature engineering methods require high-cost human labeling.To address the problems,we aim to automatically mitigate the effect of spurious correlations.This objective requires to 1)automatically mask spurious features without supervision,and 2)block the negative effect transmission from spurious features to other features during SSL.To handle the two challenges,we propose an invariant feature learning framework,which first divides user-item interactions into multiple environments with distribution shifts and then learns a feature mask mechanism to capture invariant features across environments.Based on the mask mechanism,we can remove the spurious features for robust predictions and block the negative effect transmission via mask-guided feature augmentation.Extensive experiments on two datasets demonstrate the effectiveness of the proposed framework in mitigating spurious correlations and improving the generalization abilities of SSL models.展开更多
基金supported by the National Natural Science Foundation of China(61471154,61876057)the Key Research and Development Program of Anhui Province-Special Project of Strengthening Science and Technology Police(202004D07020012).
文摘Person re-identification is a prevalent technology deployed on intelligent surveillance.There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution,yet such models are not applicable to the open world.In real world,the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera inconsistent.When low-resolution(LR)images in the query set are matched with high-resolution(HR)images in the gallery set,it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR images.To address the above issues,we present a dualstream coupling network with wavelet transform(DSCWT)for the cross-resolution person re-identification task.Firstly,we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images,which is applied to restore the lost detail information of LR images.Then,we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various resolutions.Extensive qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.
文摘Recent years have witnessed the great success of self-supervised learning(SSL)in recommendation systems.However,SSL recommender models are likely to suffer from spurious correlations,leading to poor generalization.To mitigate spurious correlations,existing work usually pursues ID-based SSL recommendation or utilizes feature engineering to identify spurious features.Nevertheless,ID-based SSL approaches sacrifice the positive impact of invariant features,while feature engineering methods require high-cost human labeling.To address the problems,we aim to automatically mitigate the effect of spurious correlations.This objective requires to 1)automatically mask spurious features without supervision,and 2)block the negative effect transmission from spurious features to other features during SSL.To handle the two challenges,we propose an invariant feature learning framework,which first divides user-item interactions into multiple environments with distribution shifts and then learns a feature mask mechanism to capture invariant features across environments.Based on the mask mechanism,we can remove the spurious features for robust predictions and block the negative effect transmission via mask-guided feature augmentation.Extensive experiments on two datasets demonstrate the effectiveness of the proposed framework in mitigating spurious correlations and improving the generalization abilities of SSL models.
