Pub Date : 2022-01-01DOI: 10.1109/WACV51458.2022.00177
Anran Liu, Xiangsheng Huang, Tong Li, Pengcheng Ma
In this paper, a fine-to-finer segmentation task is investigated driven by region and contour features collaboratively on Glomerular Electron-Dense Deposits (GEDD) in view of the complementary nature of these two types of features. To this end, a novel network (Co-Net) is presented to dynamically use fine saliency segmentation to guide finer segmentation on boundaries. The whole architecture contains double mutually boosted decoders sharing one common encoder. Specifically, a new structure named Global-guided Interaction Module (GIM) is designed to effectively control the information flow and reduce redundancy in the cross-level feature fusion process. At the same time, the global features are used in it to make the features of each layer gain access to richer context, and a fine segmentation map is obtained initially; Discontinuous Boundary Supervision (DBS) strategy is applied to pay more attention to discontinuity positions and modifying segmentation errors on boundaries. At last, Selective Kernel (SK) is used for dynamical aggregation of the region and contour features to obtain a finer segmentation. Our proposed approach is evaluated on an independent GEDD dataset labeled by pathologists and also on open polyp datasets to test the generalization. Ablation studies show the effectiveness of different modules. On all datasets, our proposal achieves high segmentation accuracy and surpasses previous methods.
{"title":"Co-Net: A Collaborative Region-Contour-Driven Network for Fine-to-Finer Medical Image Segmentation","authors":"Anran Liu, Xiangsheng Huang, Tong Li, Pengcheng Ma","doi":"10.1109/WACV51458.2022.00177","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00177","url":null,"abstract":"In this paper, a fine-to-finer segmentation task is investigated driven by region and contour features collaboratively on Glomerular Electron-Dense Deposits (GEDD) in view of the complementary nature of these two types of features. To this end, a novel network (Co-Net) is presented to dynamically use fine saliency segmentation to guide finer segmentation on boundaries. The whole architecture contains double mutually boosted decoders sharing one common encoder. Specifically, a new structure named Global-guided Interaction Module (GIM) is designed to effectively control the information flow and reduce redundancy in the cross-level feature fusion process. At the same time, the global features are used in it to make the features of each layer gain access to richer context, and a fine segmentation map is obtained initially; Discontinuous Boundary Supervision (DBS) strategy is applied to pay more attention to discontinuity positions and modifying segmentation errors on boundaries. At last, Selective Kernel (SK) is used for dynamical aggregation of the region and contour features to obtain a finer segmentation. Our proposed approach is evaluated on an independent GEDD dataset labeled by pathologists and also on open polyp datasets to test the generalization. Ablation studies show the effectiveness of different modules. On all datasets, our proposal achieves high segmentation accuracy and surpasses previous methods.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123010863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1109/WACV51458.2022.00267
Jiawei Liu, Jing Zhang, N. Barnes
Aleatoric uncertainty captures noise within the observations. For camouflaged object detection, due to similar appearance of the camouflaged foreground and the back-ground, it’s difficult to obtain highly accurate annotations, especially annotations around object boundaries. We argue that training directly with the "noisy" camouflage map may lead to a model of poor generalization ability. In this paper, we introduce an explicitly aleatoric uncertainty estimation technique to represent predictive uncertainty due to noisy labeling. Specifically, we present a confidence-aware camouflaged object detection (COD) framework using dynamic supervision to produce both an accurate camouflage map and a reliable "aleatoric uncertainty". Different from existing techniques that produce deterministic prediction following the point estimation pipeline, our framework formalises aleatoric uncertainty as probability distribution over model output and the input image. We claim that, once trained, our confidence estimation network can evaluate the pixel-wise accuracy of the prediction without relying on the ground truth camouflage map. Extensive results illustrate the superior performance of the proposed model in explaining the camouflage prediction. Our codes are available at https://github.com/Carlisle-Liu/OCENet
{"title":"Modeling Aleatoric Uncertainty for Camouflaged Object Detection","authors":"Jiawei Liu, Jing Zhang, N. Barnes","doi":"10.1109/WACV51458.2022.00267","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00267","url":null,"abstract":"Aleatoric uncertainty captures noise within the observations. For camouflaged object detection, due to similar appearance of the camouflaged foreground and the back-ground, it’s difficult to obtain highly accurate annotations, especially annotations around object boundaries. We argue that training directly with the \"noisy\" camouflage map may lead to a model of poor generalization ability. In this paper, we introduce an explicitly aleatoric uncertainty estimation technique to represent predictive uncertainty due to noisy labeling. Specifically, we present a confidence-aware camouflaged object detection (COD) framework using dynamic supervision to produce both an accurate camouflage map and a reliable \"aleatoric uncertainty\". Different from existing techniques that produce deterministic prediction following the point estimation pipeline, our framework formalises aleatoric uncertainty as probability distribution over model output and the input image. We claim that, once trained, our confidence estimation network can evaluate the pixel-wise accuracy of the prediction without relying on the ground truth camouflage map. Extensive results illustrate the superior performance of the proposed model in explaining the camouflage prediction. Our codes are available at https://github.com/Carlisle-Liu/OCENet","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121240749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, we focus on supervised domain adaptation for object detection in few-shot loose annotation setting, where the source images are sufficient and fully labeled but the target images are few-shot and loosely annotated. As annotated objects exist in the target domain, instance level alignment can be utilized to improve the performance. Traditional methods conduct the instance level alignment by semantically aligning the distributions of paired object features with domain adversarial training. Although it is demonstrated that point-wise surrogates of distribution alignment provide a more effective solution in few-shot classification tasks across domains, this point-wise alignment approach has not yet been extended to object detection. In this work, we propose a method that extends the point-wise alignment from classification to object detection. Moreover, in the few-shot loose annotation setting, the background ROIs of target domain suffer from severe label noise problem, which may make the point-wise alignment fail. To this end, we exploit moving average centroids to mitigate the label noise problem of background ROIs. Meanwhile, we exploit point-wise alignment over instances and centroids to tackle the problem of scarcity of labeled target instances. Hence this method is not only robust against label noises of background ROIs but also robust against the scarcity of labeled target objects. Experimental results show that the proposed instance level alignment method brings significant improvement compared with the baseline and is superior to state-of-the-art methods.
{"title":"PICA: Point-wise Instance and Centroid Alignment Based Few-shot Domain Adaptive Object Detection with Loose Annotations","authors":"Chaoliang Zhong, Jiexi Wang, Chengang Feng, Ying Zhang, Jun Sun, Yasuto Yokota","doi":"10.1109/WACV51458.2022.00047","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00047","url":null,"abstract":"In this work, we focus on supervised domain adaptation for object detection in few-shot loose annotation setting, where the source images are sufficient and fully labeled but the target images are few-shot and loosely annotated. As annotated objects exist in the target domain, instance level alignment can be utilized to improve the performance. Traditional methods conduct the instance level alignment by semantically aligning the distributions of paired object features with domain adversarial training. Although it is demonstrated that point-wise surrogates of distribution alignment provide a more effective solution in few-shot classification tasks across domains, this point-wise alignment approach has not yet been extended to object detection. In this work, we propose a method that extends the point-wise alignment from classification to object detection. Moreover, in the few-shot loose annotation setting, the background ROIs of target domain suffer from severe label noise problem, which may make the point-wise alignment fail. To this end, we exploit moving average centroids to mitigate the label noise problem of background ROIs. Meanwhile, we exploit point-wise alignment over instances and centroids to tackle the problem of scarcity of labeled target instances. Hence this method is not only robust against label noises of background ROIs but also robust against the scarcity of labeled target objects. Experimental results show that the proposed instance level alignment method brings significant improvement compared with the baseline and is superior to state-of-the-art methods.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133433044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1109/WACV51458.2022.00357
Z. Wang, Chengcheng Li
Channel pruning has become an effective yet still challenging approach to achieve compact neural networks. It aims to prune the optimal set of filters whose removal results in minimal performance degradation of the slimmed network. Due to the prohibitively vast search space of filter combinations, existing approaches usually use various criteria to estimate the filter importance while sacrificing some precision. Here we present a new approach to optimizing the filter selection in channel pruning with lookahead search guided reinforcement learning (RL). A neural network that takes as input filterrelated features is trained with RL to prune the optimal sequence of filters and maximize the performance of the remaining network. In addition, we employ Monte Carlo tree search (MCTS) to provide a lookahead search for filter selection, which increases the sample efficiency for the RL training. Experiments on MNIST, CIFAR-10, and ILSVRC-2012 validate the effectiveness of our approach compared to both traditional and automated existing channel pruning approaches.
{"title":"Channel Pruning via Lookahead Search Guided Reinforcement Learning","authors":"Z. Wang, Chengcheng Li","doi":"10.1109/WACV51458.2022.00357","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00357","url":null,"abstract":"Channel pruning has become an effective yet still challenging approach to achieve compact neural networks. It aims to prune the optimal set of filters whose removal results in minimal performance degradation of the slimmed network. Due to the prohibitively vast search space of filter combinations, existing approaches usually use various criteria to estimate the filter importance while sacrificing some precision. Here we present a new approach to optimizing the filter selection in channel pruning with lookahead search guided reinforcement learning (RL). A neural network that takes as input filterrelated features is trained with RL to prune the optimal sequence of filters and maximize the performance of the remaining network. In addition, we employ Monte Carlo tree search (MCTS) to provide a lookahead search for filter selection, which increases the sample efficiency for the RL training. Experiments on MNIST, CIFAR-10, and ILSVRC-2012 validate the effectiveness of our approach compared to both traditional and automated existing channel pruning approaches.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133588371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1109/WACV51458.2022.00336
Nikolaus Salvatore, Justin Fletcher
The detection of dim artificial Earth satellites using ground-based electro-optical sensors, particularly in the presence of background light, is technologically challenging. This perceptual task is foundational to our understanding of the space environment, and grows in importance as the number, variety, and dynamism of space objects increases. We present a hybrid image- and event-based architecture that leverages dynamic vision sensing technology to detect resident space objects in geosynchronous Earth orbit. Given the asynchronous, one-dimensional image data supplied by a dynamic vision sensor, our architecture applies conventional image feature extractors to integrated, two-dimensional frames in conjunction with point-cloud feature extractors, such as PointNet, in order to increase detection performance for dim objects in scenes with high background activity. In addition, an end-to-end event-based imaging simulator is developed to both produce data for model training as well as approximate the optimal sensor parameters for event-based sensing in the context of electrooptical telescope imagery. Experimental results confirm that the inclusion of point-cloud feature extractors increases recall for dim objects in the high-background regime.
{"title":"Learned Event-based Visual Perception for Improved Space Object Detection","authors":"Nikolaus Salvatore, Justin Fletcher","doi":"10.1109/WACV51458.2022.00336","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00336","url":null,"abstract":"The detection of dim artificial Earth satellites using ground-based electro-optical sensors, particularly in the presence of background light, is technologically challenging. This perceptual task is foundational to our understanding of the space environment, and grows in importance as the number, variety, and dynamism of space objects increases. We present a hybrid image- and event-based architecture that leverages dynamic vision sensing technology to detect resident space objects in geosynchronous Earth orbit. Given the asynchronous, one-dimensional image data supplied by a dynamic vision sensor, our architecture applies conventional image feature extractors to integrated, two-dimensional frames in conjunction with point-cloud feature extractors, such as PointNet, in order to increase detection performance for dim objects in scenes with high background activity. In addition, an end-to-end event-based imaging simulator is developed to both produce data for model training as well as approximate the optimal sensor parameters for event-based sensing in the context of electrooptical telescope imagery. Experimental results confirm that the inclusion of point-cloud feature extractors increases recall for dim objects in the high-background regime.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133694593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1109/WACV51458.2022.00068
Ahmed Abdelreheem, Ujjwal Upadhyay, Ivan Skorokhodov, Rawan Al Yahya, Jun Chen, Mohamed Elhoseiny
In this paper, we study fine-grained 3D object identification in real-world scenes described by a textual query. The task aims to discriminatively understand an instance of a particular 3D object described by natural language utterances among other instances of 3D objects of the same class appearing in a visual scene. We introduce the 3DRefTransformer net, a transformer-based neural network that identifies 3D objects described by linguistic utterances in real-world scenes. The network’s input is 3D object segmented point cloud images representing a real-world scene and a language utterance that refers to one of the scene objects. The goal is to identify the referred object. Compared to the state-of-the-art models that are mostly based on graph convolutions and LSTMs, our 3DRefTrans-former net offers two key advantages. First, it is an end-to-end transformer model that operates both on language and 3D visual objects. Second, it has a natural ability to ground textual terms in the utterance to the learning representation of 3D objects in the scene. We further incorporate object pairwise spatial relation loss and contrastive learning during model training. We show in our experiments that our model improves the performance upon the current SOTA significantly on Referit3D Nr3D and Sr3D datasets. Code and Models will be made publicly available at https://vision-cair.github.io/3dreftransformer/.
{"title":"3DRefTransformer: Fine-Grained Object Identification in Real-World Scenes Using Natural Language","authors":"Ahmed Abdelreheem, Ujjwal Upadhyay, Ivan Skorokhodov, Rawan Al Yahya, Jun Chen, Mohamed Elhoseiny","doi":"10.1109/WACV51458.2022.00068","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00068","url":null,"abstract":"In this paper, we study fine-grained 3D object identification in real-world scenes described by a textual query. The task aims to discriminatively understand an instance of a particular 3D object described by natural language utterances among other instances of 3D objects of the same class appearing in a visual scene. We introduce the 3DRefTransformer net, a transformer-based neural network that identifies 3D objects described by linguistic utterances in real-world scenes. The network’s input is 3D object segmented point cloud images representing a real-world scene and a language utterance that refers to one of the scene objects. The goal is to identify the referred object. Compared to the state-of-the-art models that are mostly based on graph convolutions and LSTMs, our 3DRefTrans-former net offers two key advantages. First, it is an end-to-end transformer model that operates both on language and 3D visual objects. Second, it has a natural ability to ground textual terms in the utterance to the learning representation of 3D objects in the scene. We further incorporate object pairwise spatial relation loss and contrastive learning during model training. We show in our experiments that our model improves the performance upon the current SOTA significantly on Referit3D Nr3D and Sr3D datasets. Code and Models will be made publicly available at https://vision-cair.github.io/3dreftransformer/.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125647411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1109/WACV51458.2022.00342
Miriam Farber, Roman Goldenberg, G. Leifman, Gal Novich
We revisit existing ensemble diversification approaches and present two novel diversification methods tailored for open-set scenarios. The first method uses a new loss, designed to encourage models disagreement on outliers only, thus alleviating the intrinsic accuracy-diversity trade-off. The second method achieves diversity via automated feature engineering, by training each model to disregard input features learned by previously trained ensemble models. We conduct an extensive evaluation and analysis of the proposed techniques on seven datasets that cover image classification, re-identification and recognition domains. We compare to and demonstrate accuracy improvements over the existing state-of-the-art ensemble diversification methods.
{"title":"Novel Ensemble Diversification Methods for Open-Set Scenarios","authors":"Miriam Farber, Roman Goldenberg, G. Leifman, Gal Novich","doi":"10.1109/WACV51458.2022.00342","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00342","url":null,"abstract":"We revisit existing ensemble diversification approaches and present two novel diversification methods tailored for open-set scenarios. The first method uses a new loss, designed to encourage models disagreement on outliers only, thus alleviating the intrinsic accuracy-diversity trade-off. The second method achieves diversity via automated feature engineering, by training each model to disregard input features learned by previously trained ensemble models. We conduct an extensive evaluation and analysis of the proposed techniques on seven datasets that cover image classification, re-identification and recognition domains. We compare to and demonstrate accuracy improvements over the existing state-of-the-art ensemble diversification methods.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124334643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1109/WACV51458.2022.00080
Camilo Pestana, Naveed Akhtar, N. Rahnavard, M. Shah, A. Mian
Deep visual models are known to be vulnerable to adversarial attacks. The last few years have seen numerous techniques to compute adversarial inputs for these models. However, there are still under-explored avenues in this critical research direction. Among those is the estimation of adversarial textures for 3D models in an end-to-end optimization scheme. In this paper, we propose such a scheme to generate adversarial textures for 3D models that are highly transferable and invariant to different camera views and lighting conditions. Our method makes use of neural rendering with explicit control over the model texture and background. We ensure transferability of the adversarial textures by employing an ensemble of robust and non-robust models. Our technique utilizes 3D models as a proxy to simulate closer to real-life conditions, in contrast to conventional use of 2D images for adversarial attacks. We show the efficacy of our method with extensive experiments.
{"title":"Transferable 3D Adversarial Textures using End-to-end Optimization","authors":"Camilo Pestana, Naveed Akhtar, N. Rahnavard, M. Shah, A. Mian","doi":"10.1109/WACV51458.2022.00080","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00080","url":null,"abstract":"Deep visual models are known to be vulnerable to adversarial attacks. The last few years have seen numerous techniques to compute adversarial inputs for these models. However, there are still under-explored avenues in this critical research direction. Among those is the estimation of adversarial textures for 3D models in an end-to-end optimization scheme. In this paper, we propose such a scheme to generate adversarial textures for 3D models that are highly transferable and invariant to different camera views and lighting conditions. Our method makes use of neural rendering with explicit control over the model texture and background. We ensure transferability of the adversarial textures by employing an ensemble of robust and non-robust models. Our technique utilizes 3D models as a proxy to simulate closer to real-life conditions, in contrast to conventional use of 2D images for adversarial attacks. We show the efficacy of our method with extensive experiments.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133503645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1109/WACV51458.2022.00014
Haesoo Chung, N. Cho
High dynamic range (HDR) imaging is a highly challenging task since a large amount of information is lost due to the limitations of camera sensors. For HDR imaging, some methods capture multiple low dynamic range (LDR) images with altering exposures to aggregate more information. However, these approaches introduce ghosting artifacts when significant inter-frame motions are present. Moreover, although multi-exposure images are given, we have little information in severely over-exposed areas. Most existing methods focus on motion compensation, i.e., alignment of multiple LDR shots to reduce the ghosting artifacts, but they still produce unsatisfying results. These methods also rather overlook the need to restore the saturated areas. In this paper, we generate well-aligned multi-exposure features by reformulating a motion alignment problem into a simple brightness adjustment problem. In addition, we propose a coarse-to-fine merging strategy with explicit saturation compensation. The saturated areas are reconstructed with similar well-exposed content using adaptive contextual attention. We demonstrate that our method outperforms the state-of-the-art methods regarding qualitative and quantitative evaluations.
{"title":"High Dynamic Range Imaging of Dynamic Scenes with Saturation Compensation but without Explicit Motion Compensation","authors":"Haesoo Chung, N. Cho","doi":"10.1109/WACV51458.2022.00014","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00014","url":null,"abstract":"High dynamic range (HDR) imaging is a highly challenging task since a large amount of information is lost due to the limitations of camera sensors. For HDR imaging, some methods capture multiple low dynamic range (LDR) images with altering exposures to aggregate more information. However, these approaches introduce ghosting artifacts when significant inter-frame motions are present. Moreover, although multi-exposure images are given, we have little information in severely over-exposed areas. Most existing methods focus on motion compensation, i.e., alignment of multiple LDR shots to reduce the ghosting artifacts, but they still produce unsatisfying results. These methods also rather overlook the need to restore the saturated areas. In this paper, we generate well-aligned multi-exposure features by reformulating a motion alignment problem into a simple brightness adjustment problem. In addition, we propose a coarse-to-fine merging strategy with explicit saturation compensation. The saturated areas are reconstructed with similar well-exposed content using adaptive contextual attention. We demonstrate that our method outperforms the state-of-the-art methods regarding qualitative and quantitative evaluations.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131897867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1109/WACV51458.2022.00312
Chin-Chia Tsai, Tsung-Hsuan Wu, S. Lai
Unsupervised representation learning has been proven to be effective for the challenging anomaly detection and segmentation tasks. In this paper, we propose a multi-scale patch-based representation learning method to extract critical and representative information from normal images. By taking the relative feature similarity between patches of different local distances into account, we can achieve better representation learning. Moreover, we propose a refined way to improve the self-supervised learning strategy, thus allowing our model to learn better geometric relationship between neighboring patches. Through sliding patches of different scales all over an image, our model extracts representative features from each patch and compares them with those in the training set of normal images to detect the anomalous regions. Our experimental results on MVTec AD dataset and BTAD dataset demonstrate the proposed method achieves the state-of-the-art accuracy for both anomaly detection and segmentation.
{"title":"Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation","authors":"Chin-Chia Tsai, Tsung-Hsuan Wu, S. Lai","doi":"10.1109/WACV51458.2022.00312","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00312","url":null,"abstract":"Unsupervised representation learning has been proven to be effective for the challenging anomaly detection and segmentation tasks. In this paper, we propose a multi-scale patch-based representation learning method to extract critical and representative information from normal images. By taking the relative feature similarity between patches of different local distances into account, we can achieve better representation learning. Moreover, we propose a refined way to improve the self-supervised learning strategy, thus allowing our model to learn better geometric relationship between neighboring patches. Through sliding patches of different scales all over an image, our model extracts representative features from each patch and compares them with those in the training set of normal images to detect the anomalous regions. Our experimental results on MVTec AD dataset and BTAD dataset demonstrate the proposed method achieves the state-of-the-art accuracy for both anomaly detection and segmentation.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133199235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：