Accurate alignment of calcium imaging data, which is critical for the extraction of neuronal activity signals, is often hindered by the image noise and the neuronal activity itself. To address the problem, we propose an algorithm named REALS for robust and efficient batch image alignment through simultaneous transformation and low rank and sparse decomposition. REALS is constructed upon our finding that the low rank subspace can be recovered via linear projection, which allows us to perform simultaneous image alignment and decomposition with gradient-based updates. REALS achieves orders-of-magnitude improvement in terms of accuracy and speed compared to the state-of-the-art robust image alignment algorithms.
{"title":"Robust and Efficient Alignment of Calcium Imaging Data through Simultaneous Low Rank and Sparse Decomposition","authors":"Junmo Cho, Seungjae Han, Eun-Seo Cho, Kijung Shin, Young-Gyu Yoon","doi":"10.1109/WACV56688.2023.00198","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00198","url":null,"abstract":"Accurate alignment of calcium imaging data, which is critical for the extraction of neuronal activity signals, is often hindered by the image noise and the neuronal activity itself. To address the problem, we propose an algorithm named REALS for robust and efficient batch image alignment through simultaneous transformation and low rank and sparse decomposition. REALS is constructed upon our finding that the low rank subspace can be recovered via linear projection, which allows us to perform simultaneous image alignment and decomposition with gradient-based updates. REALS achieves orders-of-magnitude improvement in terms of accuracy and speed compared to the state-of-the-art robust image alignment algorithms.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116706308","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 : 2023-01-01DOI: 10.1109/WACV56688.2023.00158
Shentong Mo, Zhun Sun, Chao Li
Contrastive learning has shown its effectiveness in image classification and generation. Recent works apply contrastive learning to the discriminator of the Generative Adversarial Networks. However, there is little work exploring if contrastive learning can be applied to the encoderdecoder structure to learn disentangled representations. In this work, we propose a simple yet effective method via incorporating contrastive learning into latent optimization, where we name it ContraLORD. Specifically, we first use a generator to learn discriminative and disentangled embeddings via latent optimization. Then an encoder and two momentum encoders are applied to dynamically learn disentangled information across a large number of samples with content-level and residual-level contrastive loss. In the meanwhile, we tune the encoder with the learned embeddings in an amortized manner. We evaluate our approach on ten benchmarks regarding representation disentanglement and linear classification. Extensive experiments demonstrate the effectiveness of our ContraLORD on learning both discriminative and generative representations.
{"title":"Representation Disentanglement in Generative Models with Contrastive Learning","authors":"Shentong Mo, Zhun Sun, Chao Li","doi":"10.1109/WACV56688.2023.00158","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00158","url":null,"abstract":"Contrastive learning has shown its effectiveness in image classification and generation. Recent works apply contrastive learning to the discriminator of the Generative Adversarial Networks. However, there is little work exploring if contrastive learning can be applied to the encoderdecoder structure to learn disentangled representations. In this work, we propose a simple yet effective method via incorporating contrastive learning into latent optimization, where we name it ContraLORD. Specifically, we first use a generator to learn discriminative and disentangled embeddings via latent optimization. Then an encoder and two momentum encoders are applied to dynamically learn disentangled information across a large number of samples with content-level and residual-level contrastive loss. In the meanwhile, we tune the encoder with the learned embeddings in an amortized manner. We evaluate our approach on ten benchmarks regarding representation disentanglement and linear classification. Extensive experiments demonstrate the effectiveness of our ContraLORD on learning both discriminative and generative representations.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115422818","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 : 2023-01-01DOI: 10.1109/WACV56688.2023.00185
Mrinmoy Sen, Sai Pradyumna Chermala, Nazrinbanu Nurmohammad Nagori, V. Peddigari, Praful Mathur, B. H. P. Prasad, Moonsik Jeong
Shadow Removal is an important and widely researched topic in computer vision. Recent advances in deep learning have resulted in addressing this problem by using convolutional neural networks (CNNs) similar to other vision tasks. But these existing works are limited to low-resolution images. Furthermore, the existing methods rely on heavy network architectures which cannot be deployed on resource-constrained platforms like smartphones. In this paper, we propose SHARDS, a shadow removal method for high-resolution images. The proposed method solves shadow removal for high-resolution images in two stages using two lightweight networks: a Low-resolution Shadow Removal Network (LSRNet) followed by a Detail Refinement Network (DRNet). LSRNet operates at low-resolution and computes a low-resolution, shadow-free output. It achieves state-of-the-art results on standard datasets with 65x lesser network parameters than existing methods. This is followed by DRNet, which is tasked to refine the low-resolution output to a high-resolution output using the high-resolution input shadow image as guidance. We construct high-resolution shadow removal datasets and through our experiments, prove the effectiveness of our proposed method on them. It is then demonstrated that this method can be deployed on modern day smartphones and is the first of its kind solution that can efficiently (2.4secs) perform shadow removal for high-resolution images (12MP) in these devices. Like many existing approaches, our shadow removal network relies on a shadow region mask as input to the network. To complement the lightweight shadow removal network, we also propose a lightweight shadow detector in this paper.
{"title":"SHARDS: Efficient SHAdow Removal using Dual Stage Network for High-Resolution Images","authors":"Mrinmoy Sen, Sai Pradyumna Chermala, Nazrinbanu Nurmohammad Nagori, V. Peddigari, Praful Mathur, B. H. P. Prasad, Moonsik Jeong","doi":"10.1109/WACV56688.2023.00185","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00185","url":null,"abstract":"Shadow Removal is an important and widely researched topic in computer vision. Recent advances in deep learning have resulted in addressing this problem by using convolutional neural networks (CNNs) similar to other vision tasks. But these existing works are limited to low-resolution images. Furthermore, the existing methods rely on heavy network architectures which cannot be deployed on resource-constrained platforms like smartphones. In this paper, we propose SHARDS, a shadow removal method for high-resolution images. The proposed method solves shadow removal for high-resolution images in two stages using two lightweight networks: a Low-resolution Shadow Removal Network (LSRNet) followed by a Detail Refinement Network (DRNet). LSRNet operates at low-resolution and computes a low-resolution, shadow-free output. It achieves state-of-the-art results on standard datasets with 65x lesser network parameters than existing methods. This is followed by DRNet, which is tasked to refine the low-resolution output to a high-resolution output using the high-resolution input shadow image as guidance. We construct high-resolution shadow removal datasets and through our experiments, prove the effectiveness of our proposed method on them. It is then demonstrated that this method can be deployed on modern day smartphones and is the first of its kind solution that can efficiently (2.4secs) perform shadow removal for high-resolution images (12MP) in these devices. Like many existing approaches, our shadow removal network relies on a shadow region mask as input to the network. To complement the lightweight shadow removal network, we also propose a lightweight shadow detector in this paper.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125031012","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 : 2023-01-01DOI: 10.1109/WACV56688.2023.00011
Yuting Wang, Ricardo Guerrero, V. Pavlovic
Weakly-supervised object detection (WSOD) models attempt to leverage image-level annotations in lieu of accurate but costly-to-obtain object localization labels. This oftentimes leads to substandard object detection and lo-calization at inference time. To tackle this issue, we propose D2F2WOD, a Dual-Domain Fully-to-Weakly Supervised Object Detection framework that leverages synthetic data, annotated with precise object localization, to supplement a natural image target domain, where only image-level labels are available. In its warm-up domain adaptation stage, the model learns a fully-supervised object detector (FSOD) to improve the precision of the object proposals in the target domain, and at the same time learns target-domain-specific and detection-aware proposal features. In its main WSOD stage, a WSOD model is specifically tuned to the target domain. The feature extractor and the object proposal generator of the WSOD model are built upon the fine-tuned FSOD model. We test D2F2WOD on five dual-domain image benchmarks. The results show that our method results in consistently improved object detection and localization compared with state-of-the-art methods.
{"title":"D2F2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation","authors":"Yuting Wang, Ricardo Guerrero, V. Pavlovic","doi":"10.1109/WACV56688.2023.00011","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00011","url":null,"abstract":"Weakly-supervised object detection (WSOD) models attempt to leverage image-level annotations in lieu of accurate but costly-to-obtain object localization labels. This oftentimes leads to substandard object detection and lo-calization at inference time. To tackle this issue, we propose D2F2WOD, a Dual-Domain Fully-to-Weakly Supervised Object Detection framework that leverages synthetic data, annotated with precise object localization, to supplement a natural image target domain, where only image-level labels are available. In its warm-up domain adaptation stage, the model learns a fully-supervised object detector (FSOD) to improve the precision of the object proposals in the target domain, and at the same time learns target-domain-specific and detection-aware proposal features. In its main WSOD stage, a WSOD model is specifically tuned to the target domain. The feature extractor and the object proposal generator of the WSOD model are built upon the fine-tuned FSOD model. We test D2F2WOD on five dual-domain image benchmarks. The results show that our method results in consistently improved object detection and localization compared with state-of-the-art methods.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"56 42","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120839604","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 : 2023-01-01DOI: 10.1109/WACV56688.2023.00190
Ting-Wei Wu, Jia-Hong Huang, Joseph Lin, M. Worring
Automatic machine learning-based (ML-based) medical report generation systems for retinal images suffer from a relative lack of interpretability. Hence, such ML-based systems are still not widely accepted. The main reason is that trust is one of the important motivating aspects of interpretability and humans do not trust blindly. Precise technical definitions of interpretability still lack consensus. Hence, it is difficult to make a human-comprehensible ML-based medical report generation system. Heat maps/saliency maps, i.e., post-hoc explanation approaches, are widely used to improve the interpretability of ML-based medical systems. However, they are well known to be problematic. From an ML-based medical model’s perspective, the highlighted areas of an image are considered important for making a prediction. However, from a doctor’s perspective, even the hottest regions of a heat map contain both useful and non-useful information. Simply localizing the region, therefore, does not reveal exactly what it was in that area that the model considered useful. Hence, the post-hoc explanation-based method relies on humans who probably have a biased nature to decide what a given heat map might mean. Interpretability boosters, in particular expert-defined keywords, are effective carriers of expert domain knowledge and they are human-comprehensible. In this work, we propose to exploit such keywords and a specialized attention-based strategy to build a more human-comprehensible medical report generation system for retinal images. Both keywords and the proposed strategy effectively improve the interpretability. The proposed method achieves state-of-the-art performance under commonly used text evaluation metrics BLEU, ROUGE, CIDEr, and METEOR. Project website: https://github.com/Jhhuangkay/Expert-defined-Keywords-Improve-Interpretability-of-Retinal-Image-Captioning.
{"title":"Expert-defined Keywords Improve Interpretability of Retinal Image Captioning","authors":"Ting-Wei Wu, Jia-Hong Huang, Joseph Lin, M. Worring","doi":"10.1109/WACV56688.2023.00190","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00190","url":null,"abstract":"Automatic machine learning-based (ML-based) medical report generation systems for retinal images suffer from a relative lack of interpretability. Hence, such ML-based systems are still not widely accepted. The main reason is that trust is one of the important motivating aspects of interpretability and humans do not trust blindly. Precise technical definitions of interpretability still lack consensus. Hence, it is difficult to make a human-comprehensible ML-based medical report generation system. Heat maps/saliency maps, i.e., post-hoc explanation approaches, are widely used to improve the interpretability of ML-based medical systems. However, they are well known to be problematic. From an ML-based medical model’s perspective, the highlighted areas of an image are considered important for making a prediction. However, from a doctor’s perspective, even the hottest regions of a heat map contain both useful and non-useful information. Simply localizing the region, therefore, does not reveal exactly what it was in that area that the model considered useful. Hence, the post-hoc explanation-based method relies on humans who probably have a biased nature to decide what a given heat map might mean. Interpretability boosters, in particular expert-defined keywords, are effective carriers of expert domain knowledge and they are human-comprehensible. In this work, we propose to exploit such keywords and a specialized attention-based strategy to build a more human-comprehensible medical report generation system for retinal images. Both keywords and the proposed strategy effectively improve the interpretability. The proposed method achieves state-of-the-art performance under commonly used text evaluation metrics BLEU, ROUGE, CIDEr, and METEOR. Project website: https://github.com/Jhhuangkay/Expert-defined-Keywords-Improve-Interpretability-of-Retinal-Image-Captioning.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124015723","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 : 2023-01-01DOI: 10.1109/WACV56688.2023.00607
Mohammad Saeed Ebrahimi Saadabadi, Sahar Rahimi Malakshan, A. Zafari, Moktari Mostofa, N. Nasrabadi
Currently available face datasets mainly consist of a large number of high-quality and a small number of low-quality samples. As a result, a Face Recognition (FR) network fails to learn the distribution of low-quality samples since they are less frequent during training (underrepresented). Moreover, current state-of-the-art FR training paradigms are based on the sample-to-center comparison (i.e., Softmax-based classifier), which results in a lack of uniformity between train and test metrics. This work integrates a quality-aware learning process at the sample level into the classification training paradigm (QAFace). In this regard, Softmax centers are adaptively guided to pay more attention to low-quality samples by using a quality-aware function. Accordingly, QAFace adds a quality-based adjustment to the updating procedure of the Softmax-based classifier to improve the performance on the underrepresented low-quality samples. Our method adaptively finds and assigns more attention to the recognizable low-quality samples in the training datasets. In addition, QAFace ignores the unrecognizable low-quality samples using the feature magnitude as a proxy for quality. As a result, QAFace prevents class centers from getting distracted from the optimal direction. The proposed method is superior to the state-of-the-art algorithms in extensive experimental results on the CFP-FP, LFW, CPLFW, CALFW, AgeDB, IJB-B, and IJB-C datasets.
{"title":"A Quality Aware Sample-to-Sample Comparison for Face Recognition","authors":"Mohammad Saeed Ebrahimi Saadabadi, Sahar Rahimi Malakshan, A. Zafari, Moktari Mostofa, N. Nasrabadi","doi":"10.1109/WACV56688.2023.00607","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00607","url":null,"abstract":"Currently available face datasets mainly consist of a large number of high-quality and a small number of low-quality samples. As a result, a Face Recognition (FR) network fails to learn the distribution of low-quality samples since they are less frequent during training (underrepresented). Moreover, current state-of-the-art FR training paradigms are based on the sample-to-center comparison (i.e., Softmax-based classifier), which results in a lack of uniformity between train and test metrics. This work integrates a quality-aware learning process at the sample level into the classification training paradigm (QAFace). In this regard, Softmax centers are adaptively guided to pay more attention to low-quality samples by using a quality-aware function. Accordingly, QAFace adds a quality-based adjustment to the updating procedure of the Softmax-based classifier to improve the performance on the underrepresented low-quality samples. Our method adaptively finds and assigns more attention to the recognizable low-quality samples in the training datasets. In addition, QAFace ignores the unrecognizable low-quality samples using the feature magnitude as a proxy for quality. As a result, QAFace prevents class centers from getting distracted from the optimal direction. The proposed method is superior to the state-of-the-art algorithms in extensive experimental results on the CFP-FP, LFW, CPLFW, CALFW, AgeDB, IJB-B, and IJB-C datasets.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"1981 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130297215","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 : 2023-01-01DOI: 10.1109/WACV56688.2023.00580
Jamie Watson, S. Vicente, Oisin Mac Aodha, Clément Godard, G. Brostow, Michael Firman
3D scene reconstruction from a sequence of posed RGB images is a cornerstone task for computer vision and augmented reality (AR). While depth-based fusion is the foundation of most real-time approaches for 3D reconstruction, recent learning based methods that operate directly on RGB images can achieve higher quality reconstructions, but at the cost of increased runtime and memory requirements, making them unsuitable for AR applications. We propose an efficient learning-based method that refines the 3D reconstruction obtained by a traditional fusion approach. By leveraging a top-down heightfield representation, our method remains real-time while approaching the quality of other learning-based methods. Despite being a simplification, our heightfield is perfectly appropriate for robotic path planning or augmented reality character placement. We outline several innovations that push the performance beyond existing top-down prediction baselines, and we present an evaluation framework on the challenging ScanNetV2 dataset, targeting AR tasks.
{"title":"Heightfields for Efficient Scene Reconstruction for AR","authors":"Jamie Watson, S. Vicente, Oisin Mac Aodha, Clément Godard, G. Brostow, Michael Firman","doi":"10.1109/WACV56688.2023.00580","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00580","url":null,"abstract":"3D scene reconstruction from a sequence of posed RGB images is a cornerstone task for computer vision and augmented reality (AR). While depth-based fusion is the foundation of most real-time approaches for 3D reconstruction, recent learning based methods that operate directly on RGB images can achieve higher quality reconstructions, but at the cost of increased runtime and memory requirements, making them unsuitable for AR applications. We propose an efficient learning-based method that refines the 3D reconstruction obtained by a traditional fusion approach. By leveraging a top-down heightfield representation, our method remains real-time while approaching the quality of other learning-based methods. Despite being a simplification, our heightfield is perfectly appropriate for robotic path planning or augmented reality character placement. We outline several innovations that push the performance beyond existing top-down prediction baselines, and we present an evaluation framework on the challenging ScanNetV2 dataset, targeting AR tasks.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130056256","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 : 2023-01-01DOI: 10.1109/WACV56688.2023.00577
A. Ramesh, F. Giovanneschi, M. González-Huici
Depth completion is the task of generating dense depth images from sparse depth measurements, e.g., LiDARs. Existing unguided approaches fail to recover dense depth images with sharp object boundaries due to depth bleeding, especially from extremely sparse measurements. State-of-the-art guided approaches require additional processing for spatial and temporal alignment of multi-modal inputs, and sophisticated architectures for data fusion, making them non-trivial for customized sensor setup. To address these limitations, we propose an unguided approach based on U-Net that is invariant to sparsity of inputs. Boundary consistency in reconstruction is explicitly enforced through auxiliary learning on a synthetic dataset with dense depth and depth contour images as targets, followed by fine-tuning on a real-world dataset. With our network architecture and simple implementation approach, we achieve competitive results among unguided approaches on KITTI benchmark and show that the reconstructed image has sharp boundaries and is robust even towards extremely sparse LiDAR measurements.
{"title":"SIUNet: Sparsity Invariant U-Net for Edge-Aware Depth Completion","authors":"A. Ramesh, F. Giovanneschi, M. González-Huici","doi":"10.1109/WACV56688.2023.00577","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00577","url":null,"abstract":"Depth completion is the task of generating dense depth images from sparse depth measurements, e.g., LiDARs. Existing unguided approaches fail to recover dense depth images with sharp object boundaries due to depth bleeding, especially from extremely sparse measurements. State-of-the-art guided approaches require additional processing for spatial and temporal alignment of multi-modal inputs, and sophisticated architectures for data fusion, making them non-trivial for customized sensor setup. To address these limitations, we propose an unguided approach based on U-Net that is invariant to sparsity of inputs. Boundary consistency in reconstruction is explicitly enforced through auxiliary learning on a synthetic dataset with dense depth and depth contour images as targets, followed by fine-tuning on a real-world dataset. With our network architecture and simple implementation approach, we achieve competitive results among unguided approaches on KITTI benchmark and show that the reconstructed image has sharp boundaries and is robust even towards extremely sparse LiDAR measurements.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130180179","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 : 2023-01-01DOI: 10.1109/WACV56688.2023.00083
Hanbit Lee, Youna Kim, Sang-goo Lee
Recent advances in Generative Adversarial Networks (GANs) have enabled photo-realistic synthesis of single object images. Yet, modeling more complex distributions, such as scenes with multiple objects, remains challenging. The difficulty stems from the incalculable variety of scene configurations which contain multiple objects of different categories placed at various locations. In this paper, we aim to alleviate the difficulty by enhancing the discriminative ability of the discriminator through a locally defined self-supervised pretext task. To this end, we design a discriminator to leverage multi-scale local feedback that guides the generator to better model local semantic structures in the scene. Then, we require the discriminator to carry out pixel-level contrastive learning at multiple scales to enhance discriminative capability on local regions. Experimental results on several challenging scene datasets show that our method improves the synthesis quality by a substantial margin compared to state-of-the-art baselines.
{"title":"Multi-scale Contrastive Learning for Complex Scene Generation","authors":"Hanbit Lee, Youna Kim, Sang-goo Lee","doi":"10.1109/WACV56688.2023.00083","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00083","url":null,"abstract":"Recent advances in Generative Adversarial Networks (GANs) have enabled photo-realistic synthesis of single object images. Yet, modeling more complex distributions, such as scenes with multiple objects, remains challenging. The difficulty stems from the incalculable variety of scene configurations which contain multiple objects of different categories placed at various locations. In this paper, we aim to alleviate the difficulty by enhancing the discriminative ability of the discriminator through a locally defined self-supervised pretext task. To this end, we design a discriminator to leverage multi-scale local feedback that guides the generator to better model local semantic structures in the scene. Then, we require the discriminator to carry out pixel-level contrastive learning at multiple scales to enhance discriminative capability on local regions. Experimental results on several challenging scene datasets show that our method improves the synthesis quality by a substantial margin compared to state-of-the-art baselines.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131165439","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 : 2023-01-01DOI: 10.1109/WACV56688.2023.00308
Markus Plack, C. Callenberg, M. Schneider, M. Hullin
Research into non-line-of-sight imaging problems has gained momentum in recent years motivated by intriguing prospective applications in e.g. medicine and autonomous driving. While transient image formation is well understood and there exist various reconstruction approaches for non-line-of-sight scenes that combine efficient forward renderers with optimization schemes, those approaches suffer from runtimes in the order of hours even for moderately sized scenes. Furthermore, the ill-posedness of the inverse problem often leads to instabilities in the optimization.Inspired by the latest advances in direct-line-of-sight inverse rendering that have led to stunning results for reconstructing scene geometry and appearance, we present a fast differentiable transient renderer that accelerates the inverse rendering runtime to minutes on consumer hardware, making it possible to apply inverse transient imaging on a wider range of tasks and in more time-critical scenarios. We demonstrate its effectiveness on a series of applications using various datasets and show that it can be used for self-supervised learning.
{"title":"Fast Differentiable Transient Rendering for Non-Line-of-Sight Reconstruction","authors":"Markus Plack, C. Callenberg, M. Schneider, M. Hullin","doi":"10.1109/WACV56688.2023.00308","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00308","url":null,"abstract":"Research into non-line-of-sight imaging problems has gained momentum in recent years motivated by intriguing prospective applications in e.g. medicine and autonomous driving. While transient image formation is well understood and there exist various reconstruction approaches for non-line-of-sight scenes that combine efficient forward renderers with optimization schemes, those approaches suffer from runtimes in the order of hours even for moderately sized scenes. Furthermore, the ill-posedness of the inverse problem often leads to instabilities in the optimization.Inspired by the latest advances in direct-line-of-sight inverse rendering that have led to stunning results for reconstructing scene geometry and appearance, we present a fast differentiable transient renderer that accelerates the inverse rendering runtime to minutes on consumer hardware, making it possible to apply inverse transient imaging on a wider range of tasks and in more time-critical scenarios. We demonstrate its effectiveness on a series of applications using various datasets and show that it can be used for self-supervised learning.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128725960","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}
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