{"title":"Shape and Intensity Analysis of Glioblastoma Multiforme Tumors","authors":"Yi Tang Chen, S. Kurtek","doi":"10.1109/CVPRW59228.2023.00062","DOIUrl":"https://doi.org/10.1109/CVPRW59228.2023.00062","url":null,"abstract":"We use a geometric approach to characterize tumor shape and intensity along the tumor contour in the context of Glioblastoma Multiforme. Properties of the proposed shape+intensity representation include invariance to translation, scale, rotation and reparameterization, which allow for objective comparison of tumor features. Controlling for the weight of intensity information in the shape+intensity representation results in improved comparisons between tumor features of different patients who have been diagnosed with Glioblastoma Multiforme; further, it allows for identification of different partitions of the data associated with different median survival among such patients. Our findings suggest that integrating and appropriately balancing information regarding GBM tumor shape and intensity can be beneficial for disease prognosis. We evaluate the proposed statistical framework using simulated examples as well as a real dataset of Glioblastoma Multiforme tumors.","PeriodicalId":355438,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124651302","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}
Xiangyu Kong, Fan Wang, Dafeng Zhang, Jinlong Wu, Zi-jie Liu
{"title":"NAFBET: Bokeh Effect Transformation with Parameter Analysis Block based on NAFNet","authors":"Xiangyu Kong, Fan Wang, Dafeng Zhang, Jinlong Wu, Zi-jie Liu","doi":"10.1109/CVPRW59228.2023.00162","DOIUrl":"https://doi.org/10.1109/CVPRW59228.2023.00162","url":null,"abstract":"Bokeh effect transformation(BET) aims to transform the bokeh effect of one lens to another lens without harming the sharp foreground regions in the image. Recent studies have shown remarkable success in bokeh effect rendering. However, unlike the traditional bokeh effect rendering task, the BET task needs to transform the image into the bokeh effect of the specified lens. The existing bokeh rendering method is invalid or inefficient for BET, because each pair of lens needs to independently build different model. To address this limitation, we propose NAFBET, a scalable approach than can perform bokeh rendering for multiple lens using only a single model. NAFBET is based on the structure of the image restoration model NAFNet and expands it by adding the source and target parameter analysis block(PAB) to adapt to the BET task. This block can be very convenient to apply in UNet-based model, which can greatly improve BET performance. We did a lot of experiments to prove the effectiveness of our method. In particular, NAFBET won the 1st place in the NTIRE 2023 Bokeh effect transformation Challenge.","PeriodicalId":355438,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129466099","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}
{"title":"Geometry Enhanced Reference-based Image Super-resolution","authors":"Han Zou, Liang Xu, Takayuki Okatani","doi":"10.1109/CVPRW59228.2023.00652","DOIUrl":"https://doi.org/10.1109/CVPRW59228.2023.00652","url":null,"abstract":"With the prevalence of smartphones equipped with a multi-camera system comprising multiple cameras with different field-of-view (FoVs), images captured by two or three cameras now share a portion of the FoV that are compatible with reference-based super-resolution (RefSR). In this work, we propose a novel RefSR model that utilizes geometric matching methods to enhance its performance in two aspects. First, we integrate geometric matching maps to improve feature fusion. Second, we train the matching modules equipped in the RefSR models under the supervision of accurate geometric matching maps to increase their robustness. Our experimental results demonstrate the effectiveness and state-of-the-art performance of the proposed method.","PeriodicalId":355438,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128491292","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}
Alina Shutova, E. Ershov, Georgy Perevozchikov, I. Ermakov, Nikola Banić, R. Timofte, Richard Collins, Maria Efimova, A. Terekhin, Simone Zini, C. Rota, M. Buzzelli, S. Bianco, R. Schettini, C. Lei, Tingniao Wang, Songmei Wang, Shuai Liu, Chaoyu Feng, Guangqi Shao, Hao Wang, Xiaotao Wang, Lei Lei, Lu Xu, Chao Zhang, Yasi Wang, Jin Guo, Yangfan Sun, Tianli Liu, D. Hao, Furkan Kinli, B. Özcan, Mustafa Furkan Kıraç, Hyerin Chung, Nakyung Lee, Sung-Keun Kwak, Marcos V. Conde, Tim Seizinger, Florin-Alexandru Vasluianu, Omar Elezabi, Chia-Hsuan Hsieh, Wei-Ting Chen, Hao-Hsiang Yang, Zhi-Kai Huang, Hua-En Chang, I-Hsiang Chen, Yi-Chung Chen, Yuan Chiang
{"title":"NTIRE 2023 Challenge on Night Photography Rendering","authors":"Alina Shutova, E. Ershov, Georgy Perevozchikov, I. Ermakov, Nikola Banić, R. Timofte, Richard Collins, Maria Efimova, A. Terekhin, Simone Zini, C. Rota, M. Buzzelli, S. Bianco, R. Schettini, C. Lei, Tingniao Wang, Songmei Wang, Shuai Liu, Chaoyu Feng, Guangqi Shao, Hao Wang, Xiaotao Wang, Lei Lei, Lu Xu, Chao Zhang, Yasi Wang, Jin Guo, Yangfan Sun, Tianli Liu, D. Hao, Furkan Kinli, B. Özcan, Mustafa Furkan Kıraç, Hyerin Chung, Nakyung Lee, Sung-Keun Kwak, Marcos V. Conde, Tim Seizinger, Florin-Alexandru Vasluianu, Omar Elezabi, Chia-Hsuan Hsieh, Wei-Ting Chen, Hao-Hsiang Yang, Zhi-Kai Huang, Hua-En Chang, I-Hsiang Chen, Yi-Chung Chen, Yuan Chiang","doi":"10.1109/CVPRW59228.2023.00192","DOIUrl":"https://doi.org/10.1109/CVPRW59228.2023.00192","url":null,"abstract":"This paper presents a review of the NTIRE 2023 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year’s competition, participants were not provided with a large training dataset for the target sensor. Instead, this time they were given images of a color checker illuminated by a known light source. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. The highest ranking solutions were further ranked by Richard Collins, a renowned photographer. The top ranking participants’ solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://nightimaging.org/","PeriodicalId":355438,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128682158","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}
S. Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Sahand Ghorbanpour, Vineet Gundecha, Antonio Guillen, Ricardo Luna, Avisek Naug
{"title":"RL-CAM: Visual Explanations for Convolutional Networks using Reinforcement Learning","authors":"S. Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Sahand Ghorbanpour, Vineet Gundecha, Antonio Guillen, Ricardo Luna, Avisek Naug","doi":"10.1109/CVPRW59228.2023.00400","DOIUrl":"https://doi.org/10.1109/CVPRW59228.2023.00400","url":null,"abstract":"Convolutional Neural Networks (CNNs) are state-of-the-art models for computer vision tasks such as image classification, object detection, and segmentation. However, these models suffer from their inability to explain decisions, particularly in fields like healthcare and security, where interpretability is critical. Previous research has developed various methods for interpreting CNNs, including visualization-based approaches (e.g., saliency maps) that aim to reveal the underlying features used by the model to make predictions. In this work, we propose a novel approach that uses reinforcement learning to generate a visual explanation for CNNs. Our method considers the black-box CNN model and relies solely on the probability distribution of the model’s output to localize the features contributing to a particular prediction. The proposed reinforcement learning algorithm has an agent with two actions, a forward action that explores the input image and identifies the most sensitive region to generate a localization mask, and a reverse action that fine-tunes the localization mask. We evaluate the performance of our approach using multiple image segmentation metrics and compare it with existing visualization-based methods. The experimental results demonstrate that our proposed method outperforms the existing techniques, producing more accurate localization masks of regions of interest in the input images.","PeriodicalId":355438,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128720485","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}
{"title":"A Closer Look at Geometric Temporal Dynamics for Face Anti-Spoofing","authors":"Chih-Jung Chang, Yaw-Chern Lee, Shih-Hsuan Yao, Min-Hung Chen, Chien-Yi Wang, S. Lai, Trista Pei-chun Chen","doi":"10.1109/CVPRW59228.2023.00115","DOIUrl":"https://doi.org/10.1109/CVPRW59228.2023.00115","url":null,"abstract":"Face anti-spoofing (FAS) is indispensable for a face recognition system. Many texture-driven countermeasures were developed against presentation attacks (PAs), but the performance against unseen domains or unseen spoofing types is still unsatisfactory. Instead of exhaustively collecting all the spoofing variations and making binary decisions of live/spoof, we offer a new perspective on the FAS task to distinguish between normal and abnormal movements of live and spoof presentations. We propose Geometry-Aware Interaction Network (GAIN), which exploits dense facial landmarks with spatio-temporal graph convolutional network (ST-GCN) to establish a more interpretable and modularized FAS model. Additionally, with our cross-attention feature interaction mechanism, GAIN can be easily integrated with other existing methods to significantly boost performance. Our approach achieves state-of-the-art performance in the standard intra- and cross-dataset evaluations. Moreover, our model outperforms state-of-the-art methods by a large margin in the cross-dataset cross-type protocol on CASIA-SURF 3DMask (+10.26 higher AUC score), exhibiting strong robustness against domain shifts and unseen spoofing types.","PeriodicalId":355438,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129374615","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}
Pierluigi Zama Ramirez, F. Tosi, L. Di Stefano, R. Timofte, Alex Costanzino, Matteo Poggi, Samuele Salti, S. Mattoccia, Jun Shi, Dafeng Zhang, Yong A, Yixiang Jin, Dingzhe Li, Chao Li, Zhiwen Liu, Qi Zhang, Yixing Wang, S. Yin
{"title":"NTIRE 2023 Challenge on HR Depth from Images of Specular and Transparent Surfaces","authors":"Pierluigi Zama Ramirez, F. Tosi, L. Di Stefano, R. Timofte, Alex Costanzino, Matteo Poggi, Samuele Salti, S. Mattoccia, Jun Shi, Dafeng Zhang, Yong A, Yixiang Jin, Dingzhe Li, Chao Li, Zhiwen Liu, Qi Zhang, Yixing Wang, S. Yin","doi":"10.1109/CVPRW59228.2023.00143","DOIUrl":"https://doi.org/10.1109/CVPRW59228.2023.00143","url":null,"abstract":"This paper reports about the NTIRE 2023 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2023. This challenge is held to boost the research on depth estimation, mainly to deal with two of the open issues in the field: high-resolution images and non-Lambertian surfaces characterizing specular and transparent materials. The challenge is divided into two tracks: a stereo track focusing on disparity estimation from rectified pairs and a mono track dealing with single-image depth estimation. The challenge attracted about 100 registered participants for the two tracks. In the final testing stage, 5 participating teams submitted their models and fact sheets, 2 and 3 for the Stereo and Mono tracks, respectively.","PeriodicalId":355438,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129668318","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}
{"title":"Temporally Averaged Regression for Semi-Supervised Low-Light Image Enhancement","authors":"Sunhyeok Lee, D. Jang, Dae-Shik Kim","doi":"10.1109/CVPRW59228.2023.00443","DOIUrl":"https://doi.org/10.1109/CVPRW59228.2023.00443","url":null,"abstract":"Constructing annotated paired datasets for low-light image enhancement is complex and time-consuming, and existing deep learning models often generate noisy outputs or misinterpret shadows. To effectively learn intricate relationships between features in image space with limited labels, we introduce a deep learning model with a backbone structure that incorporates both spatial and layer-wise dependencies. The proposed model features a baseline image-enhancing network with spatial dependencies and an optimized layer attention mechanism to learn feature sparsity and importance. We present a progressive supervised loss function for improvement. Furthermore, we propose a novel Multi-Consistency Regularization (MCR) loss and integrate it within a Multi-Consistency Mean Teacher (MCMT) framework, which enforces agreement on high-level features and incorporates intermediate features for better understanding of the entire image. By combining the MCR loss with the progressive supervised loss, student network parameters can be updated in a single step. Our approach achieves significant performance improvements using fewer labeled data and unlabeled low-light images within our semi-supervised framework. Qualitative evaluations demonstrate the effectiveness of our method in leveraging comprehensive dependencies and unlabeled data for low-light image enhancement.","PeriodicalId":355438,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127069214","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}
{"title":"KBody: Balanced monocular whole-body estimation","authors":"N. Zioulis, J. F. O'Brien","doi":"10.1109/CVPRW59228.2023.00361","DOIUrl":"https://doi.org/10.1109/CVPRW59228.2023.00361","url":null,"abstract":"KBody is a method for fitting a low-dimensional body model to an image. It follows a predict-and-optimize approach, relying on data-driven model estimates for the constraints that will be used to solve for the body’s parameters. Compared to other approaches, it introduces virtual joints to identify higher quality correspondences and disentangles the optimization between the pose and shape parameters to achieve a more balanced result in terms of pose and shape capturing capacity, as well as pixel alignment.","PeriodicalId":355438,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130010263","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}
{"title":"Integrating Appearance and Spatial-Temporal Information for Multi-Camera People Tracking","authors":"Wenjie Yang, Zhe Xie, Yaoming Wang, Yang Zhang, Xiao Ma, Bing Hao","doi":"10.1109/CVPRW59228.2023.00554","DOIUrl":"https://doi.org/10.1109/CVPRW59228.2023.00554","url":null,"abstract":"Multi-Camera People Tracking (MCPT) is a crucial task in intelligent surveillance systems. However, it presents significant challenges due to issues such as heavy occlusion and variations in appearance that arise from multiple camera perspectives and congested scenarios. In this paper, we propose an effective system that integrates both appearance and spatial-temporal information to address these problems, consisting of three specially designed modules: (1) A Multi-Object Tracking (MOT) method that minimizes ID-switch errors and generates accurate trajectory appearance features for MCPT. (2) A robust intra-camera association method that leverages both appearance and spatial-temporal information. (3) An effective post-processing module comprising multi-step processing. Our proposed system is evaluated on the test set of Track1 for the 2023 AI CITY CHALLENGE, and the experimental results demonstrate its effectiveness, achieving an IDF1 score of 93.31% and ranking 3rd on the leaderboard.","PeriodicalId":355438,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130556571","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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