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NTIRE 2022 Spectral Recovery Challenge and Data Set

NTIRE 2022 Spectral Recovery Challenge and Data Set

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Arad, Boaz Timofte, Radu Yahel, Rony Morag, Nimrod Bernat, Amir Cai, Yuanhao Lin, Jing Lin, Zudi Wang, Haoqian Zhang, Yulun Pfister, Hanspeter Van Gool, Luc Liu, Shuai Li, Yongqiang Feng, Chaoyu Lei, Lei Li, Jiaojiao Du, Songcheng Wu, Chaoxiong Leng, Yihong Song, Rui Zhang, Mingwei Song, Chongxing Zhao, Shuyi Lang, Zhiqiang Wei, Wei Zhang, Lei Dian, Renwei Shan, Tianci Guo, Anjing Feng, Chengguo Liu, Jinyang Agarla, Mirko Bianco, Simone Buzzelli, Marco Celona, Luigi Schettini, Raimondo He, Jiang Xiao, Yi Xiao, Jiajun Yuan, Qiangqiang Li, Jie Zhang, Liangpei Kwon, Taesung Ryu, Dohoon Bae, Hyokyoung Yang, Hao-Hsiang Chang, Hua-En Huang, Zhi-Kai Chen, Wei-Ting Kuo, Sy-Yen Chen, Junyu Li, Haiwei Liu, Song Uma, Sabarinathan K. Bama, B. Sathya Roomi, S. Mohamed Mansoor Oddity Tech Ltd New York NY USA Voyage81 Ltd New York NY USA Swiss Fed Inst Technol Computat Vis Lab Zurich Switzerland Univ Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Acad Coll Tel Aviv Yaffo Tel Aviv Israel Tel Aviv Univ Tel Aviv Israel Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Harvard Univ Visual Comp Grp Cambridge MA USA JMU Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Xiaomi Inc Beijing Peoples R China Xidian Univ Xian Peoples R China Northwestern Polytech Univ Xian Peoples R China Northwestern Polytech Univ Changan Campus Xian Peoples R China Hunan Univ Changsha Hunan Peoples R China Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy Wuhan Univ Sch Geodesy & Geomat Wuhan Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Peoples R China Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Daejeon South Korea Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Peoples R China Couger Inc Tokyo Japan Thiagarajar Coll Engn Kamarajar Salai India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the "ARAD 1K" data set: a new, larger-than-ever natural hyperspectral image data set containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated JPEG-compressed RGB images simulating capture by a known calibrated camera, operating under partially known parameters, in a setting which includes acquisition noise. The challenge was attended by 241 teams, with 60 teams competing in the final testing phase, 12 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current state-of-the-art in spectral reconstruction from natural RGB images.

关键词： Training computer vision Costs conferences Neural networks Cameras pattern recognition

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High Quality Reference Feature for Two Stage Bracketing Image Restoration and Enhancement

High Quality Reference Feature for Two Stage Bracketing Imag...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiaoxia Xing HyunHee Park Fan Wang Ying Zhang Sejun Song Changho Kim Xiangyu Kong Samsung R&D Institute China-Beijing Department of Camera Innovation Group Samsung Electronics

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In a low-light environment, it is difficult to obtain high-quality or high-resolution images with sharp details and high dynamic range (HDR) without noise or blur. To solve this problem, the Bracketing Image Restoration and Enhancement integrates Dnoise, Deblur, HDR Reconstruction, and Super Resolution techniques into a unified framework. However, we find that most methods select the image that aligns with GT as the reference image. Since the details of the reference image are not good enough, seriously affects the feature fusion, which finally leads to details being blurred. To generate a high dynamic range and a high-quality image, we propose a two-stage Bracketing method named RT-IRE. In the first stage, we generate the high-quality reference feature to guide feature fusion, remove the degradation, and reconstruct HDR to get coarse results. The second stage learns the residuals between the coarse result and the GT, which further enhances and generates details. Extensive experiments show the effectiveness of the proposed module. In particular, RT-IRE won two champions in the NTIRE 2024 Bracketing Image Restoration and Enhancement Challenge.

关键词： Degradation computer vision Accuracy conferences Noise Superresolution Image restoration

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Shadow-Mapping for Unsupervised Neural Causal Discovery

Shadow-Mapping for Unsupervised Neural Causal Discovery

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Vowels, Matthew J. Camgoz, Necati Cihan Bowden, Richard Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9781665448994

An important goal across most scientific fields is the discovery of causal structures underling a set of observations. Unfortunately, causal discovery methods which are based on correlation or mutual information can often fail to identify causal links in systems which exhibit dynamic relationships. Such dynamic systems (including the famous coupled logistic map) exhibit 'mirage' correlations which appear and disappear depending on the observation window. This means not only that correlation is not causation but, perhaps counter-intuitively, that causation may occur without correlation. In this paper we describe Neural Shadow-Mapping, a neural network based method which embeds high-dimensional video data into a low-dimensional shadow representation, for subsequent estimation of causal links. We demonstrate its performance at discovering causal links from video-representations of dynamic systems.

关键词： computer vision Correlation conferences Neural networks Estimation pattern recognition Dynamical systems

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Modeling Fashion Compatibility with Explanation by using Bidirectional LSTM

Modeling Fashion Compatibility with Explanation by using Bid...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Pang Kaicheng Zou Xingxing Wong, Wai Keung Hong Kong Polytech Univ Inst Text & Clothing Hong Kong Peoples R China Lab Artificial Intelligence Design Hong Kong Peoples R China

ISBN: (纸本)9781665448994

The goal of this paper is to model the fashion compatibility of an outfit and provide the explanations. We first extract features of all attributes of all items via convolutional neural networks, and then train the bidirectional Long Shortterm Memory (Bi-LSTM) model to learn the compatibility of an outfit by treating these attribute features as a sequence. Gradient penalty regularization is exploited for training inter-factor compatibility net which is used to compute the loss for judgment and provide its explanation which is generated from the recognized reasons related to the judgment. To train and evaluate the proposed approach, we expanded the EVALUATION3 dataset in terms of the number of items and attributes. Experiment results show that our approach can successfully evaluate compatibility with reason.

关键词： Training Industries computer vision Computational modeling conferences Feature extraction pattern recognition

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Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models

Strategies to Improve Real-World Applicability of Laparoscop...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Fiona R. Kolbinger Jiangpeng He Jinge Ma Fengqing Zhu Purdue University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision techniques for surgical decision support. Segmentation performance of such models is traditionally reported using metrics of overlap such as IoU. However, imbalanced and unrealistic representation of classes in the training data and suboptimal selection of reported metrics have the potential to skew nominal segmentation performance and thereby ultimately limit clinical translation. In this work, we systematically analyze the impact of class characteristics (i.e., organ size differences), training and test data composition (i.e., representation of positive and negative examples), and modeling parameters (i.e., foreground-to-background class weight) on eight segmentation metrics: accuracy, precision, recall, IoU, F1 score (Dice Similarity Coefficient), specificity, Hausdorff Distance, and Average Symmetric Surface Distance. Our findings support two adjustments to account for data biases in surgical data science: First, training on datasets that are similar to the clinical real-world scenarios in terms of class distribution, and second, class weight adjustments to optimize segmentation model performance with regard to metrics of particular relevance in the respective clinical setting.

关键词： Measurement Training Laparoscopes Image segmentation computer vision Computational modeling Surgery

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Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Image Retrieval

Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Ima...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Fuentes, Anibal Saavedra, Jose M. Impresee Inc 600 Calif St San Francisco CA 94108 USA

ISBN: (纸本)9781665448994

Architectures based on siamese networks with triplet loss have shown outstanding performance on the image-based similarity search problem. This approach attempts to discriminate between positive (relevant) and negative (irrelevant) items. However, it undergoes a critical weakness. Given a query, it cannot discriminate weakly relevant items, for instance, items of the same type but different color or texture as the given query, which could be a serious limitation for many real-world search applications. Therefore, in this work, we present a quadruplet-based architecture that overcomes the aforementioned weakness. Moreover, we present an instance of this quadruplet network, which we call Sketch-QNet, to deal with the color sketch-based image retrieval (CSBIR) problem, achieving new state-of-the-art results.

关键词： Training Visualization computer vision Image color analysis conferences Image retrieval computer architecture

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Drone-HAT: Hybrid Attention Transformer for Complex Action recognition in Drone Surveillance Videos

Drone-HAT: Hybrid Attention Transformer for Complex Action R...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mustaqeem Khan Jamil Ahmad Abdulmotaleb El Saddik Wail Gueaieb Giulia De Masi Fakhri Karray MBZUAI Abu Dhabi UAE University of Ottawa Canada Technology Innovation Institute UAE University of Waterloo Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Ultra-high-resolution aerial videos are becoming increasingly popular for enhancing surveillance capabilities in sparsely populated areas. However, analyzing human activities automatically, such as "who is doing what?" in these videos, is desirable to realize their surveillance potential. In contrast, atomic visual action detection has successfully recognized such activities in movie data. However, adapting it to ultra-high resolution aerial videos is challenging because the target persons appear relatively tiny from overhead views and are sparsely located. Additionally, existing atomic visual action detection methods are based on single-label actions. However, people can perform multiple actions simultaneously, so a multi-label approach would be more appropriate. To address these problems, we propose a multi-label action detection/recognition framework using a hybrid attention vision transformer (HAT) to recognize recurrent actions more efficiently. Additionally, a multi-scale, multi-granularity module inside the action recognition transformer block extracts relevant features without redundancy. Using the Okutama Dataset, we demonstrated that our method performs better than existing state-of-the-art methodologies for interpreting aerial videos for human activity.

关键词： Training Visualization computer vision Surveillance Termination of employment Transformers Feature extraction

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HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves

HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classif...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： William Michael Laprade Pawel Pieta Svetlana Kutuzova Jesper Cairo Westergaard Mads Nielsen Svend Christensen Anders Bjorholm Dahl Department of Applied Mathematics and Computer Science Technical University of Denmark Department of Computer Science University of Copenhagen Department of Plant and Environmental Sciences University of Copenhagen

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hyperspectral imaging is a widely used method in remote sensing, particularly for use in airborne and satellite-based land surveillance. Its versatility is, however, much larger and has also seen usage in everything ranging from food processing and surveillance to astronomy and waste sorting. It is also gaining inroads with agricultural research. With most available datasets focusing on per-pixel classification, there is, however, a potential for hyperspectral whole-image analysis, but there is a severe lack of datasets for whole-image analysis. To help fill this gap and facilitate methodological development in whole-image hyperspectral image analysis, we introduce the Hy-perLeaf2024 dataset. The dataset consists of 2410 hyper-spectral images of wheat leaves, along with associated classification and regression targets at both the leaf level and the plot level. In addition to the dataset, we also provide experiments showing the importance of pretraining and highlighting the future research direction in whole-image hyper-spectral image analysis.

关键词： Adaptation models Image analysis Image coding Surveillance conferences Focusing Distance measurement

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ObjectGraphs: Using Objects and a Graph Convolutional Network for the Bottom-up recognition and Explanation of Events in Video

ObjectGraphs: Using Objects and a Graph Convolutional Networ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gkalelis, Nikolaos Goulas, Andreas Galanopoulos, Damianos Mezaris, Vasileios CERTH ITI 6th Km Charilaou Thermi RdPOB 60361 Thessaloniki Greece

ISBN: (纸本)9781665448994

In this paper a novel bottom-up video event recognition approach is proposed, ObjectGraphs, which utilizes a rich frame representation and the relations between objects within each frame. Following the application of an object detector (OD) on the frames, graphs are used to model the object relations and a graph convolutional network (GCN) is utilized to perform reasoning on the graphs. The resulting object-based frame-level features are then forwarded to a long short-term memory (LSTM) network for video event recognition. Moreover, the weighted in-degrees (WiDs) derived from the graph's adjacency matrix at frame level are used for identifying the objects that were considered most (or least) salient for event recognition and contributed the most (or least) to the final event recognition decision, thus providing an explanation for the latter. The experimental results show that the proposed method achieves state-of-the-art performance on the publicly available FCVID and YLI-MED datasets(1).

关键词： computer vision Computational modeling conferences Detectors Cognition pattern recognition Object recognition

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VLM-PL: Advanced Pseudo Labeling approach for Class Incremental Object Detection via vision-Language Model

VLM-PL: Advanced Pseudo Labeling approach for Class Incremen...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Junsu Kim Yunhoe Ku Jihyeon Kim Junuk Cha Seungryul Baek UNIST MODULABS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of Class Incremental Object Detection (CIOD), creating models that can continuously learn like humans is a major challenge. Pseudo-labeling methods, although initially powerful, struggle with multi-scenario incremental learning due to their tendency to forget past knowledge. To overcome this, we introduce a new approach called vision-Language Model assisted Pseudo-Labeling (VLM-PL). This technique uses vision-Language Model (VLM) to verify the correctness of pseudo ground-truths (GTs) without requiring additional model training. VLM-PL starts by deriving pseudo GTs from a pre-trained detector. Then, we generate custom queries for each pseudo GT using carefully designed prompt templates that combine image and text features. This allows the VLM to classify the correctness through its responses. Furthermore, VLM-PL integrates refined pseudo and real GTs from upcoming training, effectively combining new and old knowledge. Extensive experiments conducted on the Pascal VOC and MS COCO datasets not only highlight VLM-PL’s exceptional performance in multi-scenario but also illuminate its effectiveness in dual-scenario by achieving state-of-the-art results in both.

关键词： Training computer vision Incremental learning conferences Computational modeling Object detection Detectors

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