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N-ROD: a Neuromorphic Dataset for Synthetic-to-Real Domain Adaptation

N-ROD: a Neuromorphic Dataset for Synthetic-to-Real Domain A...

引用

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

作者： Cannici, Marco Plizzari, Chiara Planamente, Mirco Ciccone, Marco Bottino, Andrea Caputo, Barbara Matteucci, Matteo Politecn Milan Milan Italy Politecn Torino Turin Italy Ist Italiano Tecnol Genoa Italy

ISBN: (纸本)9781665448994

Event cameras are novel neuromorphic sensors, which asynchronously capture pixel-level intensity changes in the form of "events". Event simulation from existing RGB datasets is commonly used to overcome the need of large amount of annotated data, which lacks due to the novelty of the event sensors. In this context, the possibility of using event simulation in synthetic scenarios, where data generation is not limited to pre-existing datasets, is to date still unexplored. In this work, we analyze the synth-to-real domain shift in event data, i.e., the gap arising between simulated events obtained from synthetic renderings and those captured with a real camera on real images. To this purpose, we extend to the event modality the popular RGB-D Object Dataset (ROD), which already comes with its synthetic version (SynROD). The resulting Neuromorphic ROD dataset (N-ROD) is the first to enable a synth-to-real analysis on event data, showing the effectiveness of Domain Adaptation techniques in reducing the synth-to-real shift. Moreover, through extensive experiments on multi-modal RGBE data, we show that events can be effectively combined with conventional visual information, encouraging further research in this area. The N-ROD dataset is available at https://***/home/.

关键词： Visualization computer vision Neuromorphics conferences Cameras Rendering (computer graphics) Data models

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NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2024 Challenge on Stereo Image Super-Resolution: Metho...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Longguang Wang Yulan Guo Juncheng Li Hongda Liu Yang Zhao Yingqian Wang Zhi Jin Shuhang Gu Radu Timofte

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of this challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnification factor of × 4 under a limited computational budget. Compared with single image SR, the major challenge of this challenge lies in how to exploit additional information in another viewpoint and how to maintain stereo consistency in the results. This challenge has 2 tracks, including one track on bicubic degradation and one track on real degradations. In total, 108 and 70 participants were successfully registered for each track, respectively. In the test phase, 14 and 13 teams successfully submitted valid results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR.

关键词： Degradation computer vision conferences Superresolution Benchmark testing pattern recognition

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Towards Quantitative Evaluation Metrics for Image Editing Approaches

Towards Quantitative Evaluation Metrics for Image Editing Ap...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Dana Cohen Hochberg Oron Anschel Alon Shoshan Igor Kviatkovsky Manoj Aggarwal Gérard Medioni Amazon

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the rapidly evolving field of Generative AI, this work takes initial steps towards establishing a systematic approach for comparing image editing methods. Currently, there is a lack of quantitative metrics for evaluating image editing tasks, with new methods being evaluated mostly qualitatively. Our methodology involves three key components: 1) The creation of a large synthetic dataset using GAN-Control, which enables the generation of ground-truth images for consistent edits across different facial identities; 2) A matching procedure that pairs the edited images with their corresponding ground-truth; and 3) Application of the Perceptual Distance metric to matched pairs. We assessed the effectiveness of our proposed framework through a user study and a set of simulation experiments. Our results indicate that our approach can rank image-editing methods in a way that aligns with human judgment. This research seeks to lay the foundation for a comprehensive evaluation framework for image editing techniques in subsequent studies, initiating a dialogue on this topic.

关键词： Measurement computer vision Systematics Generative AI conferences pattern recognition Synthetic data

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Context-aware Video Anomaly Detection in Long-Term Datasets

Context-aware Video Anomaly Detection in Long-Term Datasets

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhengye Yang Richard J. Radke Department of ECSE Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video anomaly detection research is generally evaluated on short, isolated benchmark videos only a few minutes long. However, in real-world environments, security cameras observe the same scene for months or years at a time, and the notion of anomalous behavior critically depends on context, such as the time of day, day of week, or schedule of events. Here, we propose a context-aware video anomaly detection algorithm, Trinity, specifically targeted to these scenarios. Trinity is especially well-suited to crowded scenes in which individuals cannot be easily tracked, and anomalies are due to speed, direction, or absence of group motion. Trinity is a contrastive learning framework that aims to learn alignments between context, appearance, and motion, and uses alignment quality to classify videos as normal or anomalous. We evaluate our algorithm on both conventional benchmarks and a public webcam-based dataset we collected that spans more than three months of activity.

关键词： Schedules computer vision Target tracking conferences Contrastive learning Benchmark testing pattern recognition

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Multi-scale Attention-Based Inclination Angles Estimation for Panoramic Camera

Multi-scale Attention-Based Inclination Angles Estimation fo...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yuhao Shan Heyu Chen Jiaying Zhang Shigang Li Jianfeng Li Southwest University Chongqing China Hiroshima City University Hiroshima Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Images taken by panoramic cameras in the upright posture can give viewers a better sense and make the downstream panoramic image-based computer vision tasks easier. To estimate the inclination angles of panoramic camera, we proposed a simple but elegant panoramic image-based network, which combines the advantages of geometry-based and deep-learning-based methods. First, a backbone network with five down-sampling layers is designed to focus on the local distortion features. Then, since non-upright panoramic images have highly uniform geometric distortion for the same camera inclination angles, a multi-scale attention module is proposed for the first time, which can weigh each pixel on the feature maps of the backbone network and allows the network to focus on the global and shallow geometric features. Moreover, apart from angle loss, pixel-level image loss is introduced in our network for the inclination angles estimation task to allow the network to compensate for pixel deviations during training. The experiments show that our method overcomes other leading state-of-the-art methods in this field.

关键词： Training computer vision conferences Estimation Cameras Distortion pattern recognition

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Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning

Unveiling the Anomalies in an Ever-Changing World: A Benchma...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nikola Bugarin Jovana Bugaric Manuel Barusco Davide Dalle Pezze Gian Antonio Susto University of Padova

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes over time in the input data distribution, which may cause a significant decrease in performance. In this study, we investigate the problem of Pixel-Level Anomaly Detection in the Continual Learning setting, where new data arrives over time and the goal is to perform well on new and old data. We implement several state-of-the-art techniques to solve the Anomaly Detection problem in the classic setting and adapt them to work in the Continual Learning setting. To validate the approaches, we use a real-world dataset of images with pixel-based anomalies to provide a reliable benchmark and serve as a foundation for further advancements in the field. We provide a comprehensive analysis, discussing which Anomaly Detection methods and which families of approaches seem more suitable for the Continual Learning setting.

关键词： Continuing education computer vision conferences Benchmark testing pattern recognition Reliability Anomaly detection

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PLM: Partial Label Masking for Imbalanced Multi-label Classification

PLM: Partial Label Masking for Imbalanced Multi-label Classi...

引用

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

作者： Duarte, Kevin Rawat, Yogesh Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9781665448994

Neural networks trained on real-world datasets with long-tailed label distributions are biased towards frequent classes and perform poorly on infrequent classes. The imbalance in the ratio of positive and negative samples for each class skews network output probabilities further from ground-truth distributions. We propose a method, Partial Label Masking (PLM), which utilizes this ratio during training. By stochastically masking labels during loss computation, the method balances this ratio for each class, leading to improved recall on minority classes and improved precision on frequent classes. The ratio is estimated adaptively based on the network's performance by minimizing the KL divergence between predicted and ground-truth distributions. Whereas most existing approaches addressing data imbalance are mainly focused on single-label classification and do not generalize well to the multi-label case, this work proposes a general approach to solve the long-tail data imbalance issue for multi-label classification. PLM is versatile: it can be applied to most objective functions and it can be used alongside other strategies for class imbalance. Our method achieves strong performance when compared to existing methods on both multi-label (MultiMNIST and MSCOCO) and single-label (imbalanced CIFAR-10 and CIFAR-100) image classification datasets.

关键词： Training computer vision conferences Neural networks Linear programming pattern recognition Classification algorithms

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Benchmarking Zero-Shot recognition with vision-Language Models: Challenges on Granularity and Specificity

Benchmarking Zero-Shot Recognition with Vision-Language Mode...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhenlin Xu Yi Zhu Siqi Deng Abhay Mittal Yanbei Chen Manchen Wang Paolo Favaro Joseph Tighe Davide Modolo AWS AI Labs Boson AI Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks like image captioning, they face challenges in open-world settings. Our benchmarks test VLMs’ consistency in understanding concepts across semantic granularity levels and their response to varying text specificity. Findings show that VLMs favor moderately fine-grained concepts and struggle with specificity, often misjudging texts that differ from their training data. Extensive evaluations reveal limitations in current VLMs, particularly in distinguishing between correct and subtly incorrect descriptions. While fine-tuning offers some improvements, it doesn’t fully address these issues, highlighting the need for VLMs with enhanced generalization capabilities for real-world applications. This study provides insights into VLM limitations and suggests directions for developing more robust models.

关键词： computer vision Computational modeling Face recognition conferences Semantics Training data Focusing

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Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment

Self-Supervised Multi-Task Pretraining Improves Image Aesthe...

引用

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

作者： Pfister, Jan Kobs, Konstantin Hotho, Andreas Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9781665448994

Neural networks for Image Aesthetic Assessment are usually initialized with weights of pretrained ImageNet models and then trained using a labeled image aesthetics dataset. We argue that the ImageNet classification task is not well-suited for pretraining, since content based classification is designed to make the model invariant to features that strongly influence the image's aesthetics, e.g. style-based features such as brightness or contrast. We propose to use self-supervised aesthetic-aware pretext tasks that let the network learn aesthetically relevant features, based on the observation that distorting aesthetic images with image filters usually reduces their appeal. To ensure that images are not accidentally improved when filters are applied, we introduce a large dataset comprised of highly aesthetic images as the starting point for the distortions. The network is then trained to rank less distorted images higher than their more distorted counterparts. To exploit effects of multiple different objectives, we also embed this task into a multi-task setting by adding either a self-supervised classification or regression task. In our experiments, we show that our pretraining improves performance over the ImageNet initialization and reduces the number of epochs until convergence by up to 47 %. Additionally, we can match the performance of an ImageNet-initialized model while reducing the labeled training data by 20 %. We make our code, data, and pretrained models available.

关键词： computer vision conferences Computational modeling Neural networks Training data Distortion Data models

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Noise Conditional Flow Model for Learning the Super-Resolution Space

Noise Conditional Flow Model for Learning the Super-Resoluti...

引用

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

作者： Kim, Younggeun Son, Donghee Seoul Natl Univ Seoul South Korea Lomin Inc Seoul South Korea Deepest Seoul South Korea

ISBN: (纸本)9781665448994

Fundamentally, super-resolution is ill-posed problem because a low-resolution image can be obtained from many high-resolution images. Recent studies for super-resolution cannot create diverse super-resolution images. Although SRFlow tried to account for ill-posed nature of the super-resolution by predicting multiple high-resolution images given a low-resolution image, there is room to improve the diversity and visual quality. In this paper, we propose Noise Conditional flow model for Super-Resolution, NCSR, which increases the visual quality and diversity of images through noise conditional layer. To learn more diverse data distribution, we add noise to training data. However, low-qualit . , images are resulted from adding noise. We propose the noise conditional layer to overcome this phenomenon. The noise conditional layer makes our model generate more diverse images with higher visual quality than other works. Furthermore, we show that this layer can overcome data distribution mismatch, a problem that arises in normalizing flow models. With these benefits, NCSR outperforms baseline in diversity and visual quality and achieves better visual quality than traditional GAN-based models. We also get outperformed scores at NTIRE 2021 challenge [21].

关键词： Training Degradation Visualization computer vision Computational modeling conferences Superresolution

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