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Spatial-Angular Multi-Scale Mechanism for Light Field Spatial Super-Resolution

Spatial-Angular Multi-Scale Mechanism for Light Field Spatia...

引用

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

作者： Chen Gao Youfang Lin Song Chang Shuo Zhang Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University

Light Field (LF) cameras are promising due to their ability to capture both spatial and angular information of scenes. However, the trade-off between spatial and angular resolution significantly limits the real-world applications. In this paper, we propose a spatial-angular multi-scale decoupling network to reconstruct high-resolution LF images. Considering the epipolar geometry, we propose a spatial-angular multi-scale processing approach to explore the correspondence of sub-pixel information with different disparity ranges between sub-aperture images in LFs. We extract sub-pixel information from various dimensions and fuse it to generate global high-frequency details. Finally, we combine upsampled low-frequency and high-frequency details to generate high resolution results. To further filter the correct interpolation information, we use the shear operation to change the disparity range of the LF images and fine-tune the results. Experimental results on synthetic and real-world datasets demonstrate that our method outperforms other state-of-the-art methods in visual and numerical evaluations, especially on datasets with small disparity ranges. Furthermore, our approach fully considers the epipolar geometry of the LF image, enabling us to recover information that better maintains the imaging consistency of the LF.

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An Effective Ensemble Learning Framework for Affective Behaviour Analysis

An Effective Ensemble Learning Framework for Affective Behav...

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

作者： Wei Zhang Feng Qiu Chen Liu Lincheng Li Heming Du Tianchen Guo Xin Yu Netease Fuxi AI Lab The University of Queensland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Affective Behavior Analysis aims to facilitate technology emotionally smart, creating a world where devices can understand and react to our emotions as humans do. To comprehensively evaluate the authenticity and applicability of emotional behavior analysis techniques in natural environments, the 6th competition on Affective Behavior Analysis in-the-wild (ABAW) utilizes the Aff-Wild2, Hume-Vidmimic2, and C-EXPR-DB datasets to set up five competitive tracks, i.e., Valence-Arousal (VA) Estimation, Expression (EXPR) recognition, Action Unit (AU) Detection, Compound Expression (CE) recognition, and Emotional Mimicry Intensity (EMI) Estimation. In this paper, we present our method designs for VA estimation, expression recognition, and AU detection tracks. Specifically, our framework mainly includes three aspects: 1) To achieve high-quality facial feature representations, we employ Masked-Auto Encoder as the visual features extraction model and fine-tune it with our facial dataset. 2) Utilizing a transformer-based feature fusion module to fully integrate emotional information provided by audio signals, visual images, and transcripts, offering high-quality expression features for the downstream tasks. 3) Considering the complexity of the video collection scenes, we conduct a more detailed dataset division based on scene characteristics and train the classifier for each scene. Extensive experiments demonstrate the superiority of our designs. Our work won the championship in the AU, EXPR, and VA tracks at the ABAW6 competition.

关键词： Human computer interaction Gold Visualization Emotion recognition Design methodology Estimation Feature extraction

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Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection

Back to the Feature: Classical 3D Features are (Almost) All ...

引用

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

作者： Eliahu Horwitz Yedid Hoshen School of Computer Science and Engineering The Hebrew University of Jerusalem Israel

Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity. First, we present a surprising finding: standard color-only methods outperform all current methods that are explicitly designed to exploit 3D information. This is counter-intuitive as even a simple inspection of the dataset shows that color-only methods are insufficient for images containing geometric anomalies. This motivates the question: how can anomaly detection methods effectively use 3D information? We investigate a range of shape representations including hand-crafted and deep-learning-based; we demonstrate that rotation invariance plays the leading role in the performance. We uncover a simple 3D-only method that beats all recent approaches while not using deep learning, external pre-training datasets, or color information. As the 3D-only method cannot detect color and texture anomalies, we combine it with color-based features, significantly outperforming previous state-of-the-art. Our method, dubbed BTF (Back to the Feature) achieves pixel-wise ROCAUC: 99.3% and PRO: 96.4% on MVTec 3D-AD.

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Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained vision Transformers

Calibrating Higher-Order Statistics for Few-Shot Class-Incre...

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

作者： Dipam Goswami Bartłomiej Twardowski Joost Van de Weijer Department of Computer Science Universitat Autònoma de Barcelona Computer Vision Center Barcelona IDEAS-NCBR

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Few-shot class-incremental learning (FSCIL) aims to adapt the model to new classes from very few data (5 samples) without forgetting the previously learned classes. Recent works in many-shot CIL (MSCIL) (using all available training data) exploited pre-trained models to reduce forgetting and achieve better plasticity. In a similar fashion, we use ViT models pre-trained on large-scale datasets for few-shot settings, which face the critical issue of low plasticity. FSCIL methods start with a many-shot first task to learn a very good feature extractor and then move to the few-shot setting from the second task onwards. While the focus of most recent studies is on how to learn the many-shot first task so that the model generalizes to all future few-shot tasks, we explore in this work how to better model the few-shot data using pre-trained models, irrespective of how the first task is trained. Inspired by recent works in MSCIL, we explore how using higher-order feature statistics can influence the classification of few-shot classes. We identify the main challenge of obtaining a good covariance matrix from few-shot data and propose to calibrate the covariance matrix for new classes based on semantic similarity to the many-shot base classes. Using the calibrated feature statistics in combination with existing methods significantly improves few-shot continual classification on several FSCIL benchmarks. Code is available at https://***/dipamgoswami/FSCIL-Calibration.

关键词： Adaptation models computer vision Training data Benchmark testing Feature extraction Transformers Data models

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DiffLight: Integrating Content and Detail for Low-light Image Enhancement

DiffLight: Integrating Content and Detail for Low-light Imag...

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

作者： Yixu Feng Shuo Hou Haotian Lin Yu Zhu Peng Wu Wei Dong Jinqiu Sun Qingsen Yan Yanning Zhang Northwestern Polytechnical University Xi’an University of Architecture and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Low Light Image Enhancement (LLIE) task has been a hotspot in low-level computer vision research. The camera sensor can only capture a small amount of ambient light signal in low-light condition, resulting in significant noise black pseudo artifacts in images, which not only degrade visual quality but also affect the performance of down-stream visual tasks. However, current methods often produce overly smoothed and distorted results, or introduce strong noise artifacts. Moreover, for recent UHD high-definition low-light images, due to GPU memory limitations, LLIE must be conducted in patches, leading to block artifacts. Faced with these challenges, we propose a dual-branch pipeline called DiffLight. Specifically, it consists of the Denoising Enhancement (DE) branch and the Detail Preservation (DP) branch. The DE-branch adopts a combination of DiffIR and LEDNet to reduce noise and enhance brightness, while the DP-branch utilizes a novel Light Full-Former (LFF) method, which comprises 20 Full-Attention (LFA) modules to preserve full-scale image details. To tackle block artifacts, we further introduce Progressive Patch Fusion (PPF) for patch fusion. Experimental results demonstrate that our approach is high-ranked in the CVPR2024 NTIRE Low Light Enhancement challenge and produced state-of-the (SOTA) results on other datasets.

关键词： Visualization computer vision Image color analysis conferences Pipelines Noise reduction Graphics processing units

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Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation: Do We Need Artificial Augmentation?

Virtually Enriched NYU Depth V2 Dataset for Monocular Depth ...

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

作者： Dmitry Ignatov Andrey Ignatov Radu Timofte Computer Vision Lab CAIDAS & IFI University of Wurzburg Germany Computer Vision Lab ETH Zurich Switzerland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present ANYU, a new virtually augmented version of the NYU depth v2 dataset, designed for monocular depth estimation. In contrast to the well-known approach where full 3D scenes of a virtual world are utilized to generate artificial datasets, ANYU was created by incorporating RGB-D representations of virtual reality objects into the original NYU depth v2 images. We specifically did not match each generated virtual object with an appropriate texture and a suitable location within the real-world image. Instead, an assignment of texture, location, lighting, and other rendering parameters was randomized to maximize a diversity of the training data, and to show that it is randomness that can improve the generalizing ability of a dataset. By conducting extensive experiments with our virtually modified dataset and validating on the original NYU depth v2 and iBims-1 benchmarks, we show that ANYU improves the monocular depth estimation performance and generalization of deep neural networks with considerably different architectures, especially for the current state-of-the-art VPD model. To the best of our knowledge, this is the first work that augments a real-world dataset with randomly generated virtual 3D objects for monocular depth estimation. We make our ANYU dataset publicly available in two training configurations with 10% and 100% additional synthetically enriched RGB-D pairs of training images, respectively, for efficient training and empirical exploration of virtual augmentation at https://***/ABrain-One/ANYU.

关键词： Training Solid modeling Three-dimensional displays Computational modeling Estimation Training data Lighting

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Uncovering the Hidden Cost of Model Compression

Uncovering the Hidden Cost of Model Compression

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

作者： Diganta Misra Muawiz Chaudhary Agam Goyal Bharat Runwal Pin Yu Chen Carnegie Mellon University Landskape AI Mila - Quebec AI Institute Concordia University University of Wisconsin-Madison Mila - Quebec AI Institute IBM Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In an age dominated by resource-intensive foundation models, the ability to efficiently adapt to downstream tasks is crucial. Visual Prompting (VP), drawing inspiration from the prompting techniques employed in Large Language Models (LLMs), has emerged as a pivotal method for transfer learning in the realm of computer vision. As the importance of efficiency continues to rise, research into model compression has become indispensable in alleviating the computational burdens associated with training and deploying over-parameterized neural networks. A primary objective in model compression is to develop sparse and/or quantized models capable of matching or even surpassing the performance of their over-parameterized, full-precision counterparts. Although previous studies have explored the effects of model compression on transfer learning, its impact on visual prompting-based transfer remains unclear. This study aims to bridge this gap, shedding light on the fact that model compression detrimentally impacts the performance of visual prompting-based transfer, particularly evident in scenarios with low data volume. Furthermore, our findings underscore the adverse influence of sparsity on the calibration of downstream visual-prompted models. However, intriguingly, we also illustrate that such negative effects on calibration are not present when models are compressed via quantization. This empirical investigation underscores the need for a nuanced understanding beyond mere accuracy in sparse and quantized settings, thereby paving the way for further exploration in Visual Prompting techniques tailored for sparse and quantized models.

关键词： Visualization Adaptation models Solid modeling computer vision Costs Quantization (signal) Computational modeling

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HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing

HyperStyle: StyleGAN Inversion with HyperNetworks for Real I...

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2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Alaluf, Yuval Tov, Omer Mokady, Ron Gal, Rinon Bermano, Amit Blavatnik School of Computer Science Tel Aviv University Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The inversion of real images into StyleGAN's latent space is a well-studied problem. Nevertheless, applying existing approaches to real-world scenarios remains an open challenge, due to an inherent trade-off between reconstruction and editability: latent space regions which can accurately represent real images typically suffer from degraded semantic control. Recent work proposes to mitigate this trade-off by fine-tuning the generator to add the target image to well-behaved, editable regions of the latent space. While promising, this fine-tuning scheme is impractical for prevalent use as it requires a lengthy training phase for each new image. In this work, we introduce this approach into the realm of encoder-based inversion. We propose HyperStyle, a hypernetwork that learns to modulate StyleGAN's weights to faithfully express a given image in editable regions of the latent space. A naive modulation approach would require training a hypernetwork with over three billion parameters. Through careful network design, we reduce this to be in line with existing encoders. HyperStyle yields reconstructions comparable to those of optimization techniques with the near real-time inference capabilities of encoders. Lastly, we demonstrate HyperStyle's effectiveness on several applications beyond the inversion task, including the editing of out-of-domain images which were never seen during training. Code is available on our project page: https://***/hyperstyle/. © 2022 ieee.

关键词： Training Semantics Aerospace electronics Generators Robustness Real-time systems pattern recognition

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BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection

BeCAPTCHA-Type: Biometric Keystroke Data Generation for Impr...

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

作者： Daniel DeAlcala Aythami Morales Ruben Tolosana Alejandro Acien Julian Fierrez Santiago Hernandez Miguel A. Ferrer Moises Diaz Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain University Las Palmas Gran Canaria Spain

This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. These approaches are validated on a bot detection task, using the keystroke synthetic data to improve the training process of keystroke-based bot detection systems. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects. We have analyzed the performance of the three synthesis approaches through qualitative and quantitative experiments. Different bot detectors are considered based on several supervised classifiers (Support Vector Machine, Random Forest, Gaussian Naive Bayes and a Long Short-Term Memory network) and a learning framework including human and synthetic samples. The experiments demonstrate the realism of the synthetic samples. The classification results suggest that in scenarios with large labeled data, these synthetic samples can be detected with high accuracy. However, if the proposed synthetic data is nor properly modelled using massive data by bot detectors, then that data will be very difficult to detect even for the most sophisticate bot detectors. Furthermore, these results show the great potential of the presented models for improving the training of bot detection technology.

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Unsupervised Automatic Defect Inspection based on Image Matching and Local One-class Classification

Unsupervised Automatic Defect Inspection based on Image Matc...

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

作者： Chengkan Lv Zhengtao Zhang Fei Shen Feng Zhang Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CASI Vision Technology Co. Ltd. Luoyang China

In this paper, an unsupervised defect inspection method based on anomaly detection is proposed to inspect various kinds of surface defects in the field of industrial production. This method consists of two modules: (i) An image matching module is utilized to align the input image with a pre-specified template image. Specifically, all objects to be detected will be adjusted to the same position and angle. The aligned images can reduce the difficulty of the training stage, facilitating the subsequent feature extraction and anomaly localization. (ii) After the image matching procedure, an anomaly localization module is trained to learn a mapping that concentrates normal samples in feature space. In particular, each local image region is assigned a feature center by adopting a feature map as the mapping target. Therefore, the compactness of the features extracted from the same region can be improved, which is beneficial to detect potential anomalous targets. Moreover, various artificial defective images are synthesized during the training stage to further improve the discriminatory ability of the anomaly localization module. A series of experiments are conducted on MAD dataset and the industrial production line. The experimental results verify the efficiency and versatility of the proposed method.

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