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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是251-260 订阅

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Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results

Rip Current Segmentation: A Novel Benchmark and YOLOv8 Basel...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Dumitriu, Andrei Tatui, Florin Miron, Florin Ionescu, Radu Tudor Timofte, Radu CAIDAS IFI University of Würzburg Computer Vision Lab Germany University of Bucharest Romania

ISBN: (纸本)9798350302493

Rip currents are the leading cause of fatal accidents and injuries on many beaches worldwide, emphasizing the importance of automatically detecting these hazardous surface water currents. In this paper, we address a novel task: rip current instance segmentation. We introduce a comprehensive dataset containing 2,466 images with newly created polygonal annotations for instance segmentation, used for training and validation. Additionally, we present a novel dataset comprising 17 drone videos (comprising about 24K frames) captured at 30 FPS, annotated with both polygons for instance segmentation and bounding boxes for object detection, employed for testing purposes. We train various versions of YOLOv8 for instance segmentation on static images and assess their performance on the test dataset (videos). The best results were achieved by the YOLOv8-nano model (runnable on a portable device), with an mAP50 of 88.94% on the validation dataset and 81.21% macro average on the test dataset. The results provide a baseline for future research in rip current segmentation. Our work contributes to the existing literature by introducing a detailed, annotated dataset, and training a deep learning model for instance segmentation of rip currents. The code, training details and the annotated dataset are made publicly available at https://***/Irikos/rip-currents. © 2023 ieee.

关键词： Surface waters

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Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective

Investigating Catastrophic Overfitting in Fast Adversarial T...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： He, Zhengbao Li, Tao Chen, Sizhe Huang, Xiaolin Shanghai Jiao Tong University Department of Automation China

ISBN: (纸本)9798350302493

Although fast adversarial training provides an efficient approach for building robust networks, it may suffer from a serious problem known as catastrophic overfitting (CO), where multi-step robust accuracy suddenly collapses to zero. In this paper, we for the first time decouple single-step adversarial examples into data-information and self-information, which reveals an interesting phenomenon called "self-fitting". Self-fitting, i.e., the network learns the self-information embedded in single-step perturbations, naturally leads to the occurrence of CO. When self-fitting occurs, the network experiences an obvious "channel differentiation"phenomenon that some convolution channels accounting for recognizing self-information become dominant, while others for data-information are suppressed. In this way, the network can only recognize images with sufficient self-information and loses generalization ability to other types of data. Based on self-fitting, we provide new insights into the existing methods to mitigate CO and extend CO to multi-step adversarial training. Our findings reveal a self-learning mechanism in adversarial training and open up new perspectives for suppressing different kinds of information to mitigate CO. © 2023 ieee.

关键词： computer vision

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Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting

Attention-Propagation Network for Egocentric Heatmap to 3D P...

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

作者： Kang, Taeho Lee, Youngki Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

We present EgoTAP, a heatmap-to-3D pose lifting method for highly accurate stereo egocentric 3D pose estimation. Severe self-occlusion and out-of-view limbs in egocentric camera views make accurate pose estimation a challenging problem. To address the challenge, prior methods employ joint heatmaps-probabilistic 2D representations of the body pose, but heatmap-to-3D pose conversion still remains an inaccurate process. We propose a novel heatmap-to-3D lifting method composed of the Grid ViT Encoder and the Propagation Network. The Grid ViT Encoder summarizes joint heatmaps into effective feature embedding using self-attention. Then, the Propagation Network estimates the 3D pose by utilizing skeletal information to better estimate the position of obscure joints. Our method significantly outperforms the previous state-of-the-art qualitatively and quantitatively demonstrated by a 23.9% reduction of error in an MPJPE metric. Our source code is available on GitHub (1).

关键词： 3D pose estimation egocentric vision stereo vision

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Rank in Style: A Ranking-based Approach to Find Interpretable Directions

Rank in Style: A Ranking-based Approach to Find Interpretabl...

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

作者： Kocasari, Umut Zaman, Kerem Tiftikci, Mert Simsar, Enis Yanardag, Pinar Bogazici Univ Istanbul Turkey TUM Munich Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent work such as StyleCLIP aims to harness the power of CLIP embeddings for controlled manipulations. Although these models are capable of manipulating images based on a text prompt, the success of the manipulation often depends on careful selection of the appropriate text for the desired manipulation. This limitation makes it particularly difficult to perform text-based manipulations in domains where the user lacks expertise, such as fashion. To address this problem, we propose a method for automatically determining the most successful and relevant text-based edits using a pre-trained StyleGAN model. Our approach consists of a novel mechanism that uses CLIP to guide beam-search decoding, and a ranking method that identifies the most relevant and successful edits based on a list of keywords. We also demonstrate the capabilities of our framework in several domains, including fashion.

关键词： computer vision Image synthesis conferences Search problems pattern recognition Decoding

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WSRD: A Novel Benchmark for High Resolution Image Shadow Removal

WSRD: A Novel Benchmark for High Resolution Image Shadow Rem...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu University of Würzburg Computer Vision Laboratory Caidas & Ifi Germany

ISBN: (纸本)9798350302493

Shadow removal is an important computer vision task, whose aim is to successfully detect the shadow affected area appearing through light occlussion, followed by a photorealistic restoration of the affected image contents, textures, and details. After decades of research, a multitude of hand-crafted restoration techniques were proposed, following different observations on shadow formation models, with scenes altered in particular conditions. However, the increased popularity of deep learning based solutions enabled a significant step forward for the shadow removal solutions, both in terms of reconstruction fidelity and perceptual properties. However, the publicly available datasets remain focused around a particularly low complexity setup, with a low variety of light occluders and affected backgrounds, and with limited representation for more complex light interactions and complex shadow patterns. In this work, we propose WSRD, a novel benchmark for high resolution image shadow removal, characterized by a large variety in terms or represented objects, backgrounds and light occluders. We study more complex interactions, combining self shadows with externally casted shadows, to further extend the study of the phenomenon, its factors and effects. To prove WSRD as a relevant benchmark, we propose DNSR, a novel shadow removal method, comparing the results on WSRD with the performance level observed on other well-established benchmarks like ISTD and ISTD+. We validate our approach comparing with existing state-of-the-art (SOTA) methods, improving both in reconstruction fidelity and perceptual properties, setting a new SOTA for the field. © 2023 ieee.

关键词： Restoration

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Differentiable Display Photometric Stereo

Differentiable Display Photometric Stereo

引用

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

作者： Choi, Seokjun Yoon, Seungwoo Nam, Giljoo Lee, Seungyong Baek, Seung-Hwan POSTECH Pohang South Korea Meta Real Labs Menlo Pk CA USA

ISBN: (纸本)9798350353006

Photometric stereo leverages variations in illumination conditions to reconstruct surface normals. Display photometric stereo, which employs a conventional monitor as an illumination source, has the potential to overcome limitations often encountered in bulky and difficult-to-use conventional setups. In this paper, we present differentiable display photometric stereo (DDPS), addressing an often over-looked challenge in display photometric stereo: the design of display patterns. Departing from using heuristic display patterns, DDPS learns the display patterns that yield accurate normal reconstruction for a target system in an end-to-end manner. To this end, we propose a differentiable framework that couples basis-illumination image formation with analytic photometric-stereo reconstruction. The differentiable framework facilitates the effective learning of display patterns via auto-differentiation. Also, for training supervision, we propose to use 3D printing for creating a real-world training dataset, enabling accurate reconstruction on the target real-world setup. Finally, we exploit that conventional LCD monitors emit polarized light, which allows for the optical separation of diffuse and specular reflections when combined with a polarization camera, leading to accurate normal reconstruction. Extensive evaluation of DDPS shows improved normal-reconstruction accuracy compared to heuristic patterns and demonstrates compelling properties such as robustness to pattern initialization, calibration errors, and simplifications in image formation and reconstruction.

关键词： Computational illumination computer graphics computer vision Photometric stereo

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OutfitTransformer: Outfit Representations for Fashion Recommendation

OutfitTransformer: Outfit Representations for Fashion Recomm...

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

作者： Sarkar, Rohan Bodla, Navaneeth Vasileva, Mariya Lin, Yen-Liang Beniwal, Anurag Lu, Alan Medioni, Gerard Purdue Univ W Lafayette IN 47907 USA Amazon Seattle WA 98109 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Predicting outfit compatibility and retrieving complementary items are critical components for a fashion recommendation system. We present a scalable framework, Out-fitTransformer, that learns compatibility of the entire out-fit and supports large-scale complementary item retrieval. We model outfits as an unordered set of items and leverage self-attention mechanism to learn the relationships between items. We train the framework using a proposed set-wise outfit ranking loss to generate a target item embedding given an outfit, and a target item specification. The generated target item embedding is then used to retrieve compatible items that match the outfit. Experimental results demonstrate that our approach outperforms state-of-the-art methods on compatibility prediction, fill-in-the-blank, and complementary item retrieval tasks.

关键词： computer vision conferences Task analysis Recommender systems pattern matching

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RDONet: Rate-Distortion Optimized Learned Image Compression with Variable Depth

RDONet: Rate-Distortion Optimized Learned Image Compression ...

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

作者： Brand, Fabian Fischer, Kristian Kopte, Alexander Windsheimer, Marc Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9781665487399

Rate-distortion optimization (RDO) is responsible for large gains in image and video compression. While RDO is a standard tool in traditional image and video coding, it is not yet widely used in novel end-to-end trained neural methods. The major reason is that the decoding function is trained once and does not have free parameters. In this paper, we present RDONet, a network containing state-of-the-art components, which is perceptually optimized and capable of rate-distortion optimization. With this network, we are able to outperform VVC Intra on MS-SSIM and two different perceptual LPIPS metrics. This paper is part of the CLIC challenge, where we participate under the team name RDONet FAU.

关键词： Measurement Video coding Training Image coding Rate-distortion Video compression pattern recognition

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Exploring and Utilizing pattern Imbalance

Exploring and Utilizing Pattern Imbalance

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

作者： Mei, Shibin Zhao, Chenglong Yuan, Shengchao Ni, Bingbing Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

ISBN: (纸本)9798350301298

In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly concerned with category or domain granularity, ignoring the potential finer structure that existed in datasets, we give a new definition of seed category as an appropriate optimization unit to distinguish different patterns in the same category or domain. Extensive experiments on domain generalization datasets of diverse scales demonstrate the effectiveness of the proposed method.

关键词： Datasets and evaluation

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AAFormer: A Multi-Modal Transformer Network for Aerial Agricultural Images

AAFormer: A Multi-Modal Transformer Network for Aerial Agric...

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

作者： Shen, Yao Wang, Lei Jin, Yue China Pacific Insurance Grp Co Ltd Shanghai Peoples R China East China Normal Univ Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The semantic segmentation of agricultural aerial images is very important for the recognition and analysis of farmland anomaly patterns, such as drydown, endrow, nutrient deficiency, etc. Methods for general semantic segmentation such as Fully Convolutional Networks can extract rich semantic features, but are difficult to exploit the long-range information. Recently, vision Transformer architectures have made outstanding performances in image segmentation tasks, but transformer-based models have not been fully explored in the field of ***, we propose a novel architecture called Agricultural Aerial Transformer (AAFormer) to solve the semantic segmentation of aerial farmland images. We adopt Mix Transformer (MiT) in the encoder stage to enhance the ability of field anomaly pattern recognition and leverage the Squeeze-and-Excitation (SE) module in the decoder stage to improve the effectiveness of key channels. The boundary maps of farmland are introduced into the decoder. Evaluated on the Agriculture-vision validation set, the mIoU of our proposed model reaches 45.44%.

关键词： Image segmentation Image recognition conferences Semantics computer architecture Transformers Feature extraction

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