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共 21180 条记录，以下是591-600 订阅

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MetaCLUE: Towards Comprehensive Visual Metaphors Research

MetaCLUE: Towards Comprehensive Visual Metaphors Research

引用

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

作者： Akula, Arjun R. Driscoll, Brendan Narayana, Pradyumna Changpinyo, Soravit Jia, Zhiwei Damle, Suyash Pruthi, Garima Basu, Sugato Guibas, Leonidas Freeman, William T. Li, Yuanzhen Jampani, Varun Google Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Creativity is an indispensable part of human cognition and also an inherent part of how we make sense of the world. Metaphorical abstraction is fundamental in communicating creative ideas through nuanced relationships between abstract concepts such as feelings. While computer vision benchmarks and approaches predominantly focus on understanding and generating literal interpretations of images, metaphorical comprehension of images remains relatively unexplored. Towards this goal, we introduce MetaCLUE, a set of vision tasks on visual metaphor. We also collect high-quality and rich metaphor annotations (abstract objects, concepts, relationships along with their corresponding object boxes) as there do not exist any datasets that facilitate the evaluation of these tasks. We perform a comprehensive analysis of state-of-the-art models in vision and language based on our annotations, highlighting strengths and weaknesses of current approaches in visual metaphor classification, localization, understanding (retrieval, question answering, captioning) and generation (text-to-image synthesis) tasks. We hope this work provides a concrete step towards developing AI systems with human-like creative capabilities. Project page: https://***

关键词： and reasoning language vision

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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/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kolbinger, Fiona R. He, Jiangpeng Ma, Jinge Zhu, Fengqing Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350365474

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： Class Imbalance computer-Assisted Surgery Laparoscopic Surgery Semantic Segmentation Surgical Data Science

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A-CAP: Anticipation Captioning with Commonsense Knowledge

A-CAP: Anticipation Captioning with Commonsense Knowledge

引用

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

作者： Duc Minh Vo Quoc-An Luong Sugimoto, Akihiro Nakayama, Hideki Univ Tokyo Tokyo Japan Grad Univ Adv Studies Hayama Kanagawa Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798350301298

Humans possess the capacity to reason about the future based on a sparse collection of visual cues acquired over time. In order to emulate this ability, we introduce a novel task called Anticipation Captioning, which generates a caption for an unseen oracle image using a sparsely temporally-ordered set of images. To tackle this new task, we propose a model called A-CAP, which incorporates commonsense knowledge into a pre-trained vision-language model, allowing it to anticipate the caption. Through both qualitative and quantitative evaluations on a customized visual storytelling dataset, A-CAP outperforms other image captioning methods and establishes a strong baseline for anticipation captioning. We also address the challenges inherent in this task.

关键词： and reasoning language vision

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vision Transformers Are Good Mask Auto-Labelers

Vision Transformers Are Good Mask Auto-Labelers

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

作者： Lan, Shiyi Yang, Xitong Yu, Zhiding Wu, Zuxuan Alvarez, Jose M. Anandkumar, Anima NVIDIA Santa Clara CA 95051 USA Meta AI FAIR London England Fudan Univ Shanghai Peoples R China CALTECH Pasadena CA USA

ISBN: (纸本)9798350301298

We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels. We show that vision Transformers are good mask auto-labelers. Our method significantly reduces the gap between auto-labeling and human annotation regarding mask quality. Instance segmentation models trained using the MAL-generated masks can nearly match the performance of their fully-supervised counterparts, retaining up to 97.4% performance of fully supervised models. The best model achieves 44.1% mAP on COCO instance segmentation (test-dev 2017), outperforming state-of-the-art box-supervised methods by significant margins. Qualitative results indicate that masks produced by MAL are, in some cases, even better than human annotations.

关键词： grouping and shape analysis Segmentation

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SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection

SQUID: Deep Feature In-Painting for Unsupervised Anomaly Det...

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

作者： Xiang, Tiange Zhang, Yixiao Lu, Yongyi Yuille, Alan L. Zhang, Chaoyi Cai, Weidong Zhou, Zongwei Univ Sydney Camperdown NSW Australia Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9798350301298

Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To exploit this structured information, we propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID). We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns;and in the inference, it can identify anomalies (unseen/modified patterns) in the image. SQUID surpasses 13 state-of-the-art methods in unsupervised anomaly detection by at least 5 points on two chest X-ray benchmark datasets measured by the Area Under the Curve (AUC). Additionally, we have created a new dataset (DigitAnatomy), which synthesizes the spatial correlation and consistent shape in chest anatomy. We hope DigitAnatomy can prompt the development, evaluation, and interpretability of anomaly detection methods.

关键词： cell microscopy Medical and biological vision

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Spectral Bayesian Uncertainty for Image Super-resolution

Spectral Bayesian Uncertainty for Image Super-resolution

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

作者： Liu, Tao Cheng, Jun Tan, Shan Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350301298

Recently deep learning techniques have significantly advanced image super-resolution (SR). Due to the black-box nature, quantifying reconstruction uncertainty is crucial when employing these deep SR networks. Previous approaches for SR uncertainty estimation mostly focus on capturing pixel-wise uncertainty in the spatial domain. SR uncertainty in the frequency domain which is highly related to image SR is seldom explored. In this paper, we propose to quantify spectral Bayesian uncertainty in image SR. To achieve this, a Dual-Domain Learning (DDL) framework is first proposed. Combined with Bayesian approaches, the DDL model is able to estimate spectral uncertainty accurately, enabling a reliability assessment for high frequencies reasoning from the frequency domain perspective. Extensive experiments under non-ideal premises are conducted and demonstrate the effectiveness of the proposed spectral uncertainty. Furthermore, we propose a novel Spectral Uncertainty based Decoupled Frequency (SUDF) training scheme for perceptual SR. Experimental results show the proposed SUDF can evidently boost perceptual quality of SR results without sacrificing much pixel accuracy.

关键词： Low-level vision

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KiUT: Knowledge-injected U-Transformer for Radiology Report Generation

KiUT: Knowledge-injected U-Transformer for Radiology Report ...

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

作者： Huang, Zhongzhen Zhang, Xiaofan Zhang, Shaoting Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden of report writing. Although various image caption methods have shown remarkable performance in the natural image field, generating accurate reports for medical images requires knowledge of multiple modalities, including vision, language, and medical terminology. We propose a Knowledge-injected U-Transformer (KiUT) to learn multi-level visual representation and adaptively dis-till the information with contextual and clinical knowledge for word prediction. In detail, a U-connection schema between the encoder and decoder is designed to model interactions between different modalities. And a symptom graph and an injected knowledge distiller are developed to assist the report generation. Experimentally, we outperform state-of-the-art methods on two widely used benchmark datasets: IU-Xray and MIMIC-CXR. Further experimental results prove the advantages of our architecture and the complementary benefits of the injected knowledge.

关键词： cell microscopy Medical and biological vision

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MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression recognition in-the-wild

MMA-DFER: MultiModal Adaptation of unimodal models for Dynam...

引用

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

作者： Chumachenko, Kateryna Iosifidis, Alexandros Gabbouj, Moncef Tampere Univ Tampere Finland Aarhus Univ Aarhus Denmark

ISBN: (纸本)9798350365474

Dynamic Facial Expression recognition (DFER) has received significant interest in the recent years dictated by its pivotal role in enabling empathic and human-compatible technologies. Achieving robustness towards in-the-wild data in DFER is particularly important for real-world applications. One of the directions aimed at improving such models is multimodal emotion recognition based on audio and video data. Multimodal learning in DFER increases the model capabilities by leveraging richer, complementary data representations. Within the field of multimodal DFER, recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders [40]. Another line of research has focused on adapting pre-trained static models for DFER [8]. In this work, we propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders. We identify main challenges associated with this task, namely, intra-modality adaptation, cross-modal alignment, and temporal adaptation, and propose solutions to each of them. As a result, we demonstrate improvement over current state-of-the-art on two popular DFER benchmarks, namely DFEW [19] and MFAW [29].

关键词： audiovisual emotion recognition dynamic facial expression recognition facial expression recognition multi-modal multimodal adaptation

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Can the accuracy bias by facial hairstyle be reduced through balancing the training data?

Can the accuracy bias by facial hairstyle be reduced through...

引用

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

作者： Ozturk, Kagan Wu, Haiyu Bowyer, Kevin W. Univ Notre Dame Notre Dame IN 46556 USA

ISBN: (纸本)9798350365474

Appearance of a face can be greatly altered by growing a beard and mustache. The facial hairstyles in a pair of images can cause marked changes to the impostor distribution and the genuine distribution. Also, different distributions of facial hairstyle across demographics could cause a false impression of relative accuracy across demographics. We first show that, even though larger training sets boost the recognition accuracy on all facial hairstyles, accuracy variations caused by facial hairstyles persist regardless of the size of the training set. Then, we analyze the impact of having different fractions of the training data represent facial hairstyles. We created balanced training sets using a set of identities available in Webface42M that both have clean-shaven and facial hair images. We find that, even when a face recognition model is trained with a balanced clean-shaven / facial hair training set, accuracy variation on the test data does not diminish. Next, data augmentation is employed to further investigate the effect of facial hair distribution in training data by manipulating facial hair pixels with the help of facial landmark points and a facial hair segmentation model. Our results show facial hair causes an accuracy gap between clean-shaven and facial hair images, and this impact can be significantly different between African-Americans and Caucasians.

关键词： Face recognition

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Video Interaction recognition using an Attention Augmented Relational Network and Skeleton Data

Video Interaction Recognition using an Attention Augmented R...

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

作者： Askari, Farzaneh Yared, Cyril Ramaprasad, Rohit Garg, Devin Hu, Anjun Clark, James J. McGill Univ Montreal PQ Canada Univ Calif San Diego San Diego CA USA Univ Oxford Oxford England

ISBN: (纸本)9798350365474

Recognizing interactions in multi-person videos, known as Video Interaction recognition (VIR), is crucial for understanding video content. Often the human skeleton pose (skeleton, for short) is a popular feature for VIR as the main feature, given its success for the task in hand. While many studies have made progress using complex architectures like Graph Neural Networks (GNN) and Transformers to capture interactions in videos, studies such as [33] that apply simple, easy to train, and adaptive architectures such as Relation reasoning Network (RN) [37], yield competitive results. Inspired by this trend, we propose the Attention Augmented Relational Network (AARN), a straightforward yet effective model that uses skeleton data to recognize interactions in videos. AARN outperforms other RN-based models and remains competitive against larger, more intricate models. We evaluate our approach on a challenging real-world Hockey Penalty Dataset (HPD), where the videos depict complex interactions between players in a non-laboratory recording setup, in addition to popular benchmark datasets demonstrating strong performance. Lastly, we show the impact of skeleton quality on the classification accuracy and the struggle of off-the-shelf pose estimators to extract precise skeleton from the challenging HPD dataset.

关键词： Musculoskeletal system

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