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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171-180 订阅

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Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute recognition

Learning CLIP Guided Visual-Text Fusion Transformer for Vide...

引用

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

作者： Zhu, Jun Jin, Jiandong Yang, Zihan Wu, Xiaohao Wang, Xiao Anhui University School of Computer Science and Technology Hefei230601 China Anhui University School of Artificial Intelligence Hefei230601 China

ISBN: (纸本)9798350302493

Existing pedestrian attribute recognition (PAR) algorithms are mainly developed based on a static image. However, the performance is not reliable for images with challenging factors, such as heavy occlusion, motion blur, etc. In this work, we propose to understand human attributes using video frames that can make full use of temporal information. Specifically, we formulate the video-based PAR as a vision-language fusion problem and adopt pre-trained big models CLIP to extract the feature embeddings of given video frames. To better utilize the semantic information, we take the attribute list as another input and transform the attribute words/phrase into the corresponding sentence via split, expand, and prompt. Then, the text encoder of CLIP is utilized for language embedding. The averaged visual tokens and text tokens are concatenated and fed into a fusion Transformer for multi-modal interactive learning. The enhanced tokens will be fed into a classification head for pedestrian attribute prediction. Extensive experiments on a large-scale video-based PAR dataset fully validated the effectiveness of our proposed framework. Both the source code and pre-trained models will be released at https://***/Event-AHU/VTF-PAR. © 2023 ieee.

关键词： Embeddings

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Beyond AUROC & co. for evaluating out-of-distribution detection performance

Beyond AUROC & co. for evaluating out-of-distribution detect...

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

作者： Humblot-Renaux, Galadrielle Escalera, Sergio Moeslund, Thomas B. Aalborg University Visual Analysis and Perception Lab Denmark Universitat Autònoma de Barcelona Computer Vision Center Spain Universitat de Barcelona Dept. of Mathematics and Informatics Spain

ISBN: (纸本)9798350302493

While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://***/glhr/beyond-auroc © 2023 ieee.

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A case for using rotation invariant features in state of the art feature matchers

A case for using rotation invariant features in state of the...

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

作者： Bokman, Georg Kahl, Fredrik Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781665487399

The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.

关键词： computer vision conferences Lighting pattern matching

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Blind Image Inpainting via Omni-dimensional Gated Attention and Wavelet Queries

Blind Image Inpainting via Omni-dimensional Gated Attention ...

引用

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

作者： Phutke, Shruti S. Kulkarni, Ashutosh Vipparthi, Santosh Kumar Murala, Subrahmanyam Indian Institute of Technology Ropar Computer Vision and Pattern Recognition Lab Punjab Rupnagar India

ISBN: (纸本)9798350302493

Blind image inpainting is a crucial restoration task that does not demand additional mask information to restore the corrupted regions. Yet, it is a very less explored research area due to the difficulty in discriminating between corrupted and valid regions. There exist very few approaches for blind image inpainting which sometimes fail at producing plausible inpainted images. Since they follow a common practice of predicting the corrupted regions and then inpaint them. To skip the corrupted region prediction step and obtain better results, in this work, we propose a novel end-to-end architecture for blind image inpainting consisting of wavelet query multi-head attention transformer block and the omni-dimensional gated attention. The proposed wavelet query multi-head attention in the transformer block provides encoder features via processed wavelet coefficients as query to the multi-head attention. Further, the proposed omni-dimensional gated attention effectively provides all dimensional attentive features from the encoder to the respective decoder. Our proposed approach is compared numerically and visually with existing state-of-the-art methods for blind image inpainting on different standard datasets. The comparative and ablation studies prove the effectiveness of the proposed approach for blind image inpainting. The testing code is available at : https://***/shrutiphutke/Blind-Omni-Wav-Net © 2023 ieee.

关键词： Restoration

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Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance

Impact of Pseudo Depth on Open World Object Segmentation wit...

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

作者： Schön, Robin Ludwig, Katja Lienhart, Rainer University of Augsburg Machine Learning and Computer Vision Germany

ISBN: (纸本)9798350302493

Pseudo depth maps are depth map predicitions which are used as ground truth during training. In this paper we leverage pseudo depth maps in order to segment objects of classes that have never been seen during training. This renders our object segmentation task an open world task. The pseudo depth maps are generated using pretrained networks, which have either been trained with the full intention to generalize to downstream tasks (LeRes and MiDaS), or which have been trained in an unsupervised fashion on video sequences (MonodepthV2). In order to tell our network which object to segment, we provide the network with a single click on the object's surface on the pseudo depth map of the image as input. We test our approach on two different scenarios: One without the RGB image and one where the RGB image is part of the input. Our results demonstrate a considerably better generalization performance from seen to unseen object types when depth is used. On the Semantic Boundaries Dataset we achieve an improvement from 61.57 to 69.79 IoU score on unseen classes, when only using half of the training classes during training and performing the segmentation on depth maps only. © 2023 ieee.

关键词： Semantics

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Making the V in Text-VQA Matter

Making the V in Text-VQA Matter

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

作者： Hegde, Shamanthak Jahagirdar, Soumya Gangisetty, Shankar Kle Technological University Hubballi India Cvit Iiit Hyderabad Hyderabad India Iiit Hyderabad Hyderabad India

ISBN: (纸本)9798350302493

Text-based VQA aims at answering questions by reading the text present in the images. It requires a large amount of scene-text relationship understanding compared to the VQA task. Recent studies have shown that the question-answer pairs in the dataset are more focused on the text present in the image but less importance is given to visual features and some questions do not require understanding the image. The models trained on this dataset predict biased answers due to the lack of understanding of visual context. For example, in questions like "What is written on the signboard?", the answer predicted by the model is always "STOP"which makes the model to ignore the image. To address these issues, we propose a method to learn visual features (making V matter in TextVQA) along with the OCR features and question features using VQA dataset as external knowledge for Text-based VQA. Specifically, we combine the TextVQA dataset and VQA dataset and train the model on this combined dataset. Such a simple, yet effective approach increases the understanding and correlation between the image features and text present in the image, which helps in the better answering of questions. We further test the model on different datasets and compare their qualitative and quantitative results. © 2023 ieee.

关键词： computer vision

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Inferring Affective Experience from the Big Picture Metaphor: A Two-dimensional Visual Breadth Model

Inferring Affective Experience from the Big Picture Metaphor...

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

作者： Tong, Song Duan, Jingyi Liang, Xuefeng Kumada, Takatsune Peng, Kaiping Nakashima, Ryoichi Tsinghua University Department of Psychology Beijing China Xidian University School of Artificial Intelligence Shaanxi China Kyoto University Graduate School of Informatics Kyoto Japan

ISBN: (纸本)9798350302493

This study explores the psychological significance of the commonly used visual metaphor 'seeing the big picture' and examines whether and how it leads to positive experiences in real-life situations. To elucidate this phenomenon, a two-dimensional model of visual breadth is proposed, then respectively operationalized by two computer vision approaches. Our approaches are evaluated on a collected data set with 29,216 photos. The results revealed that physical and contextual breadth are two essential visual structures that compose a 'big picture'. Furthermore, these two visual breadths interactively shape people's affective experiences. This study provides insight into the psychological implications of the 'big picture' metaphor and sheds light on its practical potential for computer vision approaches and affective computing in the wild. © 2023 ieee.

关键词： computer vision

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Bridging the Gap Between Automated and Human Facial Emotion Perception

Bridging the Gap Between Automated and Human Facial Emotion ...

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

作者： Stratton, Derek Hand, Emily Univ Nevada Reno Reno NV 89557 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Understanding the complex relationship between emotions and facial expressions is important for both psychologists and computer scientists. A large body of research in psychology investigates facial expressions, emotions, and how emotions are perceived from facial expressions. As computer scientists look to incorporate this research into automatic emotion perception systems, it is important to understand the nature and limitations of human emotion perception. These principles of emotion science affect the way datasets are created, methods are implemented, and results are interpreted in automated emotion perception. This paper aims to distill and align prior work in automated and human facial emotion perception to facilitate future discussions and research at the intersection of the two disciplines.

关键词： Emotion recognition computer vision Uncertainty conferences Sociology Psychology Natural language processing

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Denoising diffusion models for out-of-distribution detection

Denoising diffusion models for out-of-distribution detection

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

作者： Graham, Mark S. Pinaya, Walter H. L. Tudosiu, Petru-Daniel Nachev, Parashkev Ourselin, Sebastien Cardoso, M. Jorge King's College London United Kingdom University College London United Kingdom

ISBN: (纸本)9798350302493

Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or other measurements from a generative model. Reconstruction-based methods offer an alternative approach, in which a measure of reconstruction error is used to determine if a sample is out-of-distribution. However, reconstruction-based approaches are less favoured, as they require careful tuning of the model's information bottleneck-such as the size of the latent dimension - to produce good results. In this work, we exploit the view of denoising diffusion probabilistic models (DDPM) as denoising autoencoders where the bottleneck is controlled externally, by means of the amount of noise applied. We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs. We validate our approach both on standard computer-vision datasets and on higher dimension medical datasets. Our approach outperforms not only reconstruction-based methods, but also state-of-the-art generative-based approaches. Code is available at https://***/marksgraham/ddpm-ood. © 2023 ieee.

关键词： computer vision

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Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection

Gaussian Image Anomaly Detection with Greedy Eigencomponent ...

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ieee/cvf International conference on computer vision (ICCV)

作者： Gula, Tetiana Bertoldo, Joao P. C. PSL Univ Ctr Math Morphol Mines Paris F-77300 Fontainebleau France

ISBN: (纸本)9798350307443

This paper addresses the challenge of Anomaly detection (AD) in images by proposing a novel dimensionality reduction technique using pre-trained convolutional neural network (CNN) with EfficientNet model. We introduce two tree search methods with a greedy strategy for improved eigen-component selection. We conducted three experiments to evaluate our approach: examining components choice on test set performance when intentionally overfitting, training on one anomaly type and testing on others, and examining training with a minimal image set based on anomaly types. Unlike traditional methods that emphasize variance, our focus is on maximizing performance and understanding component behavior in diverse settings. Results show our technique outperforms both Principal Component Analysis (PCA) and Negated PCA (NPCA), suggesting a promising advancement in AD efficiency and effectiveness.

关键词： computer vision pattern recognition

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