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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Troubleshooting Blind Image Quality Models in the Wild

Troubleshooting Blind Image Quality Models in the Wild

引用

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

作者： Wang, Zhihua Wang, Haotao Chen, Tianlong Wang, Zhangyang Ma, Kede City Univ Hong Kong Hong Kong Peoples R China Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When applying this type of approach to troubleshoot "best-performing" BIQA models in the wild, we are faced with a practical challenge: it is highly nontrivial to obtain stronger competing models for efficient failure-spotting. Inspired by recent findings that difficult samples of deep models may be exposed through network pruning, we construct a set of "self-competitors," as random ensembles of pruned versions of the target model to be improved. Diverse failures can then be efficiently identified via self-gMAD competition. Next, we fine-tune both the target and its pruned variants on the human-rated gMAD set. This allows all models to learn from their respective failures, preparing themselves for the next round of self-gMAD competition. Experimental results demonstrate that our method efficiently troubleshoots BIQA models in the wild with improved generalizability.

关键词： Image quality Measurement computer vision Computational modeling Mathematical models pattern recognition Computational efficiency

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Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers

Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Cen...

引用

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

作者： Bhattacharyya, Apratim Reino, Daniel Olmeda Fritz, Mario Schiele, Bernt Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany Toyota Motor Europe Brussels Belgium CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany

ISBN: (纸本)9781665445092

Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions. Although recent datasets and trajectory prediction approaches have fostered the development of autonomous vehicles yet the amount of vehicle-pedestrian (bicyclist) interactions modeled are sparse. In this work, we propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In particular, our dataset caters more diverse and complex interactions in dense urban scenarios compared to the existing datasets. To address the challenges in predicting future trajectories with dense interactions, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. This enables our Joint-beta-cVAE approach to better model the distribution of future trajectories. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.

关键词： computer vision Computational modeling Urban areas Predictive models Turning Trajectory pattern recognition

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PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors

PSD: Principled Synthetic-to-Real Dehazing Guided by Physica...

引用

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

作者： Chen, Zeyuan Wang, Yangchao Yang, Yang Liu, Dong Univ Sci & Technol China Hefei Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665445092

Deep learning-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on training models with synthetic hazy images, which incurs performance drop when the models are used for real-world hazy images. We propose a Principled Synthetic-to-real Dehazing (PSD) framework to improve the generalization performance of dehazing. Starting from a dehazing model backbone that is pre-trained on synthetic data, PSD exploits real hazy images to fine-tune the model in an unsupervised fashion. For the fine-tuning, we leverage several well-grounded physical priors and combine them into a prior loss committee. PSD allows for most of the existing dehazing models as its backbone, and the combination of multiple physical priors boosts dehazing significantly. Through extensive experiments, we demonstrate that our PSD framework establishes the new state-of-the-art performance for real-world dehazing, in terms of visual quality assessed by no-reference quality metrics as well as subjective evaluation and downstream task performance indicator.

关键词： Training Measurement Learning systems Visualization computer vision Data models pattern recognition

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Dynamic Appearance Modelling from Minimal Cameras

Dynamic Appearance Modelling from Minimal Cameras

引用

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

作者： Bridgeman, Lewis Guillemaut, Jean-Yves Hilton, Adrian Univ Surrey CVSSP Guildford Surrey England

ISBN: (纸本)9781665448994

We present a novel method for modelling dynamic texture appearance from a minimal set of cameras. Previous methods to capture the dynamic appearance of a human from multi-view video have relied on large, expensive camera setups, and typically store texture on a frame-by-frame basis. We fit a parameterised human body model to multi-view video from minimal cameras (as few as 3), and combine the partial texture observations from multiple viewpoints and frames in a learned framework to generate full-body textures with dynamic details given an input pose. Key to our method are our multi-band loss functions, which apply separate blending functions to the high and low spatial frequencies to reduce texture artefacts. We evaluate our method on a range of multi-view datasets, and show that our model is able to accurately produce full-body dynamic textures, even with only partial camera coverage. We demonstrate that our method outperforms other texture generation methods on minimal camera setups.

关键词： computer vision Computational modeling conferences Biological system modeling Cameras pattern recognition

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Emotion recognition Using Transformers with Random Masking

Emotion Recognition Using Transformers with Random Masking

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Seongjae Min Junseok Yang Sejoon Lim Kookmin University Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, deep learning has achieved innovative advancements in various fields, including the analysis of human emotions and behaviors. Initiatives such as the Affective Behavior Analysis in-the-wild (ABAW) competition have been particularly instrumental in driving research in this area by providing diverse and challenging datasets that enable precise evaluation of complex emotional states. This study leverages the vision Transformer (ViT) and Transformer models to focus on the estimation of Valence-Arousal (VA), which signifies the positivity and intensity of emotions, recognition of various facial expressions, and detection of Action Units (AU) representing fundamental muscle movements. This approach transcends traditional Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) based methods, proposing a new Transformer-based framework that maximizes the understanding of temporal and spatial features. The core contributions of this research include the introduction of a learning technique through random frame masking and the application of Focal loss adapted for imbalanced data, enhancing the accuracy and applicability of emotion and behavior analysis in real-world settings. This approach is expected to contribute to the advancement of emotional computing and deep learning methodologies.

关键词： Deep learning Emotion recognition Gold computer vision Computational modeling Estimation Transformer cores

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Where and What? Examining Interpretable Disentangled Representations

Where and What? Examining Interpretable Disentangled Represe...

引用

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

作者： Zhu, Xinqi Xu, Chang Tao, Dacheng Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

Capturing interpretable variations has long been one of the goals in disentanglement learning. However, unlike the independence assumption, interpretability has rarely been exploited to encourage disentanglement in the unsupervised setting. In this paper, we examine the interpretability of disentangled representations by investigating two questions: where to be interpreted and what to be interpreted? A latent code is easily to be interpreted if it would consistently impact a certain subarea of the resulting generated image. We thus propose to learn a spatial mask to localize the effect of each individual latent dimension. On the other hand, interpretability usually comes from latent dimensions that capture simple and basic variations in data. We thus impose a perturbation on a certain dimension of the latent code, and expect to identify the perturbation along this dimension from the generated images so that the encoding of simple variations can be enforced. Additionally, we develop an unsupervised model selection method, which accumulates perceptual distance scores along axes in the latent space. On various datasets, our models can learn high-quality disentangled representations without supervision, showing the proposed modeling of interpretability is an effective proxy for achieving unsupervised disentanglement.

关键词： computer vision Codes Image coding Perturbation methods Computational modeling Encoding pattern recognition

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AI-based substation construction remote management and violation detection system 5

AI-based substation construction remote management and viola...

引用

5th International conference on computer Information Science and Artificial Intelligence, CISAI 2022

作者： Quanjing, Li Kai, Yan Hao, Li Yang, Yang School of Information Science and Technology Yunnan Normal University Kunming650500 China The Laboratory of Pattern Recognition and Artificial Intelligence Yunnan Normal University Kunming650500 China

ISBN: (纸本)9781510662520

The unattended operation of substations has become the development trend of today's power system. The traditional substation management system has outstanding problems such as complex management process and inability to realize remote unmanned control. In order to solve the above problems, electronic visa technology is proposed to realize remote unmanned permit, to realize real-time detection of remote specific targets using detection algorithms, and to track and record specific targets and behaviors using target-tracking algorithms. This system has been promoted and used in various substations in Yuxi City, Yunnan Province, solving the problem of remote safety control in the process of power production. © 2023 SPIE.

关键词： Target tracking

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Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution

Leveraging Multi scale Backbone with Multilevel supervision ...

引用

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

作者： Nathan, Sabari Kansal, Priya Couger Inc Shibuya Ku Tokyo Japan

ISBN: (纸本)9781665448994

This paper proposes an attention-based multi-level model with a multi-scale backbone for thermal image super-resolution. The model leverages the multi-scale backbone as well. The thermal image dataset is provided by PBVS 2020 in their thermal image super-resolution challenge. This dataset contains the images with three different resolution scales(low, medium, high) [1]. However, only the medium and high-resolution images are used to train the proposed architecture to generate the super-resolution images in x2, x4 scales. The proposed architecture is based on the Res2net blocks as the backbone of the network. Along with this, the coordinate convolution layer and dual attention are also used in the architecture. Further, multi-level supervision is implemented to supervise the output image resolution similarity with the real image at each block during training. To test the robustness of the proposed model, we evaluated our model on the Thermal-6 dataset [20]. The results show that our model is efficient to achieve state-of-the-art results on the PBVS dataset. Further the results on the Thermal-6 dataset show that the model has a decent generalization capacity.

关键词： Training Convolution conferences Superresolution computer architecture Robustness pattern recognition

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Anomaly Score: Evaluating Generative Models and Individual Generated Images Based on Complexity and Vulnerability

Anomaly Score: Evaluating Generative Models and Individual G...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jaehui Hwang Junghyuk Lee Jong-Seok Lee School of Integrated Technology / BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Korea

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

With the advancement of generative models, the assessment of generated images becomes increasingly more important. Previous methods measure distances between features of reference and generated images from trained vision models. In this paper, we conduct an extensive investigation into the relationship between the representation space and input space around generated images. We first propose two measures related to the presence of unnatural elements within images: complexity, which indicates how nonlinear the representation space is, and vulnerability, which is related to how easily the extracted feature changes by adversarial input changes. Based on these, we introduce a new metric to evaluating image-generative models called anomaly score (AS). Moreover, we propose AS-i (anomaly score for individual images) that can effectively evaluate generated images individually. Experimental results demonstrate the validity of the proposed approach.

关键词： Measurement computer vision Feature extraction Complexity theory pattern recognition

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Evidential Active recognition: Intelligent and Prudent Open-World Embodied Perception

Evidential Active Recognition: Intelligent and Prudent Open-...

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conference on computer vision and pattern recognition (CVPR)

作者： Lei Fan Mingfu Liang Yunxuan Li Gang Hua Ying Wu Northwestern University Wormpex AI Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Active recognition enables robots to intelligently explore novel observations, thereby acquiring more information while circumventing undesired viewing conditions. Recent approaches favor learning policies from simulated or collected data, wherein appropriate actions are more frequently selected when the recognition is accurate. However, most recognition modules are developed under the closed-world assumption, which makes them ill-equipped to handle unexpected inputs, such as the absence of the target object in the current observation. To address this issue, we propose treating active recognition as a sequential evidence-gathering process, providing by-step uncertainty quantification and reliable prediction under the evidence combination theory. Additionally, the reward function developed in this paper effectively characterizes the merit of actions when operating in open-world environments. To evaluate the performance, we collect a dataset from an indoor simulator, encompassing various recognition challenges such as distance, occlusion levels, and visibility. Through a series of experiments on recognition and robustness analysis, we demonstrate the necessity of introducing uncertainties to active recognition and the superior performance of the proposed method.

关键词： computer vision Uncertainty Accuracy Target recognition Face recognition Reliability theory Robustness

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