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MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion

MSTRIQ: No Reference Image Quality Assessment Based on Swin ...

引用

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

作者： Wang, Jing Fan, Haotian Hou, Xiaoxia Xu, Yitian Li, Tao Lu, Xuechao Fu, Lean ByteDance Inc Bldg 10Zone ABusiness PkLane 888Tianlin Rd Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Measuring the perceptual quality of images automatically is an essential task in the area of computer vision, as degradations on image quality can exist in many processes from image acquisition, transmission to enhancing. Many Image Quality Assessment(IQA) algorithms have been designed to tackle this problem. However, it still remains unsettled due to the various types of image distortions and the lack of large-scale human-rated datasets. In this paper, we propose a novel algorithm based on the Swin Transformer [31] with fused features from multiple stages, which aggregates information from both local and global features to better predict the quality. To address the issues of small-scale datasets, relative rankings of images have been taken into account together with regression loss to simultaneously optimize the model. Furthermore, effective data augmentation strategies are also used to improve the performance. In comparisons with previous works, experiments are carried out on two standard IQA datasets and a challenge dataset. The results demonstrate the effectiveness of our work. The proposed method outperforms other methods on standard datasets and ranks 2nd in the no-reference track of NTIRE 2022 Perceptual Image Quality Assessment Challenge [53]. It verifies that our method is promising in solving diverse IQA problems and thus can be used to real-word applications.

关键词： Image quality Training computer vision Predictive models Transformers Prediction algorithms Distortion

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Dual Attention Poser: Dual Path Body Tracking Based on Attention

Dual Attention Poser: Dual Path Body Tracking Based on Atten...

引用

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

作者： Di, Xinhan Dai, Xiaokun Zhang, Xinkang Chen, Xinrong Fudan Universiry Academy for Engineering&Technology China Deepearthgo Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China

ISBN: (纸本)9798350302493

Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users' virtual representation and experience is limited due to high reconstruction error when simple transformer network architecture is applied. In this paper, we present a novel model, named Dual Attention Poser, which can learn the whole body reconstruction at a high accuracy. The proposed model consists of three key modules. Among them, dual-path attention encoder is designed to extract feature of the sparse signals. Cross attention mixer module enable the fusion of representation in the double path. Attention-gated-mlp decoder is applied to decode the hidden feature from the sparse input through attention gate. Test results on the AMASS dataset show that Dual Attention Poser can reduce the error by up to 18.2% in comparison with the state-of-the-art results. © 2023 ieee.

关键词： Network architecture

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Asynchronous Federated Continual Learning

Asynchronous Federated Continual Learning

引用

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

作者： Shenaj, Donald Toldo, Marco Rigon, Alberto Zanuttigh, Pietro University of Padova Italy

ISBN: (纸本)9798350302493

The standard class-incremental continual learning setting assumes a set of tasks seen one after the other in a fixed and pre-defined order. This is not very realistic in federated learning environments where each client works independently in an asynchronous manner getting data for the different tasks in time-frames and orders totally uncorrelated with the other ones. We introduce a novel federated learning setting (AFCL) where the continual learning of multiple tasks happens at each client with different orderings and in asynchronous time slots. We tackle this novel task using prototype-based learning, a representation loss, fractal pre-training, and a modified aggregation policy. Our approach, called FedSpace, effectively tackles this task as shown by the results on the CIFAR-100 dataset using 3 different federated splits with 50, 100, and 500 clients, respectively. The code and federated splits are available at https://***/LTTM/FedSpace. © 2023 ieee.

关键词： computer aided instruction

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Multi-level Domain Adaptation for Lane Detection

Multi-level Domain Adaptation for Lane Detection

引用

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

作者： Li, Chenguang Zhang, Boheng Shi, Jia Cheng, Guangliang SenseTime Res Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We focus on bridging domain discrepancy in lane detection among different scenarios to greatly reduce extra annotation and re-training costs for autonomous driving. Critical factors hinder the performance improvement of cross-domain lane detection that conventional methods only focus on pixel-wise loss while ignoring shape and position priors of lanes. To address the issue, we propose the Multi-level Domain Adaptation (MLDA) framework, a new perspective to handle cross-domain lane detection at three complementary semantic levels of pixel, instance and category. Specifically, at pixel level, we propose to apply cross-class confidence constraints in self-training to tackle the imbalanced confidence distribution of lane and background. At instance level, we go beyond pixels to treat segmented lanes as instances and facilitate discriminative features in target domain with triplet learning, which effectively rebuilds the semantic context of lanes and contributes to alleviating the feature confusion. At category level, we propose an adaptive inter-domain embedding module to utilize the position prior of lanes during adaptation. In two challenging datasets, i.e. TuSimple and CULane, our approach improves lane detection performance by a large margin with gains of 8.8% on accuracy and 7.4% on F1-score respectively, compared with state-of-the-art domain adaptation algorithms.

关键词： computer vision Costs Lane detection Shape Image edge detection conferences Semantics

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Focused Feature Differentiation Network for Image Quality Assessment

Focused Feature Differentiation Network for Image Quality As...

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

作者： He, Gang Wang, Yong Xu, Li Zhang, Wenli Sun, Ming Wen, Xing Xidian Univ Xian Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image quality assessment (IQA) intended to assess the perceptual quality of images has been an essential problem in both human and machine vision. Recently, with the help of deep neural network (DNN), IQA algorithms can extract more valuable differences between the distorted and reference images than the traditional algorithms, and thus the performance of DNN-based algorithms is more satisfactory than that of previous algorithms. However, the accuracy for different distorted images preference rating of the existing DNN-based quality assessment methods will be decreased when multiple distorted images are quite similar to each other or to the reference image. To tackle this problem, we propose a focused feature differentiation network (FFDN) to highlight the feature maps with greater distorted and reference differentiation. Furthermore, we use the multi-scale feature fusion module to fuse the focused differentiation features at different scale receptive fields. To further improve the accuracy of our method, we predict the mean opinion score and differentiation score by stages and combine them with different self-learning weights. Finally, we convert the weighted score into different image preference degrees. Experimental results on the validation dataset of CLIC2022 and test dataset of CLIC2021 show that the accuracy of our model FFDN is higher than other excellent quality assessment methods.

关键词： Image quality Deep learning Fuses Convolution Machine vision conferences Neural networks

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SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos

SoccerTrack: A Dataset and Tracking Algorithm for Soccer wit...

引用

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

作者： Scott, Atom Uchida, Ikuma Onishi, Masaki Kameda, Yoshinari Fukui, Kazuhiro Fujii, Keisuke Univ Tsukuba Tsukuba Ibaraki Japan Natl Inst Adv Ind Sci & Technol Tsukuba Japan Nagoya Univ Nagoya Aichi Japan RIKEN JST PRESTO Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Tracking devices that can track both players and balls are critical to the performance of sports teams. Recently, significant effort has been focused on building larger broadcast sports video datasets. However, broadcast videos do not show the entire pitch and only provides partial information about the game. On the other hand, other camera perspectives can capture the whole field in a single frame, such as fish-eye and bird-eye view (drone) cameras. Unfortunately, there has not been a dataset where such data has been publicly shared until now. This paper proposes SoccerTrack, a dataset set consisting of GNSS and bounding box tracking data annotated on video captured with a 8K-resolution fish-eye camera and a 4K-resolution drone camera. In addition to a benchmark tracking algorithm, we include code for camera calibration and other preprocessing. Finally, we evaluate the tracking accuracy among a GNSS, fish-eye camera and drone camera data. SoccerTrack is expected to provide a more robust foundation for designing MOT algorithms that are less reliant on visual cues and more reliant on motion analysis.

关键词： Performance evaluation Global navigation satellite system Visualization Codes Tracking Cameras pattern recognition

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A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds

A Closer Look at Blind Super-Resolution: Degradation Models,...

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

作者： Zhang, Wenlong Shi, Guangyuan Liu, Yihao Dong, Chao Wu, Xiao-Ming HongKong Polytech Univ Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Degradation models play an important role in Blind super-resolution (SR). The classical degradation model, which mainly involves blur degradation, is too simple to simulate real-world scenarios. The recently proposed practical degradation model includes a full spectrum of degradation types, but only considers complex cases that use all degradation types in the degradation process, while ignoring many important corner cases that are common in the real world. To address this problem, we propose a unified gated degradation model to generate a broad set of degradation cases using a random gate controller. Based on the gated degradation model, we propose simple baseline networks that can effectively handle non-blind, classical, practical degradation cases as well as many other corner cases. To fairly evaluate the performance of our baseline networks against state-of-the-art methods and understand their limits, we introduce the performance upper bound of an SR network for every degradation type. Our empirical analysis shows that with the unified gated degradation model, the proposed baselines can achieve much better performance than existing methods in quantitative and qualitative results, which are close to the performance upper bounds.

关键词： Degradation Analytical models computer vision Upper bound conferences Superresolution Logic gates

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Contrastive Learning-based Robust Object Detection under Smoky Conditions

Contrastive Learning-based Robust Object Detection under Smo...

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

作者： Wu, Wei Chang, Hao Zheng, Yonghua Li, Zhu Chen, Zhiwen Zhang, Ziheng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Object detection is to effectively find out interested targets in images and then accurately determine their categories and positions. Recently many excellent methods have been developed to provide powerful detection capability. However, their performance may degrade significantly under severe weather such as smoky conditions. In this paper, we propose a contrastive learning-based robust object detection algorithm for smoke images. The proposed object detector consists of two modules: contrastive learning module and object bounding box prediction module. The first module learns representation vectors by maximizing agreement between different augmented views of the same smoke image. These representations are then sent to the second module to yield the bounding box for each object. In addition, we also propose a novel affine data augmentation method. Extensive experiments have been conducted on A2I2-Haze dataset which is the first real haze dataset with in-situ smoke measurement aligned to aerial and ground imagery. This dataset is also the only dataset used in the 5th UG(2)+ challenges of CVPR 2022 for both training and testing. Compared with state-of-the-art methods, evaluation results show the superiority of our proposed object detector.

关键词： Training computer vision conferences Object detection Detectors Transforms Prediction algorithms

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Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators

Real-time Hyper-Dimensional Reconfiguration at the Edge usin...

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

作者： Kandaswamy, Indhumathi Farkya, Saurabh Daniels, Zachary van der Wal, Gooitzen Raghavan, Aswin Zhang, Yuzheng Hu, Jun Lomnitz, Michael Isnardi, Michael Zhang, David Piacentino, Michael SRI Int Ctr Vis Technol Princeton NJ 08540 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point Multiply-ACcumulate operations) deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators. We describe the algorithm, trained quantized model generation, and simulated performance of a feature extractor free of multiply-accumulates feeding a hyperdimensional logic-based classifier. Then we show how performance increases with the number of hyperdimensions. We describe the realized low-SWaP FPGA hardware and embedded software system compared to traditional DNNs and detail the implemented hardware accelerators. We discuss the measured system latency and power, noise robustness due to use of learnable quantization and HD computing, actual versus simulated system performance for a video activity classification task and demonstration of reconfiguration on this same dataset. We show that reconfigurability in the field is achieved by retraining only the feed-forward HD classifier without gradient descent backpropagation (gradient-free), using few-shot learning of new classes at the edge. Initial work performed used LRCN DNN and is currently extended to use Two-stream DNN with improved performance.

关键词： Quantization (signal) Power measurement System performance Software algorithms Feature extraction Real-time systems pattern recognition

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3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D Residual in Residual Dense Blocks

3DRRDB: Super Resolution of Multiple Remote Sensing Images u...

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

作者： Ramzy Ibrahim, Mohamed Benavente, Robert Lumbreras, Felipe Ponsa, Daniel Arab Acad Sci & Technol Comp Engn Dept Alexandria Egypt Univ Autonoma Barcelona Dept Comp Sci Barcelona Spain Comp Vis Ctr Campus UAB Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively.

关键词： Training Solid modeling Three-dimensional displays PSNR Convolution Superresolution pattern recognition

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