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Multi-scale Grouped Dense Network for VVC Intra Coding

Multi-scale Grouped Dense Network for VVC Intra Coding

引用

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

作者： Li, Xin Sun, Simeng Zhang, Zhizheng Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Beijing Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Versatile Video Coding (H.266/VVC) standard achieves better image quality when keeping the same bits than any other conventional image codec, such as BPG, JPEG, and etc. However, it is still attractive and challenging to improve the image quality with high compression ratio on the basis of traditional coding techniques. In this paper, we design the multi-scale grouped dense network (MSGDN) to further reduce the compression artifacts by combining the multi-scale and grouped dense block, which are integrated as the post-process network of VVC intra coding. Besides, to improve the subjective quality of compressed image, we also present a generative adversarial network (MSGDN-GAN) by utilizing our MSGDN as generator. Across the extensive experiments on validation set, our MSGDN trained by MSE losses yields the PSNR of 32.622 on average with teams "IMC" and "haha" at the bit-rate of 0.15 in Low-rate track. Moreover, our MSGDN-GAN could achieve the better subjective performance.

关键词： Image coding conferences Generative adversarial networks Video coding Transform coding computer vision pattern recognition

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Dynamic Attention-based Visual Odometry

Dynamic Attention-based Visual Odometry

引用

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

作者： Kuo, Xin-Yu Liu, Chien Lin, Kai-Chen Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan

ISBN: (纸本)9781728193601

This paper proposes a dynamic attention-based visual odometry framework (DAVO), a learning-based VO method, for estimating the ego-motion of a monocular camera. DAVO dynamically adjusts the attention weights on different semantic categories for different motion scenarios based on optical flow maps. These weighted semantic categories can then be used to generate attention maps that highlight the relative importance of different semantic regions in input frames for pose estimation. In order to examine the proposed DAVO, we perform a number of experiments on the KITTI Visual Odometry and SLAM benchmark suite to quantitatively and qualitatively inspect the impacts of the dynamically adjusted weights on the accuracy of the evaluated trajectories. Moreover, we design a set of ablation analyses to justify each of our design choices, and validate the effectiveness as well as the advantages of DAVO. Our experiments on the KITTI dataset shows that the proposed DAVO framework does provide satisfactory performance in ego-motion estimation, and is able deliver competitive performance when compared to the contemporary VO methods.

关键词： computer vision

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A Non-invasive vision Based Approach to Velocity Measurement of Skeleton Training

A Non-invasive Vision Based Approach to Velocity Measurement...

引用

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

作者： Evans, Murray Needham, Laurie Colyer, Steffi L. Cosker, Darren P. Univ Bath Dept Comp Sci Ctr Anal Mot Entertainment Res & Applicat CAMERA Bath Avon England Univ Bath Dept Hlth CAMERA Bath Avon England Univ Bath Dept Comp Sci CAMERA Bath Avon England

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Skeleton is a winter sport where performance is greatly affected by the velocity an athlete can achieve during their start up to the point where they load themselves onto their sled. As such, it is of interest to athletes and coaching staff to be able to monitor the performance of their athletes and how they respond to different training schedules and techniques. This paper proposes a non-invasive vision based method for measuring the velocity of a skeleton athlete and their sled during the push start. Mean differences in estimated velocity between ground truth data and our proposed system were -0.005 (+/- 0.186) m.s(-1) for the athlete mass centre and -0.017 (+/- 0.133) m.s(-1) for the sled. The results compare favourably to techniques previously presented in the biomechanics and sport science literature.

关键词： Cameras Skeleton Image segmentation Head Three-dimensional displays Torso Tracking

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Classification-aware Semi-supervised Domain Adaptation

Classification-aware Semi-supervised Domain Adaptation

引用

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

作者： He, Gewen Liu, Xiaofeng Fan, Fangfang You, Jane Florida State Univ Tallahassee FL 32306 USA Harvard Univ Cambridge MA 02138 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA HK PolyU Hong Kong Peoples R China

ISBN: (纸本)9781728193601

Deep neural networks are usually data-starved, but manually annotation can be costly in many specific tasks. For instance, the emotion recognition from the audio. However, there is a large amount of public available labeled image-based facial expression recognition datasets. How could these images help for the audio emotion recognition with limited labeled data according to their inherent correlations can be a meaningful and challenging task. In this paper, we propose a semi-supervised adversarial network that allows the knowledge transfer from the labeled videos to the heterogeneous labeled audio domain hence enhancing the audio emotion recognition performance. Specifically, face image samples are translated to the spectrograms class-wisely. To harness the translated samples in a sparsely distributed area and construct a tighter decision boundary, we propose to precisely estimate the density on feature space and incorporate the reliable low-density sample with an annealing scheme. Moreover, the unlabeled audios are collected with the high-density path in a graph representation. As a possible "recognition via generation" framework, we empirically demonstrated its effectiveness on several audio emotional recognition benchmarks. We also demonstrated its generality on recent large-scaled semi-supervised domain adaptation tasks.

关键词： Emotion recognition

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Evaluating Scalable Bayesian Deep Learning Methods for Robust computer vision

Evaluating Scalable Bayesian Deep Learning Methods for Robus...

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

作者： Gustafsson, Fredrik K. Danelljan, Martin Schoen, Thomas B. Uppsala Univ Dept Informat Technol Uppsala Sweden Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781728193601

While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, for example in automotive applications. In Bayesian deep learning, predictive uncertainty is commonly decomposed into the distinct types of aleatoric and epistemic uncertainty. The former can be estimated by letting a neural network output the parameters of a certain probability distribution. Epistemic uncertainty estimation is a more challenging problem, and while different scalable methods recently have emerged, no extensive comparison has been performed in a real-world setting. We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. Our proposed framework is specifically designed to test the robustness required in real-world computer vision applications. We also apply this framework to provide the first properly extensive and conclusive comparison of the two current state-of-the-art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates. Code is available at https://***/fregu856/evaluating_bdl.

关键词： computer vision

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An OCR for Classical Indic Documents Containing Arbitrarily Long Words

An OCR for Classical Indic Documents Containing Arbitrarily ...

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

作者： Dwivedi, Agam Saluja, Rohit Sarvadevabhatla, Ravi Kiran Hyderabad IIIT H Int Inst Informat Technol Ctr Visual Informat Technol CVIT Hyderabad 500032 India

ISBN: (纸本)9781728193601

OCR for printed classical Indic documents written in Sanskrit is a challenging research problem. It involves complexities such as image degradation, lack of datasets and long-length words. Due to these challenges, the word accuracy of available OCR systems, both academic and industrial, is not very high for such documents. To address these shortcomings, we develop a Sanskrit specific OCR system. We present an attention-based LSTM model for reading Sanskrit characters in line images. We introduce a dataset of Sanskrit document images annotated at line level. To augment real data and enable high performance for our OCR, we also generate synthetic data via curated font selection and rendering designed to incorporate crucial glyph substitution rules. Consequently, our OCR achieves a word error rate of 15.97% and a character error rate of 3.71% on challenging Indic document texts and outperforms strong baselines. Overall, our contributions set the stage for application of OCRs on large corpora of classic Sanskrit texts containing arbitrarily long and highly conjoined words.

关键词： Optical character recognition

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CLEval: Character-Level Evaluation for Text Detection and recognition Tasks

CLEval: Character-Level Evaluation for Text Detection and Re...

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

作者： Baek, Youngmin Nam, Daehyun Park, Sungrae Lee, Junyeop Shin, Seung Baek, Jeonghun Lee, Chae Young Lee, Hwalsuk NAVER Corp Clova AI Res Seongnam Si South Korea Yale Univ New Haven CT 06520 USA

ISBN: (纸本)9781728193601

Despite the recent success of text detection and recognition methods, existing evaluation metrics fail to provide a fair and reliable comparison among those methods. In addition, there exists no end-to-end evaluation metric that takes characteristics of OCR tasks into account. Previous end-to-end metric contains cascaded errors from the binary scoring process applied in both detection and recognition tasks. Ignoring partially correct results raises a gap between quantitative and qualitative analysis, and prevents fine-grained assessment. Based on the fact that character is a key element of text, we hereby propose a Character-Level Evaluation metric (CLEval). In CLEval, the instance matching process handles split and merge detection cases, and the scoring process conducts character-level evaluation. By aggregating character-level scores, the CLEval metric provides a fine-grained evaluation of end-to-end results composed of the detection and recognition as well as individual evaluations for each module from the end-performance perspective. We believe that our metrics can play a key role in developing and analyzing state-of-the-art text detection and recognition methods. The evaluation code is publicly available at https://***/clovaai/CLEval.

关键词： Character recognition

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Reconstruct, Rasterize and Backprop: Dense shape and pose estimation from a single image

Reconstruct, Rasterize and Backprop: Dense shape and pose es...

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

作者： Pokale, Aniket Aggarwal, Aditya Jatavallabhula, Krishna Murthy Krishna, K. Madhava IIIT Hyderabad Robot Res Ctr KCIS Hyderabad India Univ Montreal Mila Montreal PQ Canada

ISBN: (纸本)9781728193601

This paper presents a new system to obtain dense object reconstructions along with 6-DoF poses from a single image. Geared towards high fidelity reconstruction, several recent approaches leverage implicit surface representations and deep neural networks to estimate a 3D mesh of an object, given a single image. However, all such approaches recover only the shape of an object;the reconstruction is often in a canonical frame, unsuitable for downstream robotics tasks. To this end, we leverage recent advances in differentiable rendering (in particular, rasterization) to close the loop with 3D reconstruction in camera frame. We demonstrate that our approach-dubbed reconstruct, rasterize and backprop (RRB)-achieves significantly lower pose estimation errors compared to prior art, and is able to recover dense object shapes and poses from imagery. We further extend our results to an (offline) setup, where we demonstrate a dense monocular object-centric egomotion estimation system.

关键词： Three dimensional computer graphics

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Optical Braille recognition Based on Semantic Segmentation Network with Auxiliary Learning Strategy

Optical Braille Recognition Based on Semantic Segmentation N...

引用

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

作者： Li, Renqiang Liu, Hong Wang, Xiangdong Xu, Jianxing Qian, Yueliang Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing Peoples R China Northwestern Polytech Univ Xian Peoples R China

ISBN: (纸本)9781728193601

Optical Braille recognition methods usually use many designed steps, such as image de-skewing, Braille dots detection, Braille cell grids construction and Braille character recognition, which are less robust for complex Braille scenes. This paper proposes an optimal semantic segmentation framework BraUNet to directly detect and recognize Braille characters in the whole original Braille images. BraUNet adds extra auxiliary learning strategy to UNet network, which uses long-range connections of feature maps between encoder and decoder to get more low-level features. And auxiliary learning strategy can combine multi-class Braille characters segmentation with Braille foreground extraction, which can improve the feature learning ability and the Braille segmentation performance. Then morphological post-processing is used on semantic segmentation results to get the final individual Braille character regions. Experimental results show the proposed framework is robust, effective and fast for Braille characters segmentation and recognition on both complex double sided Braille image dataset and handwritten Braille image dataset.

关键词： Semantic Segmentation

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Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing

Domain Agnostic Feature Learning for Image and Video Based F...

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

作者： Saha, Suman Xu, Wenhao Kanakis, Menelaos Georgoulis, Stamatios Chen, Yuhua Paudel, Danda Pani Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this direction for biometric user authentication, and in particular face recognition, that tries to prevent spoof attacks. The state-of-the-art anti-spoofing techniques leverage the ability of deep neural networks to learn discriminative features, based on cues from the training set images or video samples, in an effort to detect spoof attacks. However, due to the particular nature of the problem, i.e. large variability due to factors like different backgrounds, lighting conditions, camera resolutions, spoof materials, etc., these techniques typically fail to generalize to new samples. In this paper, we explicitly tackle this problem and propose a class-conditional domain discriminator module, that, coupled with a gradient reversal layer, tries to generate live and spoof features that are discriminative, but at the same time robust against the aforementioned variability factors. Extensive experimental analysis shows the effectiveness of the proposed method over existing image- and video-based anti-spoofing techniques, both in terms of numerical improvement as well as when visualizing the learned features.

关键词： Face Lighting computer vision Authentication Feature extraction Cameras Visualization

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