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Constrained Evolutionary Diffusion Filter for Monocular Endoscope Tracking

Constrained Evolutionary Diffusion Filter for Monocular Endo...

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conference on computer vision and pattern recognition (cvpr)

作者： Xiongbiao Luo Department of Computer Science and Technology Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

Stochastic filtering is widely used to deal with nonlinear optimization problems such as 3-D and visual tracking in various computer vision and augmented reality applications. Many current methods suffer from an imbalance between exploration and exploitation due to their particle degeneracy and impoverishment, resulting in local optimums. To address this imbalance, this work proposes a new constrained evolutionary diffusion filter for nonlinear optimization. Specifically, this filter develops spatial state constraints and adaptive history-recall differential evolution embedded evolutionary stochastic diffusion instead of sequential resampling to resolve the degeneracy and impoverishment problem. With application to monocular endoscope 3-D tracking, the experimental results show that the proposed filtering significantly improves the balance between exploration and exploitation and certainly works better than recent 3-D tracking methods. Particularly, the surgical tracking error was reduced from 4.03 mm to 2.59 mm.

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Learning Triadic Belief Dynamics in Nonverbal Communication from Videos

Learning Triadic Belief Dynamics in Nonverbal Communication ...

引用

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

作者： Fan, Lifeng Qiu, Shuwen Zheng, Zilong Gao, Tao Zhu, Song-Chun Zhu, Yixin Univ Calif Los Angeles Ctr Vis Cognit Learning & Auton Los Angeles CA 90024 USA

ISBN: (纸本)9781665445092

Humans possess a unique social cognition capability [43, 20];nonverbal communication can convey rich social information among agents. In contrast, such crucial social characteristics are mostly missing in the existing scene understanding literature. In this paper, we incorporate different nonverbal communication cues (e.g., gaze, human poses, and gestures) to represent, model, learn, and infer agents' mental states from pure visual inputs. Crucially, such a mental representation takes the agent's belief into account so that it represents what the true world state is and infers the beliefs in each agent's mental state, which may differ from the true world states. By aggregating different beliefs and true world states, our model essentially forms "five minds" during the interactions between two agents. This "five minds" model differs from prior works that infer beliefs in an infinite recursion: instead, agents' beliefs are converged into a "common mind" [31, 47]. Based on this representation, we further devise a hierarchical energy-based model that jointly tracks and predicts all five minds. From this new perspective, a social event is interpreted by a series of nonverbal communication and belief dynamics, which transcends the classic keyframe video summary . . In the experiments, we demonstrate that using such a social account provides a better video summary on videos with rich social interactions compared with state-of-the-art keyframe video summary methods.

关键词： Visualization computer vision Three-dimensional displays Graphical models Heuristic algorithms Predictive models Probabilistic logic

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Shot in the Dark: Few-Shot Learning with No Base-Class Labels

Shot in the Dark: Few-Shot Learning with No Base-Class Label...

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

作者： Chen, Zitian Maji, Subhransu Learned-Miller, Erik Univ Massachusetts Amherst Amherst MA 01003 USA

ISBN: (纸本)9781665448994

Few-shot learning aims to build classifiers for new classes from a small number of labeled examples and is commonly facilitated by access to examples from a distinct set of 'base classes'. The difference in data distribution between the test set (novel classes) and the base classes used to learn an inductive bias often results in poor generalization on the novel classes. To alleviate problems caused by the distribution shift, previous research has explored the use of unlabeled examples from the novel classes, in addition to labeled examples of the base classes, which is known as the transductive setting. In this work, we show that, surprisingly, off-the-shelf self-supervised learning outperforms transductive few-shot methods by 3.9% for 5-shot accuracy on miniImageNet without using any base class labels. This motivates us to examine more carefully the role of features learned through self-supervision in few-shot learning. Comprehensive experiments are conducted to compare the transferability, robustness, efficiency, and the complementarity of supervised and self-supervised features.

关键词： computer vision conferences Supervised learning Robustness pattern recognition

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Sign Language Production: A Review

Sign Language Production: A Review

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

作者： Rastgoo, Razieh Kiani, Kourosh Escalera, Sergio Sabokrou, Mohammad Semnan Univ Semnan Iran Inst Res Fundamental Sci IPM Tehran Iran Univ Barcelona Barcelona Spain Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9781665448994

Sign Language is the dominant yet non-primary form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental. To this end, sign language recognition and production are two necessary parts for making such a two-way system. Sign language recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. This survey aims to briefly summarize recent achievements in SLP, discussing their advantages, limitations, and future directions of research.

关键词： Deep learning computer vision Assistive technology conferences Buildings Gesture recognition Production

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Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation

Dealing with Missing Modalities in the Visual Question Answe...

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

作者： Cho, Jae Won Kim, Dong-Jin Choi, Jinsoo Jung, Yunjae Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

In this work, we address the issues of the missing modalities that have arisen from the Visual Question Answer-Difference prediction task and find a novel method to solve the task at hand. We address the missing modality-the ground truth answers-that are not present at test time and use a privileged knowledge distillation scheme to deal with the issue of the missing modality. In order to efficiently do so, we first introduce a model, the "Big" Teacher, that takes the image/question/answer triplet as its input and outperforms the baseline, then use a combination of models to distill knowledge to a target network (student) that only takes the image/question pair as its inputs. We experiment our models on the VizWiz and VQA-V2 Answer Difference datasets and show through extensive experimentation and ablation the performance of our method and a diverse possibility for future research.

关键词： Visualization computer vision conferences Knowledge discovery pattern recognition Task analysis Bars

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Dual Contrastive Learning for Unsupervised Image-to-Image Translation

Dual Contrastive Learning for Unsupervised Image-to-Image Tr...

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

作者： Han, Junlin Shoeiby, Mehrdad Petersson, Lars Armin, Mohammad Ali DATA61 CSIRO Canberra ACT Australia Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665448994

Unsupervised image-to-image translation tasks aim to find a mapping between a source domain X and a target domain Y from unpaired training data. Contrastive learning for Unpaired image-to-image Translation (CUT) yields state-of-the-art results in modeling unsupervised image-to-image translation by maximizing mutual information between input and output patches using only one encoder for both domains. In this paper, we propose a novel method based on contrastive learning and a dual learning setting (exploiting two encoders) to infer an efficient mapping between unpaired data. Additionally, while CUT suffers from mode collapse, a variant of our method efficiently addresses this issue. We further demonstrate the advantage of our approach through extensive ablation studies demonstrating superior performance comparing to recent approaches in multiple challenging image translation tasks. Lastly, we demonstrate that the gap between unsupervised methods and supervised methods can be efficiently closed.

关键词： computer vision conferences Training data pattern recognition Task analysis Mutual information

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Adaptive Weighted Discriminator for Training Generative Adversarial Networks

Adaptive Weighted Discriminator for Training Generative Adve...

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

作者： Zadorozhnyy, Vasily Cheng, Qiang Ye, Qiang Univ Kentucky Dept Math Lexington KY 40506 USA Univ Kentucky Inst Biomed Informat Dept Comp Sci Lexington KY 40506 USA Univ Kentucky Inst Biomed Informat Dept Internal Med Lexington KY 40506 USA

ISBN: (纸本)9781665445092

Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN's discriminators and they all have a common structure: a sum of real and fake losses that only depends on the actual and generated data respectively. One challenge associated with an equally weighted sum of two losses is that the training may benefit one loss but harm the other, which we show causes instability and mode collapse. In this paper, we introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions or aw-loss functions. Using the gradients of the real and fake parts of the loss, we can adaptively choose weights to train a discriminator in the direction that benefits the GAN's stability. Our method can be potentially applied to any discriminator model with a loss that is a sum of the real and fake parts. Experiments validated the effectiveness of our loss functions on unconditional and conditional image generation tasks, improving the baseline results by a significant margin on CIFAR-10, STL-10, and CIFAR-100 datasets in Inception Scores (IS) and Frechet Inception Distance (FID) metrics.

关键词： Training Measurement computer vision Image synthesis Neural networks Machine learning Generative adversarial networks

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Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences

Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D ...

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

作者： Mueller, Norman Wong, Yu-Shiang Mitra, Niloy J. Dai, Angela Niessner, Matthias Tech Univ Munich Munich Germany UCL London England Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

Multi-object tracking from RGB-D video sequences is a challenging problem due to the combination of changing viewpoints, motion, and occlusions over time. We observe that having the complete geometry of objects aids in their tracking, and thus propose to jointly infer the complete geometry of objects as well as track them, for rigidly moving objects over time. Our key insight is that inferring the complete geometry of the objects significantly helps in tracking. By hallucinating unseen regions of objects, we can obtain additional correspondences between the same instance, thus providing robust tracking even under strong change of appearance. From a sequence of RGB-D frames, we detect objects in each frame and learn to predict their complete object geometry as well as a dense correspondence mapping into a canonical space. This allows us to derive 6DoF poses for the objects in each frame, along with their correspondence between frames, providing robust object tracking across the RGB-D sequence. Experiments on both synthetic and real-world RGB-D data demonstrate that we achieve state-of-the-art performance on dynamic object tracking. Furthermore, we show that our object completion significantly helps tracking, providing an improvement of 6.5% in mean MOTA.

关键词： Geometry computer vision Three-dimensional displays Video sequences Pose estimation Pipelines Cameras

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Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding

Cycle4Completion: Unpaired Point Cloud Completion using Cycl...

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

作者： Wen, Xin Han, Zhizhong Cao, Yan-Pei Wan, Pengfei Zheng, Wen Liu, Yu-Shen Tsinghua Univ Sch Software BNRist Beijing Peoples R China Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Kuaishou Technol Y Tech Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we present a novel unpaired point cloud completion network, named Cycle4Completion, to infer the complete geometries from a partial 3D object. Previous unpaired completion methods merely focus on the learning of geometric correspondence from incomplete shapes to complete shapes, and ignore the learning in the reverse direction, which makes them suffer from low completion accuracy due to the limited 3D shape understanding ability. To address this problem, we propose two simultaneous cycle transformations between the latent spaces of complete shapes and incomplete ones. Specifically, the first cycle transforms shapes from incomplete domain to complete domain, and then projects them back to the incomplete domain. This process learns the geometric characteristic of complete shapes, and maintains the shape consistency between the complete prediction and the incomplete input. Similarly, the inverse cycle transformation starts from complete domain to incomplete domain, and goes back to complete domain to learn the characteristic of incomplete shapes. We experimentally show that our model with the learned bidirectional geometry correspondence outperforms state-of-the-art unpaired completion methods. Code will be available at https://***/diviswen/Cycle4Completion.

关键词： Geometry computer vision Three-dimensional displays Codes Shape Computational modeling Transforms

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CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

CR-FIQA: Face Image Quality Assessment by Learning Sample Re...

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conference on computer vision and pattern recognition (cvpr)

作者： Fadi Boutros Meiling Fang Marcel Klemt Biying Fu Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. This work proposes a novel FIQA method, CR-FIQA, that estimates the face image quality of a sample by learning to predict its relative classifiability. This classifiability is measured based on the allocation of the training sample feature representation in angular space with respect to its class center and the nearest negative class center. We experimentally illustrate the correlation between the face image quality and the sample relative classifiability. As such property is only observable for the training dataset, we propose to learn this property by probing internal network observations during the training process and utilizing it to predict the quality of unseen samples. Through extensive evaluation experiments on eight benchmarks and four face recognition models, we demonstrate the superiority of our proposed CR-FIQA over state-of-the-art (SOTA) FIQA algorithms. 1 1 https://***/fdbtrs/CR-FIQA

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