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Semi-Supervised Video Semantic Segmentation with Inter-Frame Feature Reconstruction

Semi-Supervised Video Semantic Segmentation with Inter-Frame...

引用

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

作者： Zhuang, Jiafan Wang, Zilei Gao, Yuan Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

One major challenge for semantic segmentation in real-world scenarios is only limited pixel-level labels available due to high expense of human labor though a vast volume of video data is provided. Existing semi-supervised methods attempt to exploit unlabeled data in model training, but they just regard video as a set of independent images. To better explore semi-supervised segmentation problem with video data, we formulate a semi-supervised video semantic segmentation task in this paper. For this task, we observe that the overfitting is surprisingly severe between labeled and unlabeled frames within a training video although they are very similar in style and contents. This is called inner-video overfitting, and it would actually lead to inferior performance. To tackle this issue, we propose a novel interframe feature reconstruction (IFR) technique to leverage the ground-truth labels to supervise the model training on unlabeled frames. IFR is essentially to utilize the internal relevance of different frames within a video. During training, IFR would enforce the feature distributions between labeled and unlabeled frames to be narrowed. Consequently, the inner-video overfitting issue can be effectively alleviated. We conduct extensive experiments on Cityscapes and CamVid, and the results demonstrate the superiority of our proposed method to previous state-of-the-art methods. The code is available at https: / /***/jfzhuang/IFR.

关键词： Training Image segmentation computer vision Codes Semantics Supervised learning Data models

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Inferring temporal order of images from 3D structure

Inferring temporal order of images from 3D structure

引用

ieee conference on computer vision and pattern recognition

作者： Schindler, Grant Dellaert, Frank Kang, Sing Bing Georgia Inst Technol Atlanta GA 30332 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781424411795

In this paper, we describe a technique to temporally sort a collection of photos that span many years. By reasoning about persistence of visible structures, we show how this sorting task can be formulated as a constraint satisfaction problem (CSP). Casting this problem as a CSP allows us to efficiently find a suitable ordering of the images despite the large size of the solution space (factorial in the number of images) and the presence of occlusions. We present experimental resultsfor photographs of a city acquired over a one hundred year period.

关键词： Three dimensional computer graphics

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DVMNet: Computing Relative Pose for Unseen Objects Beyond Hypotheses

DVMNet: Computing Relative Pose for Unseen Objects Beyond Hy...

引用

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

作者： Zhao, Chen Zhang, Tong Dang, Zheng Salzmann, Mathieu Ecole Polytech Fed Lausanne Lausanne Switzerland ClearSpace SA Renens Switzerland

ISBN: (纸本)9798350353006

Determining the relative pose of an object between two images is pivotal to the success of generalizable object pose estimation. Existing approaches typically approximate the continuous pose representation with a large number of discrete pose hypotheses, which incurs a computationally expensive process of scoring each hypothesis at test time. By contrast, we present a Deep Voxel Matching Network (DVMNet) that eliminates the need for pose hypotheses and computes the relative object pose in a single pass. To this end, we map the two input RGB images, reference and query, to their respective voxelized 3D representations. We then pass the resulting voxels through a pose estimation module, where the voxels are aligned and the pose is computed in an end-to-end fashion by solving a least-squares problem. To enhance robustness, we introduce a weighted closest voxel algorithm capable of mitigating the impact of noisy voxels. We conduct extensive experiments on the CO3D, LINEMOD, and Objaverse datasets, demonstrating that our method delivers more accurate relative pose estimates for novel objects at a lower computational cost compared to state-of-the-art methods. Our code is released at: https://***/sailor-z/DVMNet/.

关键词： 3D computer vision Multi-View Geometry Object Pose Estimation

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Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection

Informative and Consistent Correspondence Mining for Cross-D...

引用

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

作者： Hou, Luwei Zhang, Yu Fu, Kui Li, Jia Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China SenseTime Res Beijing Peoples R China

ISBN: (纸本)9781665445092

Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i.e., with object bounding boxes) to train object detectors for target domains that are weakly labeled (i.e., with image-level tags). Instead of domain-level distribution matching, as popularly adopted in the literature, we propose to learn pixel-wise cross-domain correspondences for more precise knowledge transfer. It is realized through a novel cross-domain co-attention scheme trained as region competition. In this scheme, the cross-domain correspondence module seeks for informative features on the target domain image, which if warped to the source domain image, could best explain its annotations. Meanwhile, a collaborative mask generator competes to mask out the relevant target image region to make the remaining features uninformative. Such competitive learning strives to correlate the full foreground in cross-domain image pairs, revealing the accurate object extent in target domain. To alleviate the ambiguity of inter-domain correspondence learning, a domain-cycle consistency regularizer is further proposed to leverage the more reliable intra-domain correspondence. The proposed approach achieves consistent improvements over existing approaches by a considerable margin, demonstrated by the experiments on various datasets.

关键词： computer vision Annotations Collaboration Object detection Detectors Generators pattern recognition

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A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres

A Brand New Dance Partner: Music-Conditioned Pluralistic Dan...

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

作者： Kim, Jinwoo Oh, Heeseok Kim, Seongjean Tong, Hoseok Lee, Sanghoon Yonsei Univ Sch Elect & Elect Engn Seoul South Korea Hansung Univ Dept Appl AI Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

When coming up with phrases of movement, choreographers all have their habits as they are used to their skilled dance genres. Therefore, they tend to return certain patterns of the dance genres that they are familiar with. What if artificial intelligence could be used to help choreographers blend dance genres by suggesting various dances, and one that matches their choreographic style? Numerous task-specific variants of autoregressive networks have been developed for dance generation. Yet, a serious limitation remains that all existing algorithms can return repeated patterns for a given initial pose sequence, which may be inferior. To mitigate this issue, we propose MNET, a novel and scalable approach that can perform music-conditioned pluralistic dance generation synthesized by multiple dance genres using only a single model. Here, we learn a dance-genre aware latent representation by training a conditional generative adversarial network leveraging Transformer architecture. We conduct extensive experiments on AIST++ along with user studies. Compared to the state-of-the-art methods, our method synthesizes plausible and diverse outputs according to multiple dance genres as well as generates outperforming dance sequences qualitatively and quantitatively.

关键词： Training Measurement Humanities computer vision Computational modeling computer architecture Transformers

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Weakly Supervised Dense Video Captioning 30

Weakly Supervised Dense Video Captioning

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Shen, Zhiqiang Li, Jianguo Su, Zhou Li, Minjun Chen, Yurong Jiang, Yu-Gang Xue, Xiangyang Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Intel Labs China Beijing Peoples R China

ISBN: (纸本)9781538604571

This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences. The proposed method is trained without explicit annotation of fine-grained sentence to video regionsequence correspondence, but is only based on weak videolevel sentence annotations. It differs from existing video captioning systems in three technical aspects. First, we propose lexical fully convolutional neural networks (LexicalFCN) with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels. Second, we introduce a novel submodular maximization scheme to generate multiple informative and diverse regionsequences based on the Lexical-FCN outputs. A winnertakes- all scheme is adopted to weakly associate sentences to region-sequences in the training phase. Third, a sequenceto- sequence learning based language model is trained with the weakly supervised information obtained through the association process. We show that the proposed method can not only produce informative and diverse dense captions, but also outperform state-of-the-art single video captioning methods by a large margin.

关键词： Visualization Training Motion segmentation Vocabulary computer vision Neural networks captions Motion segmentation images computer vision Neural network Visualization Vocabulary Functional training Videotapes video clips Training modelling languages

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Self Texture Transfer Networks for Low Bitrate Image Compression

Self Texture Transfer Networks for Low Bitrate Image Compres...

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

作者： Iwai, Shoma Miyazaki, Tomo Sugaya, Yoshihiro Omachi, Shinichiro Tohoku Univ Grad Sch Engn Dept Commun Sendai Miyagi Japan

ISBN: (纸本)9781665448994

Lossy image compression causes a loss of texture, especially at low bitrate. To mitigate this problem, we propose a novel image compression method that utilizes a reference-based image super-resolution model. We use two image compression models and a self texture transfer model. The image compression models encode and decode a whole input image and selected reference patches. The reference patches are small but compressed with high quality. The self texture transfer model transfers the texture of reference patches into similar regions in the compressed image. The experimental results show that our method can reconstruct accurate texture by transferring the texture of reference patches.

关键词： computer vision Image coding conferences Bit rate Superresolution pattern recognition Image reconstruction

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OPTIMAL ESTIMATION OF 3D STRUCTURES USING VISUAL SERVOING

OPTIMAL ESTIMATION OF 3D STRUCTURES USING VISUAL SERVOING

引用

1994 ieee computer-Society conference on computer vision and pattern recognition

作者： CHAUMETTE, F BOUKIR, S BOUTHEMY, P JUVIN, D IRISA INRIA RENNESF-35042 RENNESFRANCE

ISBN: (纸本)0818658258

This paper deals with the recovery of 3D information using a single mobile camera in the context of active vision. We propose a general revisited formulation of the structure-from-motion issue, and we determine adequate camera configurations and motions which lead to a robust and accurate estimation of the 3D structure parameters. We apply the visual servoing approach to perform these camera motions. Real-time experiments dealing with the 3D structure estimation of points and cylinders are reported, and demonstrate that this active vision strategy can very significantly improve the estimation accuracy.

关键词： computer vision

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Human Action recognition with Extremities as Semantic Posture Representation

Human Action Recognition with Extremities as Semantic Postur...

引用

ieee-computer-Society conference on computer vision and pattern recognition Workshops

作者： Yu, Elden Aggarwal, J. K. Univ Texas Austin Comp & Vis Res Ctr Austin TX 78712 USA

ISBN: (纸本)9781424439942

In this paper, we present at? approach for human action recognition with extremities as a compact semantic posture representation. First, we develop a variable star skeleton representation (VSS) in order to accurately,find human extremities front contours. Earlier, Fujiyoshi and Lipton [7] proposed an image skeletonization technique with the center of mass as a single star for rapid motion analysis. Yu and Aggarwal [18] used the highest contour point as the second star in their application for fence climbing detection. We implement VSS and earlier algorithms from [7, 18], and compare their performance over a set of 1000 frames from 50 sequences of persons climbing fences to analyze the characteristic of each representation. Our results show that VSS performs the best. Second, we build feature vectors out of detected extremities for Hidden Markov Model (HMM) based human action recognition. On the data set of human climbing fences, we achieved excellent classification accuracy. On the publicly available Blank et al. [3] data set, our approach showed that using only extremities is sufficient to obtain comparable classification accuracy against other state-of-the-art performance. The advantage of our approach lies in the less time complexity with comparable classification accuracy.

关键词： Fences

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Fast Solvers for Minimal Radial Distortion Relative Pose Problems

Fast Solvers for Minimal Radial Distortion Relative Pose Pro...

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

作者： Oskarsson, Magnus Lund Univ Ctr Math Sci Lund Sweden

ISBN: (纸本)9781665448994

In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can then apply standard techniques based on the action matrix and Sturm sequences to construct our solvers. This enables efficient solvers for a large class of relative pose problems with radial distortion, using a common framework. We evaluate a number of these solvers for robust two-view inlier and epipolar geometry estimation, used as minimal solvers in RANSAC.

关键词： Geometry computer vision conferences Estimation Tools Distortion Cameras

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