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

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Stochastic Variational Inference with Gradient Linearization 31

Stochastic Variational Inference with Gradient Linearization

引用

31st IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ploetz, Tobias Wannenwetsch, Anne S. Roth, Stefan Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany

ISBN: (纸本)9781538664209

Variational inference has experienced a recent surge in popularity owing to stochastic approaches, which have yielded practical tools for a wide range of model classes. A key benefit is that stochastic variational inference obviates the tedious process of deriving analytical expressions for closed-form variable updates. Instead, one simply needs to derive the gradient of the log-posterior, which is often much easier. Yet for certain model classes, the log-posterior itself is difficult to optimize using standard gradient techniques. One such example are random field models, where optimization based on gradient linearization has proven popular, since it speeds up convergence significantly and can avoid poor local optima. In this paper we propose stochastic variational inference with gradient linearization (SVIGL). It is similarly convenient as standard stochastic variational inference - all that is required is a local linearization of the energy gradient. Its benefit over stochastic variational inference with conventional gradient methods is a clear improvement in convergence speed, while yielding comparable or even better variational approximations in terms of KL divergence. We demonstrate the benefits of SVIGL in three applications: Optical flow estimation, Poisson-Gaussian denoising, and 3D surface reconstruction.

关键词： Stochastic processes Mathematical model Uncertainty Optimization Computational modeling Standards computer vision

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Stereo without depth search and metric calibration

Stereo without depth search and metric calibration

引用

IEEE conference on computer vision and pattern recognition (CVPR 2000)

作者： Hattori, H Maki, A Toshiba Co Ltd Ctr Res & Dev Kawasaki Kanagawa 2128582 Japan

ISBN: (纸本)0769506623

We propose a new stereo method for 2D navigation in a dynamic environment such as roads without depth search and metric camera calibration. Conventionally there is an effective stereo method based on the constraint that an observer moves an the ground plane, namely the GP constraint. Although the GP constraint is often violated in an outdoor environment due to the movement of the observer;i.e., camera vibrations and inclination, it can be updated using some features on the ground plate only if the stereo cameras are weakly calibrated. However;the conventional stereo method has several drawbacks. First;it is rather difficult to solve for the general epipolar geometry summarized in the fundamental matrix. Second, although the updated GP constraint often becomes imperfect. it lacks of an effective contrivance for. the noise reduction. Third. there is no measure to assess the danger of detected obstacles. To solve these problems: we develop a domain-specific stereo method which utilizes various attributes of roads. We introduce the ''pseudo-projective camera model" that provides a good approximation to the projective camera in road scenes and accordingly define the linear epipolar geometry for a pair of pseudo-projective cameras. Furthermore, we show that extraction of two parallel lines lying on the road is effective to update the GP constraint, overcome the noise issue and even to estimate the degree of danger. Through experiments we demonstrate that our method is efficient and applicable to a variety of outdoor scenes.

关键词： Stereo vision

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TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers

TransMVSNet: Global Context-aware Multi-view Stereo Network ...

引用

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

作者： Ding, Yikang Yuan, Wentao Zhu, Qingtian Zhang, Haotian Liu, Xiangyue Wang, Yuanjiang Liu, Xiao Megvii Res Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Peking Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we present TransMVSNet, based on our exploration of feature matching in multi-view stereo (MVS). We analogize MVS back to its nature of a feature matching task and therefore propose a powerful Feature Matching Transformer (FMT) to leverage intra- (self-) and inter(cross-) attention to aggregate long-range context information within and across images. To facilitate a better adaptation of the FMT, we leverage an Adaptive Receptive Field (ARF) module to ensure a smooth transit in scopes of features and bridge different stages with a feature pathway to pass transformed features and gradients across different scales. In addition, we apply pair-wise feature correlation to measure similarity between features, and adopt ambiguity-reducing focal loss to strengthen the supervision. To the best of our knowledge, TransMVSNet is the first attempt to leverage Transformer into the task of MVS. As a result, our method achieves state-of-the-art performance on DTU dataset, Tanks and Temples benchmark, and BlendedMVS dataset. Code is available at https://***/MegviiRobot/TransMVSNet.

关键词： computer vision Three-dimensional displays Correlation Aggregates Benchmark testing Transformers Loss measurement

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A Proposal-based Paradigm for Self-supervised Sound Source Localization in Videos

A Proposal-based Paradigm for Self-supervised Sound Source L...

引用

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

作者： Xuan, Hanyu Wu, Zhiliang Yang, Jian Yan, Yan Alameda-Pineda, Xavier Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China IIT Dept Comp Sci Chicago IL 60616 USA Univ Grenoble Alpes LJK Grenoble INP INRIACNRS F-38000 Grenoble France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Humans can easily recognize where and how the sound is produced via watching a scene and listening to corresponding audio cues. To achieve such cross-modal perception on machines, existing methods only use the maps generated by interpolation operations to localize the sound *** semantic object-level localization is more attractive for potential practical applications, we argue that these existing map-based approaches only provide a coarse-grained and indirect description of the sound source. In this paper,we advocate a novel proposal-based paradigm that can directly perform semantic object-level localization, without any manual annotations. We incorporate the global response map as an unsupervised spatial constraint to weight the proposals according to how well they cover the estimated global shape of the sound source. As a result, our proposal-based sound source localization can be cast into a simpler Multiple Instance Learning (MIL) problem by filtering those instances corresponding to large sound-unrelated regions. Our method achieves state-of-the-art (SOTA) performance when compared to several baselines on multiple datasets.

关键词： Location awareness Interpolation Annotations Shape Semantics Manuals pattern recognition

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Balanced MSE for Imbalanced Visual Regression

Balanced MSE for Imbalanced Visual Regression

引用

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

作者： Ren, Jiawei Zhang, Mingyuan Yu, Cunjun Liu, Ziwei Nanyang Technol Univ S Lab Singapore Singapore Natl Univ Singapore Sch Comp Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Data imbalance exists ubiquitously in real-world visual regressions, e.g., age estimation and pose estimation, hurting the model's generalizability and fairness. Thus, imbalanced regression gains increasing research attention recently. Compared to imbalanced classification, imbalanced regression focuses on continuous labels, which can be boundless and high-dimensional and hence more challenging. In this work, we identify that the widely used Mean Square Error (MSE) loss function can be ineffective in imbalanced regression. We revisit MSE from a statistical view and propose a novel loss function, Balanced MSE, to accommodate the imbalanced training label distribution. We further design multiple implementations of Balanced MSE to tackle different real-world scenarios, particularly including the one that requires no prior knowledge about the training label distribution. Moreover, to the best of our knowledge, Balanced MSE is the first general solution to high-dimensional imbalanced regression in modern context. Extensive experiments on both synthetic and three real-world benchmarks demonstrate the effectiveness of Balanced MSE. Code and models are available at ***/jiawei-ren/BalancedMSE.

关键词： Training Visualization computer vision Codes Computational modeling Pose estimation Mean square error methods

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Modeling Multi-Label Action Dependencies for Temporal Action Localization

Modeling Multi-Label Action Dependencies for Temporal Action...

引用

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

作者： Tirupattur, Praveen Duarte, Kevin Rawat, Yogesh S. Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9781665445092

Real-world videos contain many complex actions with inherent relationships between action classes. In this work, we propose an attention-based architecture that models these action relationships for the task of temporal action localization in untrimmed videos. As opposed to previous works that leverage video-level co-occurrence of actions, we distinguish the relationships between actions that occur at the same time-step and actions that occur at different time-steps (i.e. those which precede or follow each other). We define these distinct relationships as action dependencies. We propose to improve action localization performance by modeling these action dependencies in a novel attention-based Multi-Label Action Dependency (MLAD) layer. The MLAD layer consists of two branches: a Co-occurrence Dependency Branch and a Temporal Dependency Branch to model co-occurrence action dependencies and temporal action dependencies, respectively. We observe that existing metrics used for multi-label classification do not explicitly measure how well action dependencies are modeled, therefore, we propose novel metrics that consider both co-occurrence and temporal dependencies between action classes. Through empirical evaluation and extensive analysis, we show improved performance over state-of-the-art methods on multi-label action localization benchmarks (MultiTHUMOS and Charades) in terms of fmAP and our proposed metric.

关键词： Measurement Location awareness computer vision Codes Network architecture Benchmark testing pattern recognition

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Embedding Transfer with Label Relaxation for Improved Metric Learning

Embedding Transfer with Label Relaxation for Improved Metric...

引用

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

作者： Kim, Sungyeon Kim, Dongwon Cho, Minsu Kwak, Suha POSTECH Dept CSE Pohang South Korea POSTECH Grad Sch AI Pohang South Korea

ISBN: (纸本)9781665445092

This paper presents a novel method for embedding transfer, a task of transferring knowledge of a learned embedding model to another. Our method exploits pairwise similarities between samples in the source embedding space as the knowledge, and transfers them through a loss used for learning target embedding models. To this end, we design a new loss called relaxed contrastive loss, which employs the pairwise similarities as relaxed labels for intersample relations. Our loss provides a rich supervisory signal beyond class equivalence, enables more important pairs to contribute more to training, and imposes no restriction on manifolds of target embedding spaces. Experiments on metric learning benchmarks demonstrate that our method largely improves performance, or reduces sizes and output dimensions of target models effectively. We further show that it can be also used to enhance quality of self-supervised representation and performance of classification models. In all the experiments, our method clearly outperforms existing embedding transfer techniques.

关键词： Measurement Training Manifolds computer vision Computational modeling Focusing Benchmark testing

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Unsupervised Image Captioning 32

Unsupervised Image Captioning

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Feng, Yang Ma, Lin Liu, Wei Luo, Jiebo Tencent AI Lab Bellevue WA 98004 USA Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781728132938

Deep neural networks have achieved great successes on the image captioning task. However, most of the existing models depend heavily on paired image-sentence datasets, which are very expensive to acquire. In this paper, we make the first attempt to train an image captioning model in an unsupervised manner. Instead of relying on manually labeled image-sentence pairs, our proposed model merely requires an image set, a sentence corpus, and an existing visual concept detector. The sentence corpus is used to teach the captioning model how to generate plausible sentences. Meanwhile, the knowledge in the visual concept detector is distilled into the captioning model to guide the model to recognize the visual concepts in an image. In order to further encourage the generated captions to be semantically consistent with the image, the image and caption are projected into a common latent space so that they can reconstruct each other. Given that the existing sentence corpora are mainly designed for linguistic research and are thus with little reference to image contents, we crawl a large-scale image description corpus of two million natural sentences to facilitate the unsupervised image captioning scenario. Experimental results show that our proposed model is able to produce quite promising results without any caption annotations.

关键词： vision + Language

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StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval

StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retriev...

引用

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

作者： Sain, Aneeshan Bhunia, Ayan Kumar Yang, Yongxin Xiang, Tao Song, Yi-Zhe Univ Surrey CVSSP SketchX Guildford Surrey England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England

ISBN: (纸本)9781665445092

Sketch-based image retrieval (SBIR) is a cross-modal matching problem which is typically solved by learning a joint embedding space where the semantic content shared between photo and sketch modalities are preserved. However, a fundamental challenge in SBIR has been largely ignored so far, that is, sketches are drawn by humans and considerable style variations exist amongst different users. An effective SBIR model needs to explicitly account for this style diversity, crucially, to generalise to unseen user styles. To this end, a novel style-agnostic SBIR model is proposed. Different from existing models, a cross-modal variational autoencoder (VAE) is employed to explicitly disentangle each sketch into a semantic content part shared with the corresponding photo, and a style part unique to the sketcher. Importantly, to make our model dynamically adaptable to any unseen user styles, we propose to metatrain our cross-modal VAE by adding two style-adaptive components: a set of feature transformation layers to its encoder and a regulariser to the disentangled semantic content latent code. With this meta-learning framework, our model can not only disentangle the cross-modal shared semantic content for SBIR, but can adapt the disentanglement to any unseen user style as well, making the SBIR model truly style-agnostic. Extensive experiments show that our style-agnostic model yields state-of-the-art performance for both category-level and instance-level SBIR.

关键词： Adaptation models computer vision Codes Computational modeling Semantics Image retrieval pattern recognition

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Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling

Open-Vocabulary Instance Segmentation via Robust Cross-Modal...

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

作者： Dat Huynh Kuen, Jason Lin, Zhe Gu, Jiuxiang Elhamifar, Ehsan Northeastern Univ Boston MA 02115 USA Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Open-vocabulary instance segmentation aims at segmenting novel classes without mask annotations. It is an important step toward reducing laborious human supervision. Most existing works first pretrain a model on captioned images covering many novel classes and then finetune it on limited base classes with mask annotations. However, the high-level textual information learned from caption pre-training alone cannot effectively encode the details required for pixel-wise segmentation. To address this, we propose a cross-modal pseudo-labeling framework, which generates training pseudo masks by aligning word semantics in captions with visual features of object masks in images. Thus, our framework is capable of labeling novel classes in captions via their word semantics to self-train a student model. To account for noises in pseudo masks, we design a robust student model that selectively distills mask knowledge by estimating the mask noise levels, hence mitigating the adverse impact of noisy pseudo masks. By extensive experiments, we show the effectiveness of our framework, where we significantly improve mAP score by 4.5% on MS-COCO and 5.1% on the large-scale Open Images & Conceptual Captions datasets compared to the state-of-the-art.(1)

关键词： Training Image segmentation Visualization computer vision Annotations Semantics pattern recognition

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