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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是4761-4770 订阅

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GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation 31

GeoNet: Geometric Neural Network for Joint Depth and Surface...

引用

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

作者： Qi, Xiaojuan Liao, Renjie Liu, Zhengzhe Urtasun, Raquel Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Univ Toronto Toronto ON Canada Uber Adv Technol Grp Pittsburgh PA 15201 USA Tencent YouTu Lab Shenzhen Peoples R China

ISBN: (纸本)9781538664209

In this paper, we propose Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image. Building on top of two-stream CNNs, our GeoNet incorporates geometric relation between depth and surface normal via the new depth-to-normal and normal to -depth networks. Depth-to-normal network exploits the least square solution of surface normal from depth and improves its quality with a residual module. Normal-to-depth network, contrarily, refines the depth map based on the constraints from the surface normal through a kernel regression module, which has no parameter to learn. These two networks enforce the underlying model to efficiently predict depth and surface normal for high consistency and corresponding accuracy. Our experiments on NYU v2 dataset verify that our GeoNet is able to predict geometrically consistent depth and normal maps. It achieves top performance on surface normal estimation and is on par with state-of-theart depth estimation methods.

关键词： Neural networks Estimation Three-dimensional displays computer architecture Rough surfaces Surface roughness Kernel

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Bidirectional Retrieval Made Simple 31

Bidirectional Retrieval Made Simple

引用

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

作者： Wehrmann, Jonatas Barros, Rodrigo C. Pontiffcia Univ Catolica Rio Grande do Sul Sch Technol Porto Alegre RS Brazil

ISBN: (纸本)9781538664209

This paper provides a very simple yet effective character-level architecture for learning bidirectional retrieval models. Aligning multimodal content is particularly challenging considering the difficulty in finding semantic correspondence between images and descriptions. We introduce an efficient character-level inception module, designed to learn textual semantic embeddings by convolving raw characters in distinct granularity levels. Our approach is capable of explicitly encoding hierarchical information from distinct base-level representations (e.g., characters, words, and sentences) into a shared multimodal space, where it maps the semantic correspondence between images and descriptions via a contrastive pairwise loss function that minimizes order-violations. Models generated by our approach are far more robust to input noise than state-of-the-art strategies based on word-embeddings. Despite being conceptually much simpler and requiring fewer parameters, our models outperform the state-of-the-art approaches by 4.8% in the task of description retrieval and 2.7% (absolute R@ 1 values) in the task of image retrieval in the popular MS COCO retrieval dataset. We also show that our models present solid performance for text classification, specially in multilingual and noisy domains.

关键词： computer architecture Semantics Training Feature extraction Task analysis Complexity theory Image coding

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Progressive Modality Reinforcement for Human Multimodal Emotion recognition from Unaligned Multimodal Sequences

Progressive Modality Reinforcement for Human Multimodal Emot...

引用

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

作者： Lv, Fengmao Chen, Xiang Huang, Yanyong Duan, Lixin Lin, Guosheng Southwest Jiaotong Univ Chengdu Peoples R China Southwestern Univ Finance & Econ Ctr Stat Res Chengdu Peoples R China Tencent Platform & Content Grp Shenzhen Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665445092

Human multimodal emotion recognition involves time-series data of different modalities, such as natural language, visual motions, and acoustic behaviors. Due to the variable sampling rates for sequences from different modalities, the collected multimodal streams are usually unaligned. The asynchrony across modalities increases the difficulty on conducting efficient multimodal fusion. Hence, this work mainly focuses on multimodal fusion from unaligned multimodal sequences. To this end, we propose the Progressive Modality Reinforcement (PMR) approach based on the recent advances of crossmodal transformer. Our approach introduces a message hub to exchange information with each modality. The message hub sends common messages to each modality and reinforces their features via crossmodal attention. In turn, it also collects the reinforced features from each modality and uses them to generate a reinforced common message. By repeating the cycle process, the common message and the modalities' features can progressively complement each other. Finally, the reinforced features are used to make predictions for human emotion. Comprehensive experiments on different human multimodal emotion recognition benchmarks clearly demonstrate the superiority of our approach.

关键词： Emotion recognition Visualization computer vision Correlation Natural languages Benchmark testing Transformers

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When fisher meets Fukunaga-Koontz: A new look at linear discriminants

When fisher meets Fukunaga-Koontz: A new look at linear disc...

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Sheng, Zhang Sim, Terence School of Computing National University of Singapore 3 Science Drive 2 Singapore 117543 Singapore

ISBN: (纸本)0769525970

The Fisher Linear Discriminant (FLD) is commonly used in pattern recognition. It finds a linear subspace that maximally separates class patterns according to Fisher's Criterion. Several methods of computing the FLD have been proposed in the literature, most of which require the calculation of the so-called scatter matrices. In this paper, we bring a fresh perspective to FLD via the Fukunaga-Koontz Transform (FKT). We do this by decomposing the whole data space into four subspaces, and show where Fisher's Criterion is maximally satisfied. We prove the relationship between FLD and FKT analytically, and propose a method of computing the most discriminative subspace. This method is based on the QR decomposition, which works even when the scatter matrices are singular, or too large to be formed. Our method is general and may be applied to different pattern recognition problems. We validate our method by experimenting on synthetic and real data. © 2006 ieee.

关键词： pattern recognition

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Reconstructing 3D independent motions using non-accidentalness

Reconstructing 3D independent motions using non-accidentalne...

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Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition, CVPR 2004

作者： Ozden, K.E. Cornelis, K. Van Eycken, L. Van Gool, L. ESAT/PSI K.U. Leuven Leuven Belgium Computer Vision Lab. ETH Zurich Switzerland

Reconstructing 3D scenes with independently moving objects from uncalibrated monocular image sequences still poses serious challenges. One important problem is to find the relative scales between these different reconstructed objects. The perspective reconstruction of a single object can only be known up to a certain scale which results in a one-parameter family of relative scales and trajectories per moving object. The paper formulates this ambiguity and proposes solutions in the vein of "non-accidentalness". Two instantiations of its use are analyzed: planar motion and the 'heading constraint'.

关键词： Image reconstruction

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BoxeR: Box-Attention for 2D and 3D Transformers

BoxeR: Box-Attention for 2D and 3D Transformers

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

作者： Nguyen, Duy-Kien Ju, Jihong Booij, Olaf Oswald, Martin R. Snoek, Cees G. M. Univ Amsterdam Atlas Lab Amsterdam Netherlands TomTom Atlas Lab Amsterdam Netherlands

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we propose a simple attention mechanism, we call Box-Attention. It enables spatial interaction between grid features, as sampled from boxes of interest, and improves the learning capability of transformers for several vision tasks. Specifically, we present BoxeR, short for Box Transformer, which attends to a set of boxes by predicting their transformation from a reference window on an input feature map. The BoxeR computes attention weights on these boxes by considering its grid structure. Notably, BoxeR-2D naturally reasons about box information within its attention module, making it suitable for end-to-end instance detection and segmentation tasks. By learning invariance to rotation in the box-attention module, BoxeR-3D is capable of generating discriminative information from a bird's-eye view plane for 3D end-to-end object detection. Our experiments demonstrate that the proposed BoxeR-2D achieves state-of-the-art results on COCO detection and instance segmentation. Besides, BoxeR-3D improves over the end-to-end 3D object detection baseline and already obtains a compelling performance for the vehicle category of Waymo Open, without any class-specific optimization. Code is available at https://***/kienduynguyen/BoxeR.

关键词： computer vision Three-dimensional displays Codes Object detection Transformers pattern recognition Task analysis

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Quadratic filter and feature detection

Quadratic filter and feature detection

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Proceedings of the 1993 ieee computer society conference on computer vision and pattern recognition

作者： Chou, Kae-Jy Schunck, Brian G. Univ of Michigan Ann Arbor MI United States

ISBN: (纸本)0818638826

The paper explores the quadratic filter for low-level vision applications. The quadratic filter is the simplest nonlinear time-invariant filter and corresponds to the second term in the Volterra expansion. We will elaborate concepts behind the quadratic filter and show that it can be derived from fundamental properties of regions and the principle of model competition. We present two properties of the filter: it has better spatial localization than a linear filter and it is insensitive to blurred boundaries.

关键词： computer vision

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Probabilistic Prompt Learning for Dense Prediction

Probabilistic Prompt Learning for Dense Prediction

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

作者： Kwon, Hyeongjun Song, Taeyong Jeong, Somi Kim, Jin Jang, Jinhyun Sohn, Kwanghoon Yonsei Univ Seoul South Korea Hyundai Motor Co R&D Div Seoul South Korea NAVER LABS Seongnam South Korea Korea Inst Sci & Technol KIST Seoul South Korea

ISBN: (纸本)9798350301298

Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models. However, this approach results in limited performance for dense prediction tasks that require handling more complex and diverse objects, since a single and deterministic description cannot sufficiently represent the entire image. In this paper, we present a novel probabilistic prompt learning to fully exploit the vision-language knowledge in dense prediction tasks. First, we introduce learnable class-agnostic attribute prompts to describe universal attributes across the object class. The attributes are combined with class information and visual-context knowledge to define the class-specific textual distribution. Text representations are sampled and used to guide the dense prediction task using the probabilistic pixel-text matching loss, enhancing the stability and generalization capability of the proposed method. Extensive experiments on different dense prediction tasks and ablation studies demonstrate the effectiveness of our proposed method.

关键词： and reasoning language vision

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Consistent Explanations by Contrastive Learning

Consistent Explanations by Contrastive Learning

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

作者： Pillai, Vipin Koohpayegani, Soroush Abbasi Ouligian, Ashley Fong, Dennis Pirsiavash, Hamed Univ Maryland Baltimore Cty Baltimore MD 21228 USA Northrop Grumman Falls Church VA USA Univ Calif Davis Davis CA 95616 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Post-hoc explanation methods, e.g., Grad-CAM, enable humans to inspect the spatial regions responsible for a particular network decision. However, it is shown that such explanations are not always consistent with human priors, such as consistency across image transformations. Given an interpretation algorithm, e.g., Grad-CAM, we introduce a novel training method to train the model to produce more consistent explanations. Since obtaining the ground truth for a desired model interpretation is not a well-defined task, we adopt ideas from contrastive self-supervised learning, and apply them to the interpretations of the model rather than its embeddings. We show that our method, Contrastive Grad-CAM Consistency (CGC), results in Grad-CAM interpretation heatmaps that are more consistent with human annotations while still achieving comparable classification accuracy. Moreover;our method acts as a regularizes and improves the accuracy on limited-data, fine-grained classification settings. In addition, because our method does not rely on annotations, it allows for the incorporation of unlabeled data into training, which enables better generalization of the model.

关键词： Training Heating systems Deep learning computer vision Annotations Neural networks Self-supervised learning

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3D structure estimation from monocular video clips

3D structure estimation from monocular video clips

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2010 ieee computer society conference on computer vision and pattern recognition - Workshops, CVPRW 2010

作者： Donate, Arturo Liu, Xiuwen Florida State University Department of Computer Science Tallahassee FL 32306 United States

ISBN: (纸本)9781424470297

This paper explores the idea of extracting three dimensional features from a previously recorded video, in an attempt to provide three dimensional information about a video clip in order to improve the performance of various video analysis tasks. Although video analysis is a very prevalent area of research, the use of 3D features is scarce in the literature due to the inherent difficulties associated with extracting accurate 3D representations of videos in cases where no previous knowledge of the scene or camera is known. In this paper, we present a framework that attempts to compute a dense three dimensional representation of a scene using only the available video sequence. Our proposed system exploits the motion of the camera in order to estimate the relative 3D positions of salient features located in the video frames. Additionally, we incorporate the use of appearance-based models to estimate their relative poses and fit a 3D human model into the reconstructed scenes. We test our method using various video clips obtained from online databases in order to show the feasibility of this approach. © 2010 ieee.

关键词： Three dimensional computer graphics

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