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检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021"

共 11423 条记录，以下是251-260 订阅

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Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis

Exploiting Edge-Oriented Reasoning for 3D Point-based Scene ...

引用

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

作者： Zhang, Chaoyi Yu, Jianhui Song, Yang Cai, Weidong Univ Sydney Sydney NSW Australia Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9781665445092

Scene understanding is a critical problem in computer vision. In this paper, we propose a 3D point-based scene graph generation (SGG(point)) framework to effectively bridge perception and reasoning to achieve scene understanding via three sequential stages, namely scene graph construction, reasoning, and inference. Within the reasoning stage, an EDGE-oriented Graph Convolutional Network (EdgeGCN) is created to exploit multi-dimensional edge features for explicit relationship modeling, together with the exploration of two associated twinning interaction mechanisms between nodes and edges for the independent evolution of scene graph representations. Overall, our integrated SGG(point) framework is established to seek and infer scene structures of interest from both real-world and synthetic 3D point-based scenes. Our experimental results show promising edge-oriented reasoning effects on scene graph generation studies. We also demonstrate our method advantage on several traditional graph representation learning benchmark datasets, including the node-wise classification on citation networks and whole-graph recognition problems for molecular analysis.

关键词： Bridges computer vision Three-dimensional displays Benchmark testing Cognition pattern recognition

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Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision

Unsupervised Discovery of the Long-Tail in Instance Segmenta...

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

作者： Weng, Zhenzhen Ogut, Mehmet Giray Limonchik, Shai Yeung, Serena Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

Instance segmentation is an active topic in computer vision that is usually solved by using supervised learning approaches over very large datasets composed of object level masks. Obtaining such a dataset for any new domain can be very expensive and time-consuming. In addition, models trained on certain annotated categories do not generalize well to unseen objects. The goal of this paper is to propose a method that can perform unsupervised discovery of long-tail categories in instance segmentation, through learning instance embeddings of masked regions. Leveraging rich relationship and hierarchical structure between objects in the images, we propose self-supervised losses for learning mask embeddings. Trained on COCO [34] dataset without additional annotations of the long-tail objects, our model is able to discover novel and more fine-grained objects than the common categories in COCO. We show that the model achieves competitive quantitative results on LVIS [17] as compared to the supervised and partially supervised methods.

关键词： computer vision Image segmentation Annotations Computational modeling Supervised learning pattern recognition

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Scene Essence

Scene Essence

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

作者： Qiu, Jiayan Yang, Yiding Wang, Xinchao Tao, Dacheng Univ Sydney Sydney NSW Australia Stevens Inst Technol Hoboken NJ 07030 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

What scene elements, if any, are indispensable for recognizing a scene? We strive to answer this question through the lens of an exotic learning scheme. Our goal is to identify a collection of such pivotal elements, which we term as Scene Essence, to be those that would alter scene recognition if taken out from the scene. To this end, we devise a novel approach that learns to partition the scene objects into two groups, essential ones and minor ones, under the supervision that if only the essential ones are kept while the minor ones are erased in the input image, a scene recognizer would preserve its original prediction. Specifically, we introduce a learnable graph neural network (GNN) for labelling scene objects, based on which the minor ones are wiped off by an off-the-shelf image inpainter. The features of the inpainted image derived in this way, together with those learned from the GNN with the minor-object nodes pruned, are expected to fool the scene discriminator. Both subjective and objective evaluations on Places365, SUN397, and MIT67 datasets demonstrate that, the learned Scene Essence yields a visually plausible image that convincingly retains the original scene category.

关键词： computer vision Image recognition Graph neural networks pattern recognition Labeling Lenses

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UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering

UnsupervisedR&R: Unsupervised Point Cloud Registration via D...

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

作者： El Banani, Mohamed Gao, Luya Johnson, Justin Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781665445092

Aligning partial views of a scene into a single whole is essential to understanding one's environment and is a key component of numerous robotics tasks such as SLAM and SfM. Recent approaches have proposed end-to-end systems that can outperform traditional methods by leveraging pose supervision. However, with the rising prevalence of cameras with depth sensors, we can expect a new stream of raw RGB-D data without the annotations needed for supervision. We propose UnsupervisedR&R: an end-to-end unsupervised approach to learning point cloud registration from raw RGB-D video. The key idea is to leverage differentiable alignment and rendering to enforce photometric and geometric consistency between frames. We evaluate our approach on indoor scene datasets and find that we outperform existing traditional approaches with classical and learned descriptors while being competitive with supervised geometric point cloud registration approaches.

关键词： Training Simultaneous localization and mapping Robot vision systems Feature extraction Rendering (computer graphics) Cameras Sensors

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Adversarial Robustness Across Representation Spaces

Adversarial Robustness Across Representation Spaces

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

作者： Awasthi, Pranjal Yu, George Ferng, Chun-Sung Tomkins, Andrew Juan, Da-Cheng Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Adversarial robustness corresponds to the susceptibility of deep neural networks to imperceptible perturbations made at test time. In the context of image tasks, many algorithms have been proposed to make neural networks robust to adversarial perturbations made to the input pixels. These perturbations are typically measured in an 4, norm. However, robustness often holds only for the specific attack used for training. In this work we extend the above setting to consider the problem of training of deep neural networks that can be made simultaneously robust to perturbations applied in multiple natural representations spaces. For the case of image data, examples include the standard pixel representation as well as the representation in the discrete cosine transform (DCT) basis. We design a theoretically sound algorithm with formal guarantees for the above problem. Furthermore, our guarantees also hold when the goal is to require robustness with respect to multiple 4, norm based attacks. We then derive an efficient practical implementation and demonstrate the effectiveness of our approach on standard datasets for image classification.(1)

关键词： Training Deep learning computer vision Perturbation methods Neural networks Robustness pattern recognition

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Local Texture Estimator for Implicit Representation Function

Local Texture Estimator for Implicit Representation Function

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

作者： Lee, Jaewon Jin, Kyong Hwan Daegu Gyeongbuk Inst Sci & Technol DGIST Daegu South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent works with an implicit neural function shed light on representing images in arbitrary resolution. However, a standalone multi-layer perceptron shows limited performance in learning high frequency components. In this paper, we propose a Local Texture Estimator (LTE), a dominant frequency estimator for natural images, enabling an implicit function to capture fine details while reconstructing images in a continuous manner. When jointly trained with a deep super-resolution (SR) architecture, LTE is capable of characterizing image textures in 2D Fourier space. We show that an LTE-based neural function achieves favorable performance against existing deep SR methods within an arbitrary-scale factor. Furthermore, we demonstrate that our implementation takes the shortest running time compared to previous works.

关键词： Visualization Image texture computer vision Superresolution computer architecture Feature extraction Frequency estimation

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Fostering Generalization in Single-view 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors

Fostering Generalization in Single-view 3D Reconstruction by...

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

作者： Bechtold, Jan Tatarchenko, Maxim Fischer, Volker Brox, Thomas Bosch Ctr Artificial Intelligence Renningen Baden Wurttembe Germany Univ Freiburg Freiburg Germany

ISBN: (纸本)9781665445092

Single-view 3D object reconstruction has seen much progress, yet methods still struggle generalizing to novel shapes unseen during training. Common approaches predominantly rely on learned global shape priors and, hence, disregard detailed local observations. In this work, we address this issue by learning a hierarchy of priors at different levels of locality from ground truth input depth maps. We argue that exploiting local priors allows our method to efficiently use input observations, thus improving generalization in visible areas of novel shapes. At the same time, the combination of local and global priors enables meaningful hallucination of unobserved parts resulting in consistent 3D shapes. We show that the hierarchical approach generalizes much better than the global approach. It generalizes not only between different instances of a class but also across classes and to unseen arrangements of objects.

关键词： Training computer vision Three-dimensional displays Shape Training data computer architecture Network architecture

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TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text

TextOCR: Towards large-scale end-to-end reasoning for arbitr...

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

作者： Singh, Amanpreet Peng, Guan Toh, Mandy Huang, Jing Galuba, Wojciech Hassner, Tal Facebook AI Res Menlo Pk CA 94025 USA

ISBN: (纸本)9781665445092

A crucial component for the scene text based reasoning required for TextVQA and TextCaps datasets involve detecting and recognizing text present in the images using an optical character recognition (OCR) system. The current systems are crippled by the unavailability of ground truth text annotations for these datasets as well as lack of scene text detection and recognition datasets on real images disallowing the progress in the field of OCR and evaluation of scene text based reasoning in isolation from OCR systems. In this work, we propose TextOCR, an arbitrary-shaped scene text detection and recognition with 900k annotated words collected on real images from TextVQA dataset. We show that current state-of-the-art text-recognition (OCR) models fail to perform well on TextOCR and that training on TextOCR helps achieve state-of-the-art performance on multiple other OCR datasets as well. We use a TextOCR trained OCR model to create PixelM4C model which can do scene text based reasoning on an image in an end-to-end fashion, allowing us to revisit several design choices to achieve new state-of-the-art performance on TextVQA dataset.

关键词： Training computer vision Image recognition Text recognition Optical feedback Optical imaging Cognition

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Learning Deep Latent Variable Models by Short-Run MCMC Inference with Optimal Transport Correction

Learning Deep Latent Variable Models by Short-Run MCMC Infer...

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

作者： An, Dongsheng Xie, Jianwen Li, Ping Baidu Res Cognit Comp Lab 10900 NE 8th St Bellevue WA 98004 USA

ISBN: (纸本)9781665445092

Learning latent variable models with deep top-down architectures typically requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference step typically relies on either time-consuming long-run Markov chain Monte Carlo (MCMC) sampling or a separate inference model for variational learning. In this paper, we propose to use a short-run MCMC, such as a short-run Langevin dynamics, as an approximate flow-based inference engine. The bias existing in the output distribution of the non-convergent short-run Langevin dynamics is corrected by the optimal transport (OT), which aims at transforming the biased distribution produced by the finite-step MCMC to the prior distribution with a minimum transport cost. Our experiments not only verify the effectiveness of the OT correction for the short-run MCMC, but also demonstrate that the latent variable model trained by the proposed strategy performs better than the variational auto-encoder (VAE) in terms of image reconstruction/generation and anomaly detection.

关键词： Training Monte Carlo methods Costs Image synthesis Markov processes pattern recognition Task analysis

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Differentiable Diffusion for Dense Depth Estimation from Multi-view Images

Differentiable Diffusion for Dense Depth Estimation from Mul...

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

作者： Khan, Numair Kim, Min H. Tompkin, James Brown Univ Providence RI 02912 USA Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

We present a method to estimate dense depth by optimizing a sparse set of points such that their diffusion into a depth map minimizes a multi-view repmjection error from RGB supervision. We optimize point positions, depths, and weights with respect to the loss by differential splatting that models points as Gaussians with analytic transmittance. Further;we develop an efficient optimization routine that can simultaneously optimize the 50k+ points required for complex scene reconstruction. We validate our routine using ground truth data and show high reconstruction quality. Then, we apply this to light field and wider baseline images via self supervision, and show improvements in both average and outlier error for depth maps diffused from inaccurate sparse points. Finally, we compare qualitative and quantitative results to image processing and deep learning methods.

关键词： Deep learning computer vision Analytical models Estimation Rendering (computer graphics) Propagation losses Light fields

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