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

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Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation

Self-Supervised Arbitrary-Scale Point Clouds Upsampling via ...

引用

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

作者： Zhao, Wenbo Liu, Xianming Zhong, Zhiwei Jiang, Junjun Gao, Wei Li, Ge Ji, Xiangyang Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Point clouds upsampling is a challenging issue to generate dense and uniform point clouds from the given sparse input. Most existing methods either take the end-to-end supervised learning based manner, where large amounts of pairs of sparse input and dense ground-truth are exploited as supervision information;or treat up-scaling of different scale factors as independent tasks, and have to build multiple networks to handle upsampling with varying factors. In this paper, we propose a novel approach that achieves self-supervised and magnification flexible point clouds upsampling simultaneously. We formulate point clouds upsampling as the task of seeking nearest projection points on the implicit surface for seed points. To this end, we define two implicit neural functions to estimate projection direction and distance respectively, which can be trained by two pretext learning tasks. Experimental results demonstrate that our self-supervised learning based scheme achieves competitive or even better performance than supervised learning based state-of-the-art methods. The source code is publicly available at https://***/xnowbzhao/sapcu.

关键词： Point cloud compression Visualization computer vision Codes Supervised learning Self-supervised learning pattern recognition

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Hierarchical Lovasz Embeddings for Proposal-free Panoptic Segmentation

Hierarchical Lovasz Embeddings for Proposal-free Panoptic Se...

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

作者： Kerola, Tommi Li, Jie Kanehira, Atsushi Kudo, Yasunori Vallet, Alexis Gaidon, Adrien Preferred Networks Inc Tokyo Japan Toyota Res Inst TRI Los Altos CA USA

ISBN: (纸本)9781665445092

Panoptic segmentation brings together two separate tasks: instance and semantic segmentation. Although they are related, unifying them faces an apparent paradox: how to learn simultaneously instance-specific and category-specific (i.e. instance-agnostic) representations jointly. Hence, state-of-the-art panoptic segmentation methods use complex models with a distinct stream for each task. In contrast, we propose Hierarchical Lovasz Embeddings, per pixel feature vectors that simultaneously encode instance- and category-level discriminative information. We use a hierarchical Lovasz hinge loss to learn a low-dimensional embedding space structured into a unified semantic and instance hierarchy without requiring separate network branches or object proposals. Besides modeling instances precisely in a proposal-free manner, our Hierarchical Lovasz Embeddings generalize to categories by using a simple Nearest-Class-Mean classifier, including for non-instance "stuff" classes where instance segmentation methods are not applicable. Our simple model achieves state-of-the-art results compared to existing proposal-free panoptic segmentation methods on Cityscapes, COCO, and Mapillary Vistas. Furthermore, our model demonstrates temporal stability between video frames.

关键词： Semantics Fasteners Predictive models Ontologies Stability analysis pattern recognition Proposals

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Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction

Dynamic Neural Radiance Fields for Monocular 4D Facial Avata...

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

作者： Gafni, Guy Thies, Justus Zollhoefer, Michael Niessner, Matthias Tech Univ Munich Munich Germany Facebook Real Labs Res Pittsburgh PA USA

ISBN: (纸本)9781665445092

We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face(1). Digitally modeling and reconstructing a talking human is a key building-block for a variety of applications. Especially, for telepresence applications in AR or VR, a faithful reproduction of the appearance including novel viewpoint or headposes is required. In contrast to state-of-the-art approaches that model the geometry and material properties explicitly, or are purely image-based, we introduce an implicit representation of the head based on scene representation networks. To handle the dynamics of the face, we combine our scene representation network with a low-dimensional morphable model which provides explicit control over pose and expressions. We use volumetric rendering to generate images from this hybrid representation and demonstrate that such a dynamic neural scene representation can be learned from monocular input data only, without the need of a specialized capture setup. In our experiments, we show that this learned volumetric representation allows for photorealistic image generation that surpasses the quality of state-of-the-art video-based reenactment methods.

关键词： Geometry Telepresence Webcams Image synthesis Avatars Rendering (computer graphics) Hybrid power systems

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Efficient new-view synthesis using pairwise dictionary priors

Efficient new-view synthesis using pairwise dictionary prior...

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IEEE conference on computer vision and pattern recognition

作者： Woodford, O. J. Reid, I. D. Fitzgibbon, A. W. Univ Oxford Dept Engn Sci Parks Rd Oxford OX1 3PJ England Microsoft Res Cambridge England

ISBN: (纸本)9781424411795

New-view synthesis (NVS) using texture priors (as opposed to surface-smoothness priors) can yield high quality results, but the standard formulation is in terms of large-clique Markov Random Fields (MRFs). Only local optimization methods such as iterated conditional modes, which are prone to fall into local minima close to the initial estimate, are practical for solving these problems. In this paper we replace the large-clique energies with pairwise potentials, by restricting the patch dictionary for each clique to image regions suitable for that clique. This enables for the first time the use of a global optimization method, such as tree-reweighted message passing, to solve the NVS problem with image-based priors. We employ a robust, truncated quadratic kernel to reject outliers caused by occlusions, specularities and moving objects, within our global optimization. Because the MRF optimization is thus fast, computing the unary potentials becomes the new performance bottleneck. An additional contribution of this paper is a novel, fast method for enumerating color modes of the per-pixel unary potentials, despite the non-convex nature of our robust kernel. We compare the results of our technique with other rendering methods, and discuss the relative merits and flaws of regularizing color, and of local versus global dictionaries.

关键词： pattern recognition

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CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image

CVF-SID: Cyclic multi-Variate Function for Self-Supervised I...

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

作者： Neshatavar, Reyhaneh Yavartanoo, Mohsen Son, Sanghyun Lee, Kyoung Mu Seoul Natl Univ Dept ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, significant progress has been made on image denoising with strong supervision from large-scale datasets. However, obtaining well-aligned noisy-clean training image pairs for each specific scenario is complicated and costly in practice. Consequently, applying a conventional supervised denoising network on in-the-wild noisy inputs is not straightforward. Although several studies have challenged this problem without strong supervision, they rely on less practical assumptions and cannot be applied to practical situations directly. To address the aforementioned challenges, we propose a novel and powerful self-supervised denoising method called CVF-SID based on a Cyclic multiVariate Function (CVF) module and a self-supervised image disentangling (SID) framework. The CVF module can output multiple decomposed variables of the input and take a combination of the outputs back as an input in a cyclic manner. Our CVF-SID can disentangle a clean image and noise maps from the input by leveraging various self-supervised loss terms. Unlike several methods that only consider the signal-independent noise models, we also deal with signal-dependent noise components for real-world applications. Furthermore, we do not rely on any prior assumptions about the underlying noise distribution, making CVF-SID more generalizable toward realistic noise. Extensive experiments on real-world datasets show that CVFSID achieves state-of-the-art self-supervised image denoising performance and is comparable to other existing approaches. The code is publicly available from this link.

关键词： Training computer vision Correlation Codes Noise reduction pattern recognition Noise measurement

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Deep Network Interpolation for Continuous Imagery Effect Transition 32

Deep Network Interpolation for Continuous Imagery Effect Tra...

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

作者： Wang, Xintao Yu, Ke Dong, Chao Tang, Xiaoou Loy, Chen Change Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Shenzhen Guangdong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781728132938

Deep convolutional neural network has demonstrated its capability of learning a deterministic mapping for the desired imagery effect. However, the large variety of user flavors motivates the possibility of continuous transition among different output effects. Unlike existing methods that require a specific design to achieve one particular transition (e.g., style transfer), we propose a simple yet universal approach to attain a smooth control of diverse imagery effects in many low-level vision tasks, including image restoration, image-to-image translation, and style transfer. Specifically, our method, namely Deep Network Interpolation (DNI), applies linear interpolation in the parameter space of two or more correlated networks. A smooth control of imagery effects can be achieved by tweaking the interpolation coefficients. In addition to DNI and its broad applications, we also investigate the mechanism of network interpolation from the perspective of learned filters.

关键词： Low-level vision

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Background Splitting: Finding Rare Classes in a Sea of Background

Background Splitting: Finding Rare Classes in a Sea of Backg...

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

作者： Mullapudi, Ravi Teja Poms, Fait Mark, William R. Ramanan, Deva Fatahalian, Kayvon Stanford Univ Stanford CA 94305 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We focus on the problem of training deep image classification models for a small number of extremely rare categories. In this common, real-world scenario, almost all images belong to the background category in the dataset. We find that state-of-the-art approaches for training on imbalanced datasets do not produce accurate deep models in this regime. Our solution is to split the large, visually diverse background into many smaller, visually similar categories during training. We implement this idea by extending an image classification model with an additional auxiliary loss that learns to mimic the predictions of a pre-existing classification model on the training set. The auxiliary loss requires no additional human labels and regularizes feature learning in the shared network trunk by forcing the model to discriminate between auxiliary categories for all training set examples, including those belonging to the monolithic background of the main rare category classification task. To evaluate our method we contribute modified versions of the iNaturalist and Places365 datasets where only a small subset of rare category labels are available during training (all other images are labeled as background). By jointly learning to recognize both the selected rare categories and auxiliary categories, our approach yields models that perform 8.3 mAP points higher than stateof-the-art imbalanced learning baselines when 98.30% of the data is background, and up to 42.3 mAP points higher than fine-tuning baselines when 99.98% of the data is background.

关键词： Training computer vision Computational modeling Predictive models Data models pattern recognition Task analysis

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Multi-Label Learning from Single Positive Labels

Multi-Label Learning from Single Positive Labels

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

作者： Cole, Elijah Mac Aodha, Oisin Lorieul, Titouan Perona, Pietro Morris, Dan Jojic, Nebojsa CALTECH Pasadena CA 91125 USA Univ Edinburgh Edinburgh Midlothian Scotland INRIA Rocquencourt France Microsoft AI Earth Washington DC USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665445092

Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is considerably more challenging to annotate training data for multi-label classification. When the number of potential labels is large, human annotators find it difficult to mention all applicable labels for each training image. Furthermore, in some settings detection is intrinsically difficult e.g. finding small object instances in high resolution images. As a result, multi-label training data is often plagued by false negatives. We consider the hardest version of this problem, where annotators provide only one relevant label for each image. As a result, training sets will have only one positive label per image and no confirmed negatives. We explore this special case of learning from missing labels across four different multi-label image classification datasets for both linear classifiers and end-to-end fine-tuned deep networks. We extend existing multi-label losses to this setting and propose novel variants that constrain the number of expected positive labels during training. Surprisingly, we show that in some cases it is possible to approach the performance of fully labeled classifiers despite training with significantly fewer confirmed labels.

关键词： Training computer vision Image resolution Annotations Training data pattern recognition Standards

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NOC-REK: Novel Object Captioning with Retrieved Vocabulary from External Knowledge

NOC-REK: Novel Object Captioning with Retrieved Vocabulary f...

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

作者： Duc Minh Vo Chen, Hong Sugimoto, Akihiro Nakayama, Hideki Univ Tokyo Tokyo Japan Natl Inst Informat Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Novel object captioning aims at describing objects absent from training data, with the key ingredient being the provision of object vocabulary to the model. Although existing methods heavily rely on an object detection model, we view the detection step as vocabulary retrieval from an external knowledge in the form of embeddings for any object's definition from Wiktionary, where we use in the retrieval image region features learned from a transformers model. We propose an end-to-end Novel Object Captioning with Retrieved vocabulary from External Knowledge method (NOC-REK), which simultaneously learns vocabulary retrieval and caption generation, successfully describing novel objects outside of the training dataset. Furthermore, our model eliminates the requirement for model retraining by simply updating the external knowledge whenever a novel object appears. Our comprehensive experiments on held-out COCO and Nocaps datasets show that our NOC-REK is considerably effective against SOTAs.

关键词： Training Vocabulary computer vision Pipelines Training data Object detection Transformers

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Convolutional Prompting meets Language Models for Continual Learning

Convolutional Prompting meets Language Models for Continual ...

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

作者： Roy, Anurag Moulick, Riddhiman Verma, Vinay K. Ghosh, Saptarshi Das, Abir IIT Kharagpur Kharagpur W Bengal India IML Amazon India Hyderabad India

ISBN: (纸本)9798350353006

Continual Learning (CL) enables machine learning models to learn from continuously shifting new training data in absence of data from old tasks. Recently, pretrained vision transformers combined with prompt tuning have shown promise for overcoming catastrophic forgetting in CL. These approaches rely on a pool of learnable prompts which can be inefficient in sharing knowledge across tasks leading to inferior performance. In addition, the lack of fine-grained layer specific prompts does not allow these to fully express the strength of the prompts for CL. We address these limitations by proposing ConvPrompt, a novel convolutional prompt creation mechanism that maintains layer-wise shared embeddings, enabling both layer-specific learning and better concept transfer across tasks. The intelligent use of convolution enables us to maintain a low parameter overhead without compromising performance. We further leverage Large Language Models to generate fine-grained text descriptions of each category which are used to get task similarity and dynamically decide the number of prompts to be learned. Extensive experiments demonstrate the superiority of ConvPrompt and improves SOTA by 3% with significantly less parameter overhead. We also perform strong ablation over various modules to disentangle the importance of different components.(1)

关键词： Continual Learning Language Models Prompt Tuning vision Transformer

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