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共 21008 条记录，以下是1541-1550 订阅

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Learning Program Representations for Food Images and Cooking Recipes

Learning Program Representations for Food Images and Cooking...

引用

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

作者： Papadopoulos, Dim P. Mora, Enrique Chepurko, Nadiia Huang, Kuan Wei Ofli, Ferda Torralba, Antonio MIT CSAIL Cambridge MA 02139 USA Nestle Vevey Switzerland DTU Compute Lyngby Denmark HBKU Qatar Comp Res Inst Ar Rayyan Qatar

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we are interested in modeling a how-to instructional procedure, such as a cooking recipe, with a meaningfiil and rich high-level representation. Specifically, we propose to represent cooking recipes and food images as cooking programs. Programs provide a structured representation of the task, capturing cooking semantics and sequential relationships of actions in the form of a graph. This allows them to be easily manipulated by users and executed by agents. To this end, we build a model that is trained to learn a joint embedding between recipes and food images via self-supervision and jointly generate a program from this embedding as a sequence. To validate our idea, we crowdsource programs for cooking recipes and show that: (a) projecting the image-recipe embeddings into programs leads to better cross-modal retrieval results;(b) generating programs from images leads to better recognition results compared to predicting raw cooking instructions;and (c) we can generate food images by manipulating programs via optimizing the latent code of a GAN. Code, data, and models are available online(1).

关键词： computer vision Codes Image recognition Semantics Data models pattern recognition Task analysis

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Block-NeRF: Scalable Large Scene Neural View Synthesis

Block-NeRF: Scalable Large Scene Neural View Synthesis

引用

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

作者： Tancik, Matthew Casser, Vincent Yan, Xinchen Pradhan, Sabeek Mildenhall, Ben P. Srinivasan, Pratul Barron, Jonathan T. Kretzschmar, Henrik Univ Calif Berkeley Berkeley CA 94720 USA Waymo Mountain View CA 94043 USA Google Res Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments. Specifically, we demonstrate that when scaling NeRF to render city-scale scenes spanning multiple blocks, it is vital to decompose the scene into individually trained NeRFs. This decomposition decouples rendering time from scene size, enables rendering to scale to arbitrarily large environments, and allows per-block updates of the environment. We adopt several architectural changes to make NeRF robust to data captured over months under different environmental conditions. We add appearance embeddings, learned pose refinement, and controllable exposure to each individual NeRF, and introduce a procedure for aligning appearance between adjacent NeRFs so that they can be seamlessly combined. We build a grid of Block-NeRFs from 2.8 million images to create the largest neural scene representation to date, capable of rendering an entire neighborhood of San Francisco.

关键词： computer vision Buildings computer architecture Rendering (computer graphics) pattern recognition Transient analysis Image reconstruction

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Multi-Dimensional, Nuanced and Subjective - Measuring the Perception of Facial Expressions

Multi-Dimensional, Nuanced and Subjective - Measuring the Pe...

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

作者： Bryant, De'Aira Deng, Siqi Sephus, Nashlie Xia, Wei Perona, Pietro AWS AI Labs Palo Alto CA 94301 USA Georgia Inst Technol Atlanta GA 30332 USA CALTECH Pasadena CA 91125 USA Amazon Seattle WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Humans can perceive multiple expressions, each one with varying intensity, in the picture of a face. We propose a methodology for collecting and modeling multidimensional modulated expression annotations from human annotators. Our data reveals that the perception of some expressions can be quite different across observers;thus, our model is designed to represent ambiguity alongside intensity. An empirical exploration of how many dimensions are necessary to capture the perception of facial expression suggests six principal expression dimensions are sufficient. Using our method, we collected multidimensional modulated expression annotations for 1,000 images culled from the popular ExpW in-the-wild dataset. As a proof of principle of our improved measurement technique, we used these annotations to benchmark four public domain algorithms for automated facial expression prediction.

关键词： computer vision Annotations Face recognition Measurement techniques Observers Benchmark testing Prediction algorithms

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Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport

Bending Graphs: Hierarchical Shape Matching using Gated Opti...

引用

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

作者： Saleh, Mahdi Wu, Shun-Cheng Cosmo, Luca Navab, Nassir Busam, Benjamin Tombari, Federico Tech Univ Munich Munich Germany Ca Foscari Univ Venice Venice Italy USI Univ Lugano Lugano Switzerland Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Shape matching has been a long-studied problem for the computer graphics and vision community. The objective is to predict a dense correspondence between meshes that have a certain degree of deformation. Existing methods either consider the local description of sampled points or discover correspondences based on global shape information. In this work, we investigate a hierarchical learning design, to which we incorporate local patch-level information and global shape-level structures. This flexible representation enables correspondence prediction and provides rich features for the matching stage. Finally, we propose a novel optimal transport solver by recurrently updating features on non-confident nodes to learn globally consistent correspondences between the shapes. Our results on publicly available datasets suggest robust performance in presence of severe deformations without the need of extensive training or refinement.

关键词： Training Representation learning computer vision Shape Pipelines Pose estimation Logic gates

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Geometric and Textural Augmentation for Domain Gap Reduction

Geometric and Textural Augmentation for Domain Gap Reduction

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

作者： Liu, Xiao-Chang Yang, Yong-Liang Hall, Peter Univ Bath Bath Avon England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Research has shown that convolutional neural networks for object recognition are vulnerable to changes in depiction because learning is biased towards the low-level statistics of texture patches. Recent works concentrate on improving robustness by applying style transfer to training examples to mitigate against over-fitting to one depiction style. These new approaches improve performance, but they ignore the geometric variations in object shape that real art exhibits: artists deform and warp objects for artistic effect. Motivated by this observation, we propose a method to reduce bias by jointly increasing the texture and geometry diversities of the training data. In effect, we extend the visual object class to include examples with shape changes that artists use. Specifically, we learn the distribution of warps that cover each given object class. Together with augmenting textures based on a broad distribution of styles, we show by experiments that our method improves performance on several cross-domain benchmarks.

关键词： Geometry Training Visualization Shape Training data Robustness pattern recognition

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Amodal Panoptic Segmentation

Amodal Panoptic Segmentation

引用

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

作者： Mohan, Rohit Valada, Abhinav Univ Freiburg Freiburg Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Humans have the remarkable ability to perceive objects as a whole, even when parts of them are occluded. This ability of amodal perception forms the basis of our perceptual and cognitive understanding of our world. To enable robots to reason with this capability, we formulate and propose a novel task that we name amodal panoptic segmentation. The goal of this task is to simultaneously predict the pixel-wise semantic segmentation labels of the visible regions of stuff classes and the instance segmentation labels of both the visible and occluded regions of thing classes. To facilitate research on this new task, we extend two established benchmark datasets with pixel-level amodal panoptic segmentation labels that we make publicly available as KITTI-360-APS and BDD100K-APS. We present several strong baselines, along with the amodal panoptic quality (APQ) and amodal parsing coverage (APC) metrics to quantify the performance in an interpretable manner. Furthermore, we propose the novel amodal panoptic segmentation network (APSNet), as a first step towards addressing this task by explicitly modeling the complex relationships between the occluders and occludes. Extensive experimental evaluations demonstrate that APSNet achieves state-of-the-art performance on both benchmarks and more importantly exemplifies the utility of amodal recognition. The datasets are available at http://*** -***.

关键词： Measurement computer vision Computational modeling Semantics computer architecture Benchmark testing pattern recognition

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Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection

Self-supervised Learning of Adversarial Example: Towards Goo...

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

作者： Chen, Liang Zhang, Yong Song, Yibing Liu, Lingqiao Wang, Jue Univ Adelaide Adelaide SA Australia Tencent AI Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same dataset. However, the problem remains challenging when one tries to generalize the detector to forgeries created by unseen methods in the training dataset. This work addresses the generalizable deep fake detection from a simple principle: a generalizable representation should be sensitive to diverse types offorg,cries. Following this principle, we propose to enrich the "diversity" of forgeries by synthesizing augmented Pig cries with a pool offorgery configurations and strengthen the "sensitivity" to the forgeries by enforcing the model to predict the forgery configurations. To effectively explore the large forgery augmentation space, we further propose to use the adversarial training strategy to dynamically synthesize the most challenging forgeries to the current model. Through extensive experiments, we show that the proposed strategies are surprisingly effective (see Figure 1), and they could achieve superior performance than the current state-of-the-art methods.

关键词： Training Deepfakes computer vision Sensitivity Face recognition Self-supervised learning Detectors

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How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs

How Do You Do It? Fine-Grained Action Understanding with Pse...

引用

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

作者： Doughty, Hazel Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We aim to understand how actions are performed and identify subtle differences, such as `fold firmly' vs. `fold gently'. To this end, we propose a method which recognizes adverbs across different actions. However, such finegrained annotations are difficult to obtain and their longtailed nature makes it challenging to recognize adverbs in rare action-adverb compositions. Our approach therefore uses semi-supervised learning with multiple adverb pseudolabels to leverage videos with only action labels. Combined with adaptive thresholding of these pseudo-adverbs we are able to make efficient use of the available data while tackling the long-tailed distribution. Additionally, we gather adverb annotations for three existing video retrieval datasets, which allows us to introduce the new tasks of recognizing adverbs in unseen action-adverb compositions and unseen domains. Experiments demonstrate the effectiveness of our method, which outperforms prior work in recognizing adverbs and semi-supervised works adapted for adverb recognition. We also show how adverbs can relate fine-grained actions.

关键词： computer vision Annotations Semisupervised learning pattern recognition Task analysis Videos

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PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images

PolyWorld: Polygonal Building Extraction with Graph Neural N...

引用

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

作者： Zorzi, Stefano Bazrafkan, Shabab Habenschuss, Stefan Fraundorfer, Friedrich Graz Univ Technol Graz Austria Blackshark Ai Graz Austria VRVis Vienna Austria

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While most state-of-the-art instance segmentation methods produce binary segmentation masks, geographic and cartographic applications typically require precise vector polygons of extracted objects instead of rasterized output. This paper introduces PolyWorld, a neural network that directly extracts building vertices from an image and connects them correctly to create precise polygons. The model predicts the connection strength between each pair of vertices using a graph neural network and estimates the assignments by solving a differentiable optimal transport problem. Moreover, the vertex positions are optimized by minimizing a combined segmentation and polygonal angle difference loss. PolyWorld significantly outperforms the state of the art in building polygonization and achieves not only notable quantitative results, but also produces visually pleasing building polygons. Code and trained weights are publicly available at https://***/zorzi-s/PolyWorldPretrainedNetwork.

关键词： Image segmentation computer vision Satellites Codes computer network reliability Buildings Predictive models

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Point-Level Region Contrast for Object Detection Pre-Training

Point-Level Region Contrast for Object Detection Pre-Trainin...

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

作者： Bai, Yutong Chen, Xinlei Kirillov, Alexander Yuille, Alan Berg, Alexander C. Facebook AI Res FAIR Menlo Pk CA 94025 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work we present point-level region contrast, a self-supervised pre-training approach for the task of object detection. This approach is motivated by the two key factors in detection: localization and recognition. While accurate localization favors models that operate at the pixel- or point-level, correct recognition typically relies on a more holistic, region-level view of objects. Incorporating this perspective in pre-training, our approach performs contrastive learning by directly sampling individual point pairs from different regions. Compared to an aggregated representation per region, our approach is more robust to the change in input region quality, and further enables us to implicitly improve initial region assignments via online knowledge distillation during training. Both advantages are important when dealing with imperfect regions encountered in the unsupervised setting. Experiments show point-level region contrast improves on state-of-the-art pre-training methods for object detection and segmentation across multiple tasks and datasets, and we provide extensive ablation studies and visualizations to aid understanding. Code will be made available.

关键词： Location awareness Training Visualization computer vision Codes Object detection pattern recognition

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