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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是4211-4220 订阅

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Energy-Based Learning for Scene Graph Generation

Energy-Based Learning for Scene Graph Generation

引用

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

作者： Suhail, Mohammed Mittal, Abhay Siddiquie, Behjat Broaddus, Chris Eledath, Jayan Medioni, Gerard Sigal, Leonid Univ British Columbia Vancouver BC Canada Vector Inst AI Bengaluru India Canada CIFAR AI Chair Montreal PQ Canada Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. Such a formulation, however, ignores the structure in the output space, in an inherently structured prediction problem. In this work, we introduce a novel energy-based learning framework for generating scene graphs. The proposed formulation allows for efficiently incorporating the structure of scene graphs in the output space. This additional constraint in the learning framework acts as an inductive bias and allows models to learn efficiently from a small number of labels. We use the proposed energy-based framework + to train existing state-of-the-art models and obtain a significant performance improvement, of up to 21% and 27%, on the Visual Genome [9] and GQA [5] benchmark datasets, respectively. Furthermore, we showcase the learning efficiency of the proposed framework by demonstrating superior performance in the zero- and few-shot settings where data is scarce.

关键词： Training Visualization computer vision Computational modeling Genomics Benchmark testing pattern recognition

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Representative Batch Normalization with Feature Calibration

Representative Batch Normalization with Feature Calibration

引用

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

作者： Gao, Shang-Hua Han, Qi Li, Duo Cheng, Ming-Ming Peng, Pai Nankai Univ CS TKLNDST Tianjin Peoples R China HKUST Hong Kong Peoples R China Tencent Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Batch Normalization (BatchNorm) has become the default component in modern neural networks to stabilize training. In BatchNorm, centering and scaling operations, along with mean and variance statistics, are utilized for feature standardization over the batch dimension. The batch dependency of BatchNorm enables stable training and better representation of the network, while inevitably ignores the representation differences among instances. We propose to add a simple yet effective feature calibration scheme into the centering and scaling operations of BatchNorm, enhancing the instance-specific representations with the negligible computational cost. The centering calibration strengthens informative features and reduces noisy features. The scaling calibration restricts the feature intensity to form a more stable feature distribution. Our proposed variant of BatchNorm, namely Representative BatchNorm, can be plugged into existing methods to boost the performance of various tasks such as classification, detection, and segmentation.

关键词： Training computer vision Costs Neural networks Standardization Calibration pattern recognition

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In-Hand 3D Object Scanning from an RGB Sequence

In-Hand 3D Object Scanning from an RGB Sequence

引用

conference on computer vision and pattern recognition (cvpr)

作者： Shreyas Hampali Tomas Hodan Luan Tran Lingni Ma Cem Keskin Vincent Lepetit Reality Labs at Meta LIGM Ecole des Ponts Univ Gustave Eiffel CNRS Marne-la-Vallée France Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

We propose a method for in-hand 3D scanning of an unknown object with a monocular camera. Our method relies on a neural implicit surface representation that captures both the geometry and the appearance of the object, however, by contrast with most NeRF-based methods, we do not assume that the camera-object relative poses are known. Instead, we simultaneously optimize both the object shape and the pose trajectory. As direct optimization over all shape and pose parameters is prone to fail without coarse-level initialization, we propose an incremental approach that starts by splitting the sequence into carefully selected overlapping segments within which the optimization is likely to succeed. We reconstruct the object shape and track its poses independently within each segment, then merge all the segments before performing a global optimization. We show that our method is able to reconstruct the shape and color of both textured and challenging texture-less objects, outperforms classical methods that rely only on appearance features, and that its performance is close to recent methods that assume known camera poses.

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Invertible Image Signal Processing

Invertible Image Signal Processing

引用

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

作者： Xing, Yazhou Qian, Zian Chen, Qifeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Unprocessed RAW data is a highly valuable image format for image editing and computer vision. However, since the file size of RAW data is huge, most users can only get access to processed and compressed sRGB images. To bridge this gap, we design an Invertible Image Signal Processing (InvISP) pipeline, which not only enables rendering visually appealing sRGB images but also allows recovering nearly perfect RAW data. Due to our framework's inherent reversibility, we can reconstruct realistic RAW data instead of synthesizing RAW data from sRGB images without any memory overhead. We also integrate a differentiable JPEG compression simulator that empowers our framework to reconstruct RAW data from JPEG images. Extensive quantitative and qualitative experiments on two DSLR demonstrate that our method obtains much higher quality in both rendered sRGB images and reconstructed RAW data than alternative methods.

关键词： computer vision Image coding Pipelines Transform coding Signal processing Rendering (computer graphics) Stability analysis

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Explore Image Deblurring via Encoded Blur Kernel Space

Explore Image Deblurring via Encoded Blur Kernel Space

引用

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

作者： Phong Tran Anh Tuan Tran Quynh Phung Minh Hoai VinAI Res Hanoi Vietnam VinUniversity Hanoi Vietnam SUNY Stony Brook Stony Brook NY 11790 USA

ISBN: (纸本)9781665445092

This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen blur operator by a kernel in the encoded space and searches for the corresponding sharp image. Unlike recent deep-learning-based methods, our system can handle unseen blur kernel, while avoiding using complicated handcrafted priors on the blur operator often found in classical methods. Due to the method's design, the encoded kernel space is fully differentiable, thus can be easily adopted in deep neural network models. Moreover, our method can be used for blur synthesis by transferring existing blur operators from a given dataset into a new domain. Finally, we provide experimental results to confirm the effectiveness of the proposed method. The code is available at https://***/VinAIResearch/blur-kernelspace-exploring.

关键词： Deep learning computer vision Codes Design methodology Gray-scale Image restoration pattern recognition

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Transformation Invariant Few-Shot Object Detection

Transformation Invariant Few-Shot Object Detection

引用

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

作者： Li, Aoxue Li, Zhenguo Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Few-shot object detection (FSOD) aims to learn detectors that can be generalized to novel classes with only a few instances. Unlike previous attempts that exploit meta-learning techniques to facilitate FSOD, this work tackles the problem from the perspective of sample expansion. To this end, we propose a simple yet effective Transformation Invariant Principle (TIP) that can be flexibly applied to various meta-learning models for boosting the detection performance on novel class objects. Specifically, by introducing consistency regularization on predictions from various transformed images, we augment vanilla FSOD models with the generalization ability to objects perturbed by various transformation, such as occlusion and noise. Importantly, our approach can extend supervised FSOD models to naturally cope with unlabeled data, thus addressing a more practical and challenging semi-supervised FSOD problem. Extensive experiments on PASCAL VOC and MSCOCO datasets demonstrate the effectiveness of our TIP under both of the two FSOD settings.

关键词： computer vision Object detection Detectors Predictive models Boosting Data models pattern recognition

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NTIRE 2023 Challenge on HR Depth from Images of Specular and Transparent Surfaces

NTIRE 2023 Challenge on HR Depth from Images of Specular and...

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Pierluigi Zama Ramirez Fabio Tosi Luigi Di Stefano Radu Timofte Alex Costanzino Matteo Poggi Samuele Salti Stefano Mattoccia Jun Shi Dafeng Zhang Yong A Yixiang Jin Dingzhe Li Chao Li Zhiwen Liu Qi Zhang Yixing Wang Shi Yin

This paper reports about the NTIRE 2023 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at cvpr 2023. This challenge is held to boost the research on depth estimation, mainly to deal with two of the open issues in the field: high-resolution images and non-Lambertian surfaces characterizing specular and transparent materials. The challenge is divided into two tracks: a stereo track focusing on disparity estimation from rectified pairs and a mono track dealing with single-image depth estimation. The challenge attracted about 100 registered participants for the two tracks. In the final testing stage, 5 participating teams submitted their models and fact sheets, 2 and 3 for the Stereo and Mono tracks, respectively.

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MOOD: Multi-level Out-of-distribution Detection

MOOD: Multi-level Out-of-distribution Detection

引用

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

作者： Lin, Ziqian Roy, Sreya Dutta Li, Yixuan Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

ISBN: (纸本)9781665445092

Out-of-distribution (OOD) detection is essential to prevent anomalous inputs from causing a model to fail during deployment. While improved OOD detection methods have emerged, they often rely on the final layer outputs and require a full feedforward pass for any given input. In this paper, we propose a novel framework, multi-level out-of-distribution detection (MOOD), which exploits intermediate classifier outputs for dynamic and efficient OOD inference. We explore and establish a direct relationship between the OOD data complexity and optimal exit level, and show that easy OOD examples can be effectively detected early without propagating to deeper layers. At each exit, the OOD examples can be distinguished through our proposed adjusted energy score, which is both empirically and theoretically suitable for networks with multiple classifiers. We extensively evaluate MOOD across 10 OOD datasets spanning a wide range of complexities. Experiments demonstrate that MOOD achieves up to 71.05% computational reduction in inference, while maintaining competitive OOD detection performance.

关键词： computer vision Mood Complexity theory pattern recognition Computational efficiency

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Cross-Iteration Batch Normalization

Cross-Iteration Batch Normalization

引用

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

作者： Yao, Zhuliang Cao, Yue Zheng, Shuxin Huang, Gao Lin, Stephen Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

A well-known issue of Batch Normalization is its significantly reduced effectiveness in the case of small mini-batch sizes. When a mini-batch contains few examples, the statistics upon which the normalization is defined cannot be reliably estimated from it during a training iteration. To address this problem, we present Cross-Iteration Batch Normalization (CBN), in which examples from multiple recent iterations are jointly utilized to enhance estimation quality. A challenge of computing statistics over multiple iterations is that the network activations from different iterations are not comparable to each other due to changes in network weights. We thus compensate for the network weight changes via a proposed technique based on Taylor polynomials, so that the statistics can be accurately estimated and batch normalization can be effectively applied. On object detection and image classification with small mini-batch sizes, CBN is found to outperform the original batch normalization and a direct calculation of statistics over previous iterations without the proposed compensation technique.

关键词： Training computer vision Upper bound Codes Estimation Object detection pattern recognition

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High-Fidelity Guided Image Synthesis with Latent Diffusion Models

High-Fidelity Guided Image Synthesis with Latent Diffusion M...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jaskirat Singh Stephen Gould Liang Zheng The Australian National University Australian Centre for Robotic Vision

Controllable image synthesis with user scribbles has gained huge public interest with the recent advent of text-conditioned latent diffusion models. The user scribbles control the color composition while the text prompt provides control over the overall image semantics. However, we note that prior works in this direction suffer from an intrinsic domain shift problem wherein the generated outputs often lack details and resemble simplistic representations of the target domain. In this paper, we propose a novel guided image synthesis framework, which addresses this problem by modelling the output image as the solution of a constrained optimization problem. We show that while computing an exact solution to the optimization is infeasible, an approximation of the same can be achieved while just requiring a single pass of the reverse diffusion process. Additionally, we show that by simply defining a cross-attention based correspondence between the input text tokens and the user stroke-painting, the user is also able to control the semantics of different painted regions without requiring any conditional training or finetuning. Human user study results show that the proposed approach outperforms the previous state-of-the-art by over 85.32% on the overall user satisfaction scores. Project page for our paper is available at https://***/gradop.

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