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

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Generalization on Unseen Domains via Inference-time Label-Preserving Target Projections

Generalization on Unseen Domains via Inference-time Label-Pr...

引用

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

作者： Pandey, Prashant Raman, Mrigank Varambally, Sumanth Prathosh, A. P. IIT Delhi Delhi India

ISBN: (纸本)9781665445092

Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem. While many approaches have been proposed to solve this problem, they only utilize source data during training but do not take advantage of the fact that a single target example is available at the time of inference. Motivated by this, we propose a method that effectively uses the target sample during inference beyond mere classification. Our method has three components - (i) A label-preserving feature or metric transformation on source data such that the source samples are clustered in accordance with their class irrespective of their domain (ii) A generative model trained on the these features (iii) A label-preserving projection of the target point on the source-feature manifold during inference via solving an optimization problem on the input space of the generative model using the learned metric. Finally, the projected target is used in the classifier. Since the projected target feature comes from the source manifold and has the same label as the real target by design, the classifier is expected to perform better on it than the true target. We demonstrate that our method outperforms the state-of-the-art Domain Generalization methods on multiple datasets and tasks.

关键词： Manifolds Training computer vision Machine learning Extraterrestrial measurements Data models pattern recognition

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Intentonomy: a Dataset and Study towards Human Intent Understanding

Intentonomy: a Dataset and Study towards Human Intent Unders...

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

作者： Jia, Menglin Wu, Zuxuan Reiter, Austin Cardie, Claire Belongie, Serge Lim, Ser-Nam Cornell Univ Ithaca NY 14853 USA Facebook AI Menlo Pk CA 94025 USA Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

An image is worth a thousand words, conveying information that goes beyond the mere visual content therein. In this paper, we study the intent behind social media images with an aim to analyze how visual information can facilitate recognition of human intent. Towards this goal, we introduce an intent dataset, Intentonomy, comprising 14K images covering a wide range of everyday scenes. These images are manually annotated with 28 intent categories derived from a social psychology taxonomy. We then systematically study whether, and to what extent, commonly used visual information, i.e., object and context, contribute to human motive understanding. Based on our findings, we conduct further study to quantify the effect of attending to object and context classes as well as textual information in the form of hashtags when training an intent classifier. Our results quantitatively and qualitatively shed light on how visual and textual information can produce observable effects when predicting intent.(1)

关键词： Training Visualization computer vision Image recognition Social networking (online) Taxonomy Psychology

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3D Spatial recognition without Spatially Labeled 3D

3D Spatial Recognition without Spatially Labeled 3D

引用

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

作者： Ren, Zhongzheng Misra, Ishan Schwing, Alexander G. Girdhar, Rohit Facebook AI Res Menlo Pk CA 94025 USA Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781665445092

We introduce WyPR, a Weakly-supervised framework for Point cloud recognition, requiring only scene-level class tags as supervision. WyPR jointly addresses three core 3D recognition tasks: point-level semantic segmentation, 3D proposal generation, and 3D object detection, coupling their predictions through self and cross-task consistency losses. We show that in conjunction with standard multiple-instance learning objectives, WyPR can detect and segment objects in point cloud data without access to any spatial labels at training time. We demonstrate its efficacy using the ScanNet and S3DIS datasets, outperforming prior state of the art on weakly-supervised segmentation by more than 6% mIoU. In addition, we set up the first benchmark for weakly-supervised 3D object detection on both datasets, where WyPR outperforms standard approaches and establishes strong baselines for future work.

关键词： Training Couplings computer vision Three-dimensional displays Semantics Object detection pattern recognition

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Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect

Invisible Perturbations: Physical Adversarial Examples Explo...

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

作者： Sayles, Athena Hooda, Ashish Gupta, Mohit Chatterjee, Rahul Fernandes, Earlence Univ Wisconsin Madison WI 53706 USA

ISBN: (纸本)9781665445092

Physical adversarial examples for camera-based computer vision have so far been achieved through visible artifacts - a sticker on a Stop sign, colorful borders around eyeglasses or a 3D printed object with a colorful texture. An implicit assumption here is that the perturbations must be visible so that a camera can sense them. By contrast, we contribute a procedure to generate, for the first time, physical adversarial examples that are invisible to human eyes. Rather than modifying the victim object with visible artifacts, we modify light that illuminates the object. We demonstrate how an attacker can craft a modulated light signal that adversarially illuminates a scene and causes targeted misclassifications on a state-of-the-art ImageNet deep learning model. Concretely, we exploit the radiometric rolling shutter effect in commodity cameras to create precise striping patterns that appear on images. To human eyes, it appears like the object is illuminated, but the camera creates an image with stripes that will cause ML models to output the attacker-desired classification. We conduct a range of simulation and physical experiments with LEDs, demonstrating targeted attack rates up to 84%.

关键词： Deep learning computer vision Three-dimensional displays Computational modeling Perturbation methods Cameras Radiometry

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OpenGait: Revisiting Gait recognition Toward Better Practicality

OpenGait: Revisiting Gait Recognition Toward Better Practica...

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conference on computer vision and pattern recognition (cvpr)

作者： Chao Fan Junhao Liang Chuanfu Shen Saihui Hou Yongzhen Huang Shiqi Yu Department of Computer Science and Engineering Southern University of Science and Technology Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong School of Artificial Intelligence Beijing Normal University WATRIX.AI

Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly, we also find that some conclusions drawn from indoor datasets cannot be generalized to real applications. Therefore, the primary goal of this paper is to present a comprehensive benchmark study for better practicality rather than only a particular model for better performance. To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait. Based on OpenGait, we deeply revisit the recent development of gait recognition by re-conducting the ablative experiments. Encouragingly, we detect some unperfect parts of certain prior woks, as well as new insights. Inspired by these discoveries, we develop a structurally simple, empirically powerful, and practically robust baseline model, Gait-Base. Experimentally, we comprehensively compare Gait-Base with many current gait recognition methods on multiple public datasets, and the results reflect that GaitBase achieves significantly strong performance in most cases regardless of indoor or outdoor situations. Code is available at https://***/ShiqiYu/OpenGait.

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TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations

TransFill: Reference-guided Image Inpainting by Merging Mult...

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

作者： Zhou, Yuqian Barnes, Connelly Shechtman, Eli Amirghodsi, Sohrab UIUC IFP Champaign IL 61820 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

Image inpainting is the task of plausibly restoring missing pixels within a hole region that is to be removed from a target image. Most existing technologies exploit patch similarities within the image, or leverage large-scale training data to fill the hole using learned semantic and texture information. However, due to the ill-posed nature of the inpainting task, such methods struggle to complete larger holes containing complicated scenes. In this paper, we propose TransFill, a multi-homography transformed fusion method to fill the hole by referring to another source image that shares scene contents with the target image. We first align the source image to the target image by estimating multiple homographies guided by different depth levels. We then learn to adjust the color and apply a pixel-level warping to each homography-warped source image to make it more consistent with the target. Finally, a pixel-level fusion module is learned to selectively merge the different proposals. Our method achieves state-of-the-art performance on pairs of images across a variety of wide baselines and color differences, and generalizes to user-provided image pairs.

关键词： computer vision Image color analysis Semantics Merging Training data pattern recognition Image restoration

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DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation f...

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

作者： Singh, Abhishek Chopra, Ayush Garza, Ethan Zhang, Emily Vepakomma, Praneeth Sharma, Vivek Raskar, Ramesh MIT 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Med Sch Boston MA 02115 USA

ISBN: (纸本)9781665445092

Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer to practical deployment, the common assumption made about data is that it does not carry any sensitive information. This assumption may not hold for many practical cases, especially in the domain where an individual's personal information is involved, like healthcare and facial recognition systems. We posit that selectively removing features in this latent space can protect the sensitive information and provide better privacy-utility trade-off. Consequently, we propose DISCO which learns a dynamic and data driven pruning filter to selectively obfuscate sensitive information in the feature space. We propose diverse attack schemes for sensitive inputs & attributes and demonstrate the effectiveness of DISCO against state-of-the-art methods through quantitative and qualitative evaluation. Finally, we also release an evaluation benchmark dataset of 1 million sensitive representations to encourage rigorous exploration of novel attack and defense schemes at https://***/splitlearning/InferenceBenchmark.

关键词： Deep learning Privacy computer vision Face recognition Collaboration Medical services Benchmark testing

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DAA: A Delta Age AdaIN operation for age estimation via binary code transformer

DAA: A Delta Age AdaIN operation for age estimation via bina...

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conference on computer vision and pattern recognition (cvpr)

作者： Ping Chen Xingpeng Zhang Ye Li Ju Tao Bin Xiao Bing Wang Zongjie Jiang Jiayu Intelligent Technology Co. Ltd. (Affiliated With Great Wall Motor Company Limited) School of Computer Science Southwest Petroleum University Chengdu China

Naked eye recognition of age is usually based on comparison with the age of others. However, this idea is ignored by computer tasks because it is difficult to obtain representative contrast images of each age. Inspired by the transfer learning, we designed the Delta Age AdaIN (DAA) operation to obtain the feature difference with each age, which obtains the style map of each age through the learned values representing the mean and standard deviation. We let the input of transfer learning as the binary code of age natural number to obtain continuous age feature information. The learned two groups of values in Binary code mapping are corresponding to the mean and standard deviation of the comparison ages. In summary, our method consists of four parts: FaceEncoder, DAA operation, Binary code mapping, and AgeDecoder modules. After getting the delta age via AgeDecoder, we take the average value of all comparison ages and delta ages as the predicted age. Compared with state-of-the-art methods, our method achieves better performance with fewer parameters on multiple facial age datasets. Code is available at https://***/redcping/Delta_Age_AdaIN

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OTST: A Two-Phase Framework for Joint Denoising and Remosaicing in RGBW CFA

OTST: A Two-Phase Framework for Joint Denoising and Remosaic...

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2023 ieee/cvf conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Fan, Zhihao Wu, Xun Meng, Fanqing Wu, Yaqi Zhang, Feng Tetras.AI University of Shanghai for Science and Technology China Tsinghua University China Tongji University China Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798350302493

RGBW, a newly emerged type of Color Filter Array (CFA), possesses strong low-light photography capabilities. RGBW CFA shows significant application value when low-light sensitivity is critical, such as in security cameras and smartphones. However, the majority of commercial image signal processors (ISP) are primarily designed for Bayer CFA, research pertaining to RGBW CFA is very rare. To address above limitations, in this study, we propose a two-phase framework named OTST for the RGBW Joint Denoising and Remosaicing (RGBW-JRD) task. For the denoising stage, we propose Omni-dimensional Dynamic Convolution based Half-Shuffle Transformer (ODC-HST) which can fully utilize image's long-range dependencies to dynamically remove the noise. For the remosaicing stage, we propose a Spatial Compressive Transformer (SCT) to efficiently capture both local and global dependencies across spatial and channel dimensions. Experimental results demonstrate that our two-phase RGBW-JRD framework outperforms existing RGBW denoising and remosaicing solutions across a wide range of noise levels. In addition, the proposed approach ranks the 2nd place in MIPI 2023 RGBW Joint Remosaic and Denoise competition. © 2023 ieee.

关键词： computer vision

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Glocal Energy-based Learning for Few-Shot Open-Set recognition

Glocal Energy-based Learning for Few-Shot Open-Set Recogniti...

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conference on computer vision and pattern recognition (cvpr)

作者： Haoyu Wang Guansong Pang Peng Wang Lei Zhang Wei Wei Yanning Zhang Northwestern Polytechnical University Singapore Management University University of Wollonong

Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the predefined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closed-set classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of open-set samples, our model leverages both class-wise and pixel-wise features to learn a glocal energy-based score, in which a global energy score is learned using the class-wise features, while a local energy score is learned using the pixel-wise features. The model is enforced to assign large energy scores to samples that are deviated from the few-shot examples in either the class-wise features or the pixel-wise features, and to assign small energy scores otherwise. Experiments on three standard FSOR datasets show the superior performance of our model. 1 1 Code is available at https://***/00why00/Glocal

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