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Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning

Revamping Cross-Modal Recipe Retrieval with Hierarchical Tra...

引用

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

作者： Salvador, Amaia Gundogdu, Erhan Bazzani, Loris Donoser, Michael Amazon Seattle WA 98109 USA

ISBN: (纸本)9781665445092

Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people's lives, as well as the availability of vast amounts of digital cooking recipes and food images to train machine learning models. In this work, we revisit existing approaches for cross-modal recipe retrieval and propose a simplified end-to-end model based on well established and high performing encoders for text and images. We introduce a hierarchical recipe Transformer which attentively encodes individual recipe components (titles, ingredients and instructions). Further, we propose a self-supervised loss function computed on top of pairs of individual recipe components, which is able to leverage semantic relationships within recipes, and enables training using both image-recipe and recipe-only samples. We conduct a thorough analysis and ablation studies to validate our design choices. As a result, our proposed method achieves state-of-the-art performance in the cross-modal recipe retrieval task on the Recipe1M dataset. We make code and models publicly available(1).

关键词： Training computer vision Codes Computational modeling Semantics Machine learning Transformers

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Unsupervised Disentanglement of Linear-Encoded Facial Semantics

Unsupervised Disentanglement of Linear-Encoded Facial Semant...

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

作者： Zheng, Yutong Huang, Yu-Kai Tao, Ran Shen, Zhiqiang Savvides, Marios Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision. The method derives from linear regression and sparse representation learning concepts to make the disentangled latent representations easily interpreted as well. We start by coupling StyleGAN with a stabilized 3D deformable facial reconstruction method to decompose single-view GAN generations into multiple semantics. Latent representations are then extracted to capture interpretable facial semantics. In this work, we make it possible to get rid of labels for disentangling meaningful facial semantics. Also, we demonstrate that the guided extrapolation along the disentangled representations can help with data augmentation, which sheds light on handling unbalanced data. Finally, we provide an analysis of our learned localized facial representations and illustrate that the semantic information is encoded, which surprisingly complies with human intuition. The overall unsupervised design brings more flexibility to representation learning in the wild.

关键词： Couplings Extrapolation computer vision Three-dimensional displays Semantics Linear regression Reconstruction algorithms

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Holistic 3D Human and Scene Mesh Estimation from Single View Images

Holistic 3D Human and Scene Mesh Estimation from Single View...

引用

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

作者： Weng, Zhenzhen Yeung, Serena Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

The 3D world limits the human body pose and the human body pose conveys information about the surrounding objects. Indeed, from a single image of a person placed in an indoor scene, we as humans are adept at resolving ambiguities of the human pose and room layout through our knowledge of the physical laws and prior perception of the plausible object and human poses. However, few computer vision models fully leverage this fact. In this work, we propose a holistically trainable model that perceives the 3D scene from a single RGB image, estimates the camera pose and the room layout, and reconstructs both human body and object meshes. By imposing a set of comprehensive and sophisticated losses on all aspects of the estimations, we show that our model outperforms existing human body mesh methods and indoor scene reconstruction methods. To the best of our knowledge, this is the first model that outputs both object and human predictions at the mesh level, and performs joint optimization on the scene and human poses.

关键词： Measurement Solid modeling computer vision Three-dimensional displays Computational modeling Biological system modeling Pose estimation

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Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report

Efficient Deep Models for Real-Time 4K Image Super-Resolutio...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Conde, Marcos V. Zamfir, Eduard Timofte, Radu Motilla, Daniel Liu, Cen Zhang, Zexin Peng, Yunbo Lin, Yue Guo, Jiaming Zou, Xueyi Chen, Yuyi Liu, Yi Hao, Jia Yan, Youliang Zhang, Yuanfan Li, Gen Sun, Lei Kong, Lingshun Bai, Haoran Pan, Jinshan Dong, Jiangxin Tang, Jinhui Ayazoglu, Mustafa Bilecen, Bahri Batuhan Li, Mingxi Zhang, Yuhang Fan, Xianjun Sheng, Yankai Sun, Long Liu, Zibin Gou, Weiran Li, Shaoqing Yi, Ziyao Xiang, Yan Kong, Dehui Xu, Ke Gankhuyag, Ganzorig Yoon, Kihwan Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Zhou, Zhou Chao, Jiahao Gao, Hongfan Gong, Jiali Yang, Zhengfeng Zeng, Zhenbing Chen, Chengpeng Guo, Zichao Park, Anjin Liu, Yuqing Jia, Qi Yu, Hongyuan Yin, Xuanwu Zuo, Kunlong Zhang, Dongyang Fu, Ting Cheng, Zhengxue Zhu, Shiai Zhou, Dajiang Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Shao, Ben Zheng, Shaolong Yin, Daheng Chen, Baijun Liu, Mengyang Nistor, Marian-Sergiu Chen, Yi-Chung Huang, Zhi-Kai Chiang, Yuan-Chun Chen, Wei-Ting Yang, Hao-Hsiang Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Vo, Tu Yan, Qingsen Zhu, Yun Su, Jinqiu Zhang, Yanning Zhang, Cheng Luo, Jiaying Cho, Youngsun Lee, Nakyung Computer Vision Lab CAIDAS IFI University of Würzburg Germany Sony Interactive Entertainment CA United States Huawei Technologies Co. Ltd. China NetEase Games AI Lab Nanjing University of Science and Technology China Tencent China Attrsense Korea Republic of Sanechips Co Ltd Ant Group China East China Normal University China Shopee Dalian University of Technology Xiaomi Inc. China China Zhejiang Dahua Technology Co. Ltd. China Multimedia Department Xiaomi Inc. China Korea Photonic Technology Institute Korea Republic of School of Computer Science and Engineering Southeast University China University Al. I. Cuza Iasi Romania Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Northwestern Polytechnical University China KC Machine Learning Lab CJ OliveNetworks AI Research

ISBN: (纸本)9798350302493

This paper introduces a novel benchmark for efficient up-scaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (×2 and ×3 factors) in real-time on commercial GPUs. For this, we use a new test set containing diverse 4K images ranging from digital art to gaming and photography. We assessed the methods devised for 4K SR by measuring their runtime, parameters, and FLOPs, while ensuring a minimum PSNR fidelity over Bicubic interpolation. Out of the 170 participants, 25 teams contributed to this report, making it the most comprehensive benchmark to date and showcasing the latest advancements in real-time SR. © 2023 ieee.

关键词： Program processors

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Student-Teacher Learning from Clean Inputs to Noisy Inputs

Student-Teacher Learning from Clean Inputs to Noisy Inputs

引用

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

作者： Hong, Guanzhe Mao, Zhiyuan Lin, Xiaojun Chan, Stanley H. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781665445092

Feature-based student-teacher learning, a training method that encourages the student's hidden features to mimic those of the teacher network, is empirically successful in transferring the knowledge from a pre-trained teacher network to the student network. Furthermore, recent empirical results demonstrate that, the teacher's features can boost the student network's generalization even when the student's input sample is corrupted by noise. However, there is a lack of theoretical insights into why and when this method of transferring knowledge can be successful between such heterogeneous tasks. We analyze this method theoretically using deep linear networks, and experimentally using nonlinear networks. We identify three vital factors to the success of the method: (1) whether the student is trained to zero training loss;(2) how knowledgeable the teacher is on the clean-input problem;(3) how the teacher decomposes its knowledge in its hidden features. Lack of proper control in any of the three factors leads to failure of the student-teacher learning method.

关键词： Training Learning systems Knowledge engineering computer vision Systematics Protocols Numerical analysis

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Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing

Exploiting Spatial Dimensions of Latent in GAN for Real-time...

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

作者： Kim, Hyunsu Choi, Yunjey Kim, Junho Yoo, Sungjoo Uh, Youngjung NAVER AI Lab Seoul South Korea Seoul Natl Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665445092

Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embedding through an encoder. We propose StyleMapGAN: the intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN. It makes the embedding through an encoder more accurate than existing optimization-based methods while maintaining the properties of GANs. Experimental results demonstrate that our method significantly outperforms state-of-the-art models in various image manipulation tasks such as local editing and image interpolation. Last but not least, conventional editing methods on GANs are still valid on our StyleMapGAN.

关键词： Interpolation computer vision Codes Computational modeling Modulation Generative adversarial networks Real-time systems

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DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Da...

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

作者： Wolf, Valentin Lugmayr, Andreas Danelljan, Martin Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781665445092

The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications. Current strategies aim to synthesize realistic training data by modeling noise and degradations that appear in real-world settings. We propose DeFlow, a method for learning stochastic image degradations from unpaired data. Our approach is based on a novel unpaired learning formulation for conditional normalizing flows. We model the degradation process in the latent space of a shared flow encoder-decoder network. This allows us to learn the conditional distribution of a noisy image given the clean input by solely minimizing the negative log-likelihood of the marginal distributions. We validate our DeFlow formulation on the task of joint image restoration and super-resolution. The models trained with the synthetic data generated by DeFlow outperform previous learnable approaches on three recent datasets.

关键词： Degradation Superresolution Training data Stochastic processes Data models Image restoration pattern recognition

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Few-shot Open-set recognition by Transformation Consistency

Few-shot Open-set Recognition by Transformation Consistency

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

作者： Jeong, Minki Choi, Seokeon Kim, Changick Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

In this paper, we attack a few-shot open-set recognition (FSOSR) problem, which is a combination of few-shot learning (FSL) and open-set recognition (OSR). It aims to quickly adapt a model to a given small set of labeled samples while rejecting unseen class samples. Since OSR requires rich data and FSL considers closed-set classification, existing OSR and FSL methods show poor performances in solving FSOSR problems. The previous FSOSR method follows the pseudo-unseen class sample-based methods, which collect pseudo-unseen samples from the other dataset or synthesize samples to model unseen class representations. However, this approach is heavily dependent on the composition of the pseudo samples. In this paper, we propose a novel unknown class sample detector, named SnaTCHer, that does not require pseudo-unseen samples. Based on the transformation consistency, our method measures the difference between the transformed prototypes and a modified prototype set. The modified set is composed by replacing a query feature and its predicted class prototype. SnaTCHer rejects samples with large differences to the transformed prototypes. Our method alters the unseen class distribution estimation problem to a relative feature transformation problem, independent of pseudo-unseen class samples. We investigate our SnaTCHer with various prototype transformation methods and observe that our method consistently improves unseen class sample detection performance without closed-set classification reduction.

关键词： Learning systems computer vision Adaptation models Prototypes Estimation Detectors pattern recognition

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VirFace: Enhancing Face recognition via Unlabeled Shallow Data

VirFace: Enhancing Face Recognition via Unlabeled Shallow Da...

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

作者： Li, Wenyu Guo, Tianchu Li, Pengyu Chen, Binghui Wang, Biao Zuo, Wangmeng Zhang, Lei Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Recently, how to exploit unlabeled data for training face recognition models has been attracting increasing attention. However, few works consider the unlabeled shallow data(1) in real-world scenarios. The existing semi-supervised face recognition methods that focus on generating pseudo labels or minimizing softmax classification probabilities of the unlabeled data do not work very well on the unlabeled shallow data. It is still a challenge on how to effectively utilize the unlabeled shallow face data to improve the performance of face recognition. In this paper, we propose a novel face recognition method, named VirFace, to effectively exploit the unlabeled shallow data for face recognition. VirFace consists of VirClass and VirInstance. Specifically, VirClass enlarges the inter-class distance by injecting the unlabeled data as new identities, while VirInstance produces virtual instances sampled from the learned distribution of each identity to further enlarge the inter-class distance. To the best of our knowledge, we are the first to tackle the problem of unlabeled shallow face data. Extensive experiments have been conducted on both the small- and large-scale datasets, e.g. LFW and IJB-C, etc, demonstrating the superiority of the proposed method.

关键词： Training computer vision Face recognition Benchmark testing Data models

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Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps

Guided Interactive Video Object Segmentation Using Reliabili...

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

作者： Heo, Yuk Koh, Yeong Jun Kim, Chang-Su Korea Univ Seoul South Korea Chungnam Natl Univ Daejeon South Korea

ISBN: (纸本)9781665445092

We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagation module to propagate segmentation results to neighboring frames. Third, we introduce the GIS mechanism for a user to select unsatisfactory frames quickly with less e f fort. Experimental results demonstrate that the proposed algorithm provides more accurate segmentation results at a faster speed than conventional algorithms. Codes are available at https://***/yuk6heo/GIS-RAmap.

关键词： computer vision Codes Object segmentation Inspection Reliability engineering Feature extraction Data mining

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