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UIGR: Unified Interactive Garment Retrieval

UIGR: Unified Interactive Garment Retrieval

引用

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

作者： Han, Xiao He, Sen Zhang, Li Song, Yi-Zhe Xiang, Tao Univ Surrey CVSSP Guildford Surrey England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Interactive garment retrieval (IGR) aims to retrieve a target garment image based on a reference garment image along with user feedback on what to change on the reference garment. Two IGR tasks have been studied extensively: text-guided garment retrieval (TGR) and visually compatible garment retrieval (VCR). The user feedback for the former indicates what semantic attributes to change with the garment category preserved, while the category is the only thing to be changed explicitly for the latter, with an implicit requirement on style preservation. Despite the similarity between these two tasks and the practical need for an efficient system tackling both, they have never been unified and modeled jointly. In this paper, we propose a Unified Interactive Garment Retrieval (UIGR) framework to unify TGR and VCR. To this end, we first contribute a large-scale benchmark suited for both problems. We further propose a strong baseline architecture to integrate TGR and VCR in one model. Extensive experiments suggest that unifying two tasks in one framework is not only more efficient by requiring a single model only, it also leads to better performance. Code and datasets are available at GitHub.

关键词： Computational modeling Clothing Semantics computer architecture Benchmark testing Multitasking pattern recognition

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Phone2Proc: Bringing Robust Robots Into Our Chaotic World

Phone2Proc: Bringing Robust Robots Into Our Chaotic World

引用

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

作者： Deitke, Matt Hendrix, Rose Farhadi, Ali Ehsani, Kiana Kembhavi, Aniruddha Allen Inst AI PRIOR Seattle WA 98103 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798350301298

Training embodied agents in simulation has become mainstream for the embodied AI community. However, these agents often struggle when deployed in the physical world due to their inability to generalize to real-world environments. In this paper, we present Phone2Proc, a method that uses a 10-minute phone scan and conditional procedural generation to create a distribution of training scenes that are semantically similar to the target environment. The generated scenes are conditioned on the wall layout and arrangement of large objects from the scan, while also sampling lighting, clutter, surface textures, and instances of smaller objects with randomized placement and materials. Leveraging just a simple RGB camera, training with Phone2Proc shows massive improvements from 34.7% to 70.7% success rate in sim-to-real ObjectNav performance across a test suite of over 200 trials in diverse real-world environments, including homes, offices, and RoboTHOR. Furthermore, Phone2Proc's diverse distribution of generated scenes makes agents remarkably robust to changes in the real world, such as human movement, object rearrangement, lighting changes, or clutter.

关键词： Embodied vision: Active agents simulation

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Pseudo-label Guided Contrastive Learning for Semi-supervised Medical Image Segmentation

Pseudo-label Guided Contrastive Learning for Semi-supervised...

引用

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

作者： Basak, Hritam Yin, Zhaozheng SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9798350301298

Although recent works in semi-supervised learning (SemiSL) have accomplished significant success in natural image segmentation, the task of learning discriminative representations from limited annotations has been an open problem in medical images. Contrastive Learning (CL) frameworks use the notion of similarity measure which is useful for classification problems, however, they fail to transfer these quality representations for accurate pixel-level segmentation. To this end, we propose a novel semi-supervised patch-based CL framework for medical image segmentation without using any explicit pretext task. We harness the power of both CL and SemiSL, where the pseudo-labels generated from SemiSL aid CL by providing additional guidance, whereas discriminative class information learned in CL leads to accurate multi-class segmentation. Additionally, we formulate a novel loss that synergistically encourages inter-class separability and intraclass compactness among the learned representations. A new inter-patch semantic disparity mapping using average patch entropy is employed for a guided sampling of positives and negatives in the proposed CL framework. Experimental analysis on three publicly available datasets of multiple modalities reveals the superiority of our proposed method as compared to the state-of-the-art methods. Code is available at: GitHub.

关键词： cell microscopy Medical and biological vision

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Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance

Impact of Pseudo Depth on Open World Object Segmentation wit...

引用

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

作者： Schön, Robin Ludwig, Katja Lienhart, Rainer University of Augsburg Machine Learning and Computer Vision Germany

ISBN: (纸本)9798350302493

Pseudo depth maps are depth map predicitions which are used as ground truth during training. In this paper we leverage pseudo depth maps in order to segment objects of classes that have never been seen during training. This renders our object segmentation task an open world task. The pseudo depth maps are generated using pretrained networks, which have either been trained with the full intention to generalize to downstream tasks (LeRes and MiDaS), or which have been trained in an unsupervised fashion on video sequences (MonodepthV2). In order to tell our network which object to segment, we provide the network with a single click on the object's surface on the pseudo depth map of the image as input. We test our approach on two different scenarios: One without the RGB image and one where the RGB image is part of the input. Our results demonstrate a considerably better generalization performance from seen to unseen object types when depth is used. On the Semantic Boundaries Dataset we achieve an improvement from 61.57 to 69.79 IoU score on unseen classes, when only using half of the training classes during training and performing the segmentation on depth maps only. © 2023 ieee.

关键词： Semantics

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DegAE: A New Pretraining Paradigm for Low-level vision

DegAE: A New Pretraining Paradigm for Low-level Vision

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

作者： Liu, Yihao He, Jingwen Gu, Jinjin Kong, Xiangtao Qiao, Yu Dong, Chao Shanghai Artificial Intelligence Lab Shanghai Peoples R China Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350301298

Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these essential questions and establish a new pretraining scheme for low-level vision. Specifically, we examine previous pretraining methods in both high-level and low-level vision, and categorize current low-level vision tasks into two groups based on the difficulty of data acquisition: low-cost and high-cost tasks. Existing literature has mainly focused on pretraining for low-cost tasks, where the observed performance improvement is often limited. However, we argue that pretraining is more significant for highcost tasks, where data acquisition is more challenging. To learn a general low-level vision representation that can improve the performance of various tasks, we propose a new pretraining paradigm called degradation autoencoder (DegAE). DegAE follows the philosophy of designing pretext task for self-supervised pretraining and is elaborately tailored to low-level vision. With DegAE pretraining, SwinIR achieves a 6.88dB performance gain on image dehaze task, while Uformer obtains 3.22dB and 0.54dB improvement on dehaze and derain tasks, respectively.

关键词： Low-level vision

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LSDIR: A Large Scale Dataset for Image Restoration

LSDIR: A Large Scale Dataset for Image Restoration

引用

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

作者： Li, Yawei Zhang, Kai Liang, Jingyun Cao, Jiezhang Liu, Ce Gong, Rui Zhang, Yulun Tang, Hao Liu, Yun Demandolx, Denis Ranjan, Rakesh Timofte, Radu Van Gool, Luc Computer Vision Lab Eth Zürich Switzerland Meta Reality Labs United States University of Würzburg Germany Ku Leuven Belgium

ISBN: (纸本)9798350302493

The aim of this paper is to propose a large scale dataset for image restoration (LSDIR). Recent work in image restoration has been focused on the design of deep neural networks. The datasets used to train these networks 'only' contain some thousands of images, which is still incomparable with the large scale datasets for other vision tasks such as visual recognition and object detection. The small training set limits the performance of image restoration networks. To solve this problem, we collect high-resolution (HR) images from Flickr for image restoration. To ensure the pixel-level quality of the collected dataset, annotators were invited to manually inspect each of the collected image and remove the low-quality ones. The final dataset contains 84,991 high-quality training images, 1,000 validation images, and 1,000 test images. In addition, we showed that the model capacity of large networks could be fully exploited by training on the large scale dataset with significantly increased patch size and prolonged training iterations. The experimental results on image super-resolution (SR), denoising, JPEG deblocking, deblurring, and demosaicking, and real-world SR show that image restoration networks benefit a lot from the large scale dataset. © 2023 ieee.

关键词： Image reconstruction

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Train-Once-for-All Personalization

Train-Once-for-All Personalization

引用

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

作者： Chen, Hong-You Li, Yandong Cui, Yin Zhang, Mingda Chao, Wei-Lun Zhang, Li Ohio State Univ Columbus OH 43210 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

We study the problem of how to train a "personalization-friendly" model such that given only the task descriptions, the model can be adapted to different end-users' needs, e.g., for accurately classifying different subsets of objects. One baseline approach is to train a "generic" model for classifying a wide range of objects, followed by class selection. In our experiments, we however found it suboptimal, perhaps because the model's weights are kept frozen without being personalized. To address this drawback, we propose Train-once-for-All PERsonalization (TAPER), a framework that is trained just once and can later customize a model for different end-users given their task descriptions. TAPER learns a set of "basis" models and a mixer predictor, such that given the task description, the weights (not the predictions!) of the basis models can be on the fly combined into a single "personalized" model. Via extensive experiments on multiple recognition tasks, we show that TAPER consistently outperforms the baseline methods in achieving a higher personalized accuracy. Moreover, we show that TAPER can synthesize a much smaller model to achieve comparable performance to a huge generic model, making it "deployment-friendly" to resource-limited end devices. Interestingly, even without end-users' task descriptions, TAPER can still be specialized to the deployed context based on its past predictions, making it even more "personalization-friendly".

关键词： Efficient and scalable vision

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Self-Calibrated Efficient Transformer for Lightweight Super-Resolution

Self-Calibrated Efficient Transformer for Lightweight Super-...

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

作者： Zou, Wenbin Ye, Tian Zheng, Weixin Zhang, Yunchen Chen, Liang Wu, Yi Fujian Normal Univ Fujian Prov Key Lab Photon Technol Fuzhou Peoples R China Jimei Univ Sch Ocean Informat Engn Xiamen Peoples R China Fuzhou Univ Coll Phys & Informat Engn Fuzhou Peoples R China China Design Grp Co Ltd Nanjing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recently, deep learning has been successfully applied to the single-image super-resolution (SISR) with remarkable performance. However, most existing methods focus on building a more complex network with a large number of layers, which can entail heavy computational costs and memory storage. To address this problem, we present a lightweight Self-Calibrated Efficient Transformer (SCET) network to solve this problem. The architecture of SCET mainly consists of the self-calibrated module and efficient transformer block, where the self-calibrated module adopts the pixel attention mechanism to extract image features effectively. To further exploit the contextual information from features, we employ an efficient transformer to help the network obtain similar features over long distances and thus recover sufficient texture details. We provide comprehensive results on different settings of the overall network. Our proposed method achieves more remarkable performance than baseline methods.

关键词： Deep learning Visualization computer vision conferences Superresolution computer architecture Transformers

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OTB-morph: One-Time Biometrics via Morphing applied to Face Templates

OTB-morph: One-Time Biometrics via Morphing applied to Face ...

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22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Ghafourian, Mahdi Fierrez, Julian Vera-Rodriguez, Ruben Serna, Ignacio Morales, Aythami Univ Autonoma Madrid BiDA Lab Biometr & Data Pattern Analyt Madrid Spain

ISBN: (纸本)9781665458245

Cancelable biometrics refers to a group of techniques in which the biometric inputs are transformed intentionally using a key before processing or storage. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new scheme for cancelable biometrics aimed at protecting the templates against potential attacks, applicable to any biometric-based recognition system. Our proposed scheme is based on timevarying keys obtained from morphing random biometric information. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand against leakage attacks while improving the recognition performance.

关键词： Visualization computer vision Face recognition conferences Aging Servers Cryptography

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MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

MANIQA: Multi-dimension Attention Network for No-Reference I...

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

作者： Yang, Sidi Wu, Tianhe Shi, Shuwei Lao, Shanshan Gong, Yuan Cao, Mingdeng Wang, Jiahao Yang, Yujiu Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception. Unfortunately, existing NR-IQA methods are far from meeting the needs of predicting accurate quality scores on GAN-based distortion images. To this end, we propose Multi-dimension Attention Network for no-reference Image Quality Assessment (MANIQA) to improve the performance on GAN-based distortion. We firstly extract features via ViT, then to strengthen global and local interactions, we propose the Transposed Attention Block (TAB) and the Scale Swin Transformer Block (SSTB). These two modules apply attention mechanisms across the channel and spatial dimension, respectively. In this multi-dimensional manner, the modules cooperatively increase the interaction among different regions of images globally and locally. Finally, a dual branch structure for patch-weighted quality prediction is applied to predict the final score depending on the weight of each patch's score. Experimental results demonstrate that MANIQA outperforms state-of-the-art methods on four standard datasets (LIVE, TID2013, CSIQ, and KADID-10K) by a large margin. Besides, our method ranked first place in the final testing phase of the NTIRE 2022 Perceptual Image Quality Assessment Challenge Track 2: No-Reference. Codes and models are available at https://***/IIGROUP/MANIQA.

关键词： Image quality Databases Distortion Feature extraction Transformers Quality assessment pattern recognition

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