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Learning Furniture Compatibility with Graph Neural Networks

Learning Furniture Compatibility with Graph Neural Networks

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Luisa F. Polanía Mauricio Flores Matthew Nokleby Yiran Li Target Corporation Sunnyvale California USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We propose a graph neural network (GNN) approach to the problem of predicting the stylistic compatibility of a set of furniture items from images. While most existing results are based on siamese networks which evaluate pairwise compatibility between items, the proposed GNN architecture exploits relational information among groups of items. We present two GNN models, both of which comprise a deep CNN that extracts a feature representation for each image, a gated recurrent unit (GRU) network that models interactions between the furniture items in a set, and an aggregation function that calculates the compatibility score. In the first model, a generalized contrastive loss function that promotes the generation of clustered embeddings for items belonging to the same furniture set is introduced. Also, in the first model, the edge function between nodes in the GRU and the aggregation function are fixed in order to limit model complexity and allow training on smaller datasets; in the second model, the edge function and aggregation function are learned directly from the data. We demonstrate state-of-the art accuracy for compatibility prediction and "fill in the blank" tasks on the Bonn and Singapore furniture datasets. We further introduce a new dataset, called the Target Furniture Collections dataset, which contains over 6000 furniture items that have been hand-curated by stylists to make up 1632 compatible sets. We also demonstrate superior prediction accuracy on this dataset.

关键词： Feature extraction Computational modeling Data models Neural networks computer vision Logic gates Task analysis

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Offline Signature Verification on Real-World Documents

Offline Signature Verification on Real-World Documents

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Deniz Engin Alperen Kantarcı Seçil Arslan Hazım Kemal Ekenel Yapı Kredi Technology Istanbul Technical University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Research on offline signature verification has explored a large variety of methods on multiple signature datasets, which are collected under controlled conditions. However, these datasets may not fully reflect the characteristics of the signatures in some practical use cases. Real-world signatures extracted from the formal documents may contain different types of occlusions, for example, stamps, company seals, ruling lines, and signature boxes. Moreover, they may have very high intra-class variations, where even genuine signatures resemble forgeries. In this paper, we address a real-world writer independent offline signature verification problem, in which, a bank's customers' transaction request documents that contain their occluded signatures are compared with their clean reference signatures. Our proposed method consists of two main components, a stamp cleaning method based on CycleGAN and signature representation based on CNNs. We extensively evaluate different verification setups, fine-tuning strategies, and signature representation approaches to have a thorough analysis of the problem. Moreover, we conduct a human evaluation to show the challenging nature of the problem. We run experiments both on our custom dataset, as well as on the publicly available Tobacco-800 dataset. The experimental results validate the difficulty of offline signature verification on real-world documents. However, by employing the stamp cleaning process, we improve the signature verification performance significantly.

关键词： Feature extraction Cleaning Task analysis Authentication Training Mathematical model computer vision

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Improving deep network robustness to unknown inputs with objectosphere 32

Improving deep network robustness to unknown inputs with obj...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Dhamija, Akshay Raj Günther, Manuel Boult, Terrance E. Vision and Security Technology Lab University of Colorado Colorado Springs

ISBN: (纸本)9781728125060

Deep Neural Networks trained on academic datasets often fail when applied to the real world. These failures generally arise from unknown inputs that are not of interest to the system. The mis-classification of these unknown inputs as one of the known classes highlights the need for more robust deep networks. The problem of identifying samples that are not of interest to the system has previously been tackled by either thresholding softmax, which by construction cannot return none of the known classes itself, or by learning new features for the unknown inputs using an additional background or garbage class. As demonstrated, both of these approaches help but are generally insufficient when previously unseen classes are encountered. This paper overviews our recent publication Reducing Network Agnostophobia, NeurIPS 2018. The paper presented two novel loss functions that effectively handle unseen classes while providing a new measure for uncertainty. The ability to identify unknown samples plays a crucial role in developing robust networks that may be used in open-world problems. The paper also introduced an evaluation metric that focused on comparing performance of multiple approaches in an open-set setting. © 2019 ieee computer Society. All rights reserved.

关键词： Deep neural networks

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CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture recognition

CatNet: Class Incremental 3D ConvNets for Lifelong Egocentri...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zhengwei Wang Qi She Tejo Chalasani Aljosa Smolic V-SENSE Trinity College Dublin Intel Labs China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Egocentric gestures are the most natural form of communication for humans to interact with wearable devices such as VR/AR helmets and glasses. A major issue in such scenarios for real-world applications is that may easily become necessary to add new gestures to the system e.g., a proper VR system should allow users to customize gestures incrementally. Traditional deep learning methods require storing all previous class samples in the system and training the model again from scratch by incorporating previous samples and new samples, which costs humongous memory and significantly increases computation over time. In this work, we demonstrate a lifelong 3D convolutional framework - c(C)la(a)ss increment(t)al net(Net)works (CatNet), which considers temporal information in videos and enables life-long learning for egocentric gesture video recognition by learning the feature representation of an exemplar set selected from previous class samples. Importantly, we propose a two-stream CatNet, which deploys RGB and depth modalities to train two separate networks. We evaluate Cat- Nets on a publicly available dataset - EgoGesture dataset, and show that CatNets can learn many classes incrementally over a long period of time. Results also demonstrate that the two-stream architecture achieves the best performance on both joint training and class incremental training compared to 3 other one-stream architectures. The codes and pre-trained models used in this work are provided at https://***/villawang/CatNet.

关键词： Videos Task analysis Three-dimensional displays computer architecture Training Spatiotemporal phenomena Gesture recognition

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NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results

NTIRE 2020 Challenge on Video Quality Mapping: Methods and R...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dario Fuoli Zhiwu Huang Martin Danelljan Radu Timofte Hua Wang Longcun Jin Dewei Su Jing Liu Jaehoon Lee Michal Kudelski Lukasz Bala Dmitry Hrybov Marcin Mozejko Muchen Li Siyao Li Bo Pang Cewu Lu Chao Li Dongliang He Fu Li Shilei Wen Computer Vision Lab ETH Zurich Switzerland School of Software Engineering South China University of Technology Guangdong China Multimedia and Computer Vision Lab East China Normal University Shanghai China Department of Electronics and Computer Engineering Hanyang University Seoul Korea TCL Research Europe Warsaw Poland Machine Vision and Intelligence Group Department of Computer Science Shanghai Jiao Tong University Shanghai China Department of Computer Vision Technology (VIS) Baidu Inc. Beijing China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain. The challenge includes both a supervised track (track 1) and a weakly-supervised track (track 2) for two benchmark datasets. In particular, track 1 offers a new Internet video benchmark, requiring algorithms to learn the map from more compressed videos to less compressed videos in a supervised training manner. In track 2, algorithms are required to learn the quality mapping from one device to another when their quality varies substantially and weakly- aligned video pairs are available. For track 1, in total 7 teams competed in the final test phase, demonstrating novel and effective solutions to the problem. For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.

关键词： Video recording Quality assessment Generative adversarial networks Target tracking Training Cameras Image coding

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The MTA Dataset for Multi Target Multi Camera Pedestrian Tracking by Weighted Distance Aggregation

The MTA Dataset for Multi Target Multi Camera Pedestrian Tra...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Philipp Köhl Andreas Specker Arne Schumann Jürgen Beyerer Fraunhofer IOSB Karlsruhe Germany Vision and Fusion Lab Institute for Anthropomatics and Robotics Karlsruhe Germany

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Existing multi target multi camera tracking (MTMCT) datasets are small in terms of the number of identities and video length. The creation of new real world datasets is hard as privacy has to be guaranteed and the labeling is tedious. Therefore in the scope of this work a mod for GTA V to record a MTMCT dataset has been developed and used to record a simulated MTMCT dataset called Multi Camera Track Auto (MTA). The MTA dataset contains over 2,800 person identities, 6 cameras and a video length of over 100 minutes per camera. Additionally a MTMCT system has been implemented to provide a baseline for the created dataset. The system's pipeline consists of stages for person detection, person re-identification, single camera multi target tracking, track distance calculation, and track association. The track distance calculation comprises a weighted aggregation of the following distances: a single camera time constraint, a multi camera time constraint using overlapping camera areas, an appearance feature distance, a homography matching with pairwise camera homographies, and a linear prediction based on the velocity and the time difference of tracks. When using all partial distances, we were able to surpass the results of state-of-the-art single camera trackers by +13% IDF1 score. The MTA dataset, code, and baselines are available at ***/schuar-iosb/mta-dataset.

关键词： Cameras Target tracking Task analysis Synchronization Games Data privacy

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Minimizing Supervision in Multi-label Categorization

Minimizing Supervision in Multi-label Categorization

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Rajat Munender Varshney Pravendra Singh Vinay P. Namboodiri Department of Computer Science and Engineering IIT Kanpur India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Multiple categories of objects are present in most images. Treating this as a multi-class classification is not justified. We treat this as a multi-label classification problem. In this paper, we further aim to minimize the supervision required for providing supervision in multi-label classification. Specifically, we investigate an effective class of approaches that associate a weak localization with each category either in terms of the bounding box or segmentation mask. Doing so improves the accuracy of multi-label categorization. The approach we adopt is one of active learning, i.e., incrementally selecting a set of samples that need supervision based on the current model, obtaining supervision for these samples, retraining the model with the additional set of supervised samples and proceeding again to select the next set of samples. A crucial concern is the choice of the set of samples. In doing so, we provide a novel insight, and no specific measure succeeds in obtaining a consistently improved selection criterion. We, therefore, provide a selection criterion that consistently improves the overall baseline criterion by choosing the top k set of samples for a varied set of criteria. Using this criterion, we are able to show that we can retain more than 98% of the fully supervised performance with just 20% of samples (and more than 96% using 10%) of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach consistently outperforms all other baseline metrics for all benchmark datasets and model combinations.

关键词： Measurement Task analysis Machine learning Welding Feature extraction Uncertainty Computational modeling

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Multi-view vehicle re-identification using temporal attention model and metadata re-ranking 32

Multi-view vehicle re-identification using temporal attentio...

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32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Huang, Tsung-Wei Cai, Jiarui Yang, Hao Hsu, Hung-Min Hwang, Jenq-Neng Department of Electrical and Computer Engineering University of Washington United States Department of Civil and Environmental Engineering University of Washington United States Research Center for Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)9781728125060

Object re-identification (ReID) is an arduous task which requires matching an object across different non-overlapping camera views. Recently, many researchers are working on person ReID by taking advantages of appearance, human pose, temporal constraints, etc. However, vehicle ReID is even more challenging because vehicles have fewer discriminant features than human due to viewpoint orientation, changes in lighting condition and inter-class similarity. In this paper, we propose a viewpoint-aware temporal attention model for vehicle ReID utilizing deep learning features extracted from consecutive frames with vehicle orientation and metadata attributes (i.e., type, brand, color) being taken into consideration. In addition, re-ranking with soft decision boundary is applied as post-processing for result refinement. The proposed method is evaluated on CVPR AI City Challenge 2019 dataset, achieving mAP of 79.17% with the second place ranking in the competition. © 2019 ieee computer Society. All rights reserved.

关键词： Vehicles

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Deep Transfer Learning For Plant Center Localization

Deep Transfer Learning For Plant Center Localization

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Enyu Cai Sriram Baireddy Changye Yang Melba Crawford Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA School of Civil Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Plant phenotyping focuses on the measurement of plant characteristics throughout the growing season, typically with the goal of evaluating genotypes for plant breeding. Estimating plant location is important for identifying genotypes which have low emergence, which is also related to the environment and management practices such as fertilizer applications. The goal of this paper is to investigate methods that estimate plant locations for afield-based crop using RGB aerial images captured using Unmanned Aerial Vehicles (UAVs). Deep learning approaches provide promising capability for locating plants observed in RGB images, but they require large quantities of labeled data (ground truth) for training. Using a deep learning architecture fine-tuned on a single field or a single type of crop on fields in other geographic areas or with other crops may not have good results. The problem of generating ground truth for each new field is labor-intensive and tedious. In this paper, we propose a method for estimating plant centers by transferring an existing model to a new scenario using limited ground truth data. We describe the use of transfer learning using a model fine-tuned for a single field or a single type of plant on a varied set of similar crops and fields. We show that transfer learning provides promising results for detecting plant locations.

关键词： Training Machine learning Task analysis Agriculture Data models Training data Shape

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A Novel Technique Combining Image Processing, Plant Development Properties, and the Hungarian Algorithm, to Improve Leaf Detection in Maize

A Novel Technique Combining Image Processing, Plant Developm...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nazifa Azam Khan Oliver A. S. Lyon Mark Eramian Ian McQuillan Department of Computer Science University of Saskatchewan Saskatoon SK Canada School of Computing Queen’s University Kingston ON Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Manual determination of plant phenotypic properties such as plant architecture, growth, and health is very time consuming and sometimes destructive. Automatic image analysis has become a popular approach. This research aims to identify the position (and number) of leaves from a temporal sequence of high-quality indoor images consisting of multiple views, focussing in particular of images of maize. The procedure used a segmentation on the images, using the convex hull to pick the best view at each time step, followed by a skeletonization of the corresponding image. To remove skeleton spurs, a discrete skeleton evolution pruning process was applied. Pre-existing statistics regarding maize development was incorporated to help differentiate between true leaves and false leaves. Furthermore, for each time step, leaves were matched to those of the previous and next three days using the graph-theoretic Hungarian algorithm. This matching algorithm can be used to both remove false positives, and also to predict true leaves, even if they were completely occluded from the image itself. The algorithm was evaluated using an open dataset consisting of 13 maize plants across 27 days from two different views. The total number of true leaves from the dataset was 1843, and our proposed techniques detects a total of 1690 leaves including 1674 true leaves, and only 16 false leaves, giving a recall of 90.8%, and a precision of 99.0%.

关键词： Skeleton Image segmentation Topology Prediction algorithms Agriculture computer vision

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