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Fine-Grained Visual Attribute Extraction from Fashion Wear

Fine-Grained Visual Attribute Extraction from Fashion Wear

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

作者： Parekh, Viral Shaik, Karimulla Biswas, Soma Chelliah, Muthusamy Flipkart Internet Private Ltd Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781665448994

Automatically extracting visual attributes for e-commerce data has widespread applications in cataloging, catalogue qualification and enrichment, visual search, etc. Here, we address the task of visual attribute extraction for a highly challenging real-world fashion data from Flipkart catalogue (an Indian e-commerce platform), which is collected from seller uploaded product images. This data not only contains widely varying categories (e.g., shirt, sari, shoes), but also has both coarse-grained (e.g., occasion, top type, sari type) and fine-grained (e.g., neck type, print type) attributes. Training examples available for different attributes are highly imbalanced, making this task even more challenging. To this end, we propose an end-to-end framework which integrates multi-task learning with transformer as an attention module, in addition to handling the data imbalance. The proposed architecture supports multiple attributes across various product categories in a scalable manner. Extensive experiments on the in-house dataset shows effectiveness of the proposed framework in improving performance of the fine-grained attributes by 13% on the baseline across the attributes.

关键词： Visualization computer vision conferences Footwear computer architecture Neck Data mining

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Micro-expression recognition Based on Facial Graph Representation Learning and Facial Action Unit Fusion

Micro-expression Recognition Based on Facial Graph Represent...

引用

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

作者： Lei, Ling Chen, Tong Li, Shigang Li, Jianfeng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China Hiroshima City Univ Grad Sch Informat Sci Hiroshima Japan

ISBN: (纸本)9781665448994

Micro-expressions recognition is a challenge because it involves subtle variations in facial organs. In this paper, first, we propose a novel pipeline to learn a facial graph (nodes and edges) representation to capture these local subtle variations. We express the micro-expressions with multi-patches based on facial landmarks and then stack these patches into channels while using a depthwise convolution (DConv) to learn the features inside the patches, namely, node learning. Then, the encoder of the transformer (ETran) is utilized to learn the relationships between the nodes, namely, edge learning. Based on node and edge learning, a learned facial graph representation is obtained. Second, because the occurrence of an expression is closely bound to action units, we design an AU-GCN to learn the action unit's matrix by embedding and GCN. Finally, we propose a fusion model to introduce the action unit's matrix into the learned facial graph representation. The experiments are comparing with SOTA on various evaluation criteria, including common classifications on CASME II and SAMM datasets, and also conducted following Micro-expression Grand Challenge 2019 protocol.

关键词： computer vision Protocols Convolution Face recognition conferences Pipelines Feature extraction

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Discovering Multi-Hardware Mobile Models via Architecture Search

Discovering Multi-Hardware Mobile Models via Architecture Se...

引用

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

作者： Chu, Grace Arikan, Okan Bender, Gabriel Wang, Weijun Brighton, Achille Kindermans, Pieter-Jan Liu, Hanxiao Akin, Berkin Gupta, Suyog Howard, Andrew Google LLC Mountain View CA 94043 USA

ISBN: (纸本)9781665448994

Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored. In this paper, we argue that, for applications that may be deployed on multiple hardware, having different single-hardware models across the deployed hardware makes it hard to guarantee consistent outputs across hardware and duplicates engineering work for debugging and fixing. To minimize such deployment cost, we propose an alternative solution, multi-hardware models, where a single architecture is developed for multiple hardware. With thoughtful search space design and incorporating the proposed multi-hardware metrics in neural architecture search, we discover multi-hardware models that give state-of-the-art (SoTA) performance across multiple hardware in both average and worse case scenarios. For performance on individual hardware, the single multi-hardware model yields similar or better results than SoTA performance on accelerators like GPU, DSP and EdgeTPU which was achieved by different models, while having similar performance with MobilenetV3 Large Minimalistic model on mobile CPU.(1)

关键词： computer vision conferences Graphics processing units Focusing computer architecture Debugging Extraterrestrial measurements

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vision-based Neural Scene Representations for Spacecraft

Vision-based Neural Scene Representations for Spacecraft

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

作者： Mergy, Anne Lecuyer, Gurvan Derksen, Dawa Izzo, Dario European Space Agcy Noordwijk NL-2201 AZ Noordwijk Netherlands

ISBN: (纸本)9781665448994

In advanced mission concepts with high levels of autonomy, spacecraft need to internally model the pose and shape of nearby orbiting objects. Recent works in neural scene representations show promising results for inferring generic three-dimensional scenes from optical images. Neural Radiance Fields (NeRF) have shown success in rendering highly specular surfaces using a large number of images and their pose. More recently, Generative Radiance Fields (GRAF) achieved full volumetric reconstruction of a scene from unposed images only, thanks to the use of an adversarial framework to train a NeRF. In this paper, we compare and evaluate the potential of NeRF and GRAF to render novel views and extract the 3D shape of two different spacecraft, the Soil Moisture and Ocean Salinity satellite of ESA's Living Planet Programme and a generic cube sat. Considering the best performances of both models, we observe that NeRF has the ability to render more accurate images regarding the material specularity of the spacecraft and its pose. For its part, GRAF generates precise novel views with accurate details even when parts of the satellites are shadowed while having the significant advantage of not needing any information about the relative pose.

关键词： Space vehicles computer vision Surface reconstruction Sea surface Satellites Three-dimensional displays Shape

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ChaLearn LAP Large Scale Signer Independent Isolated Sign Language recognition Challenge: Design, Results and Future Research

ChaLearn LAP Large Scale Signer Independent Isolated Sign La...

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

作者： Sincan, Ozge Mercanoglu Jacques Junior, Julio C. S. Escalera, Sergio Keles, Hacer Yalim Ankara Univ Ankara Turkey Comp Vis Ctr Barcelona Spain Univ Barcelona Barcelona Spain

ISBN: (纸本)9781665448994

The performances of Sign Language recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at cvpr 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories.

关键词： computer vision Tracking Assistive technology Computational modeling Transfer learning Semantics Gesture recognition

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Class-Incremental Experience Replay for Continual Learning under Concept Drift

Class-Incremental Experience Replay for Continual Learning u...

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

作者： Korycki, Lukasz Krawczyk, Bartosz Virginia Commonwealth Univ Dept Comp Sci Richmond VA 23284 USA

ISBN: (纸本)9781665448994

Modern machine learning systems need to be able to cope with constantly arriving and changing data. Two main areas of research dealing with such scenarios are continual learning and data stream mining. Continual learning focuses on accumulating knowledge and avoiding forgetting, assuming information once learned should be stored. Data stream mining focuses on adaptation to concept drift and discarding outdated information, assuming that only the most recent data is relevant. While these two areas are mainly being developed in separation, they offer complementary views on the problem of learning from dynamic data. There is a need for unifying them, by offering architectures capable of both learning and storing new information, as well as revisiting and adapting to changes in previously seen concepts. We propose a novel continual learning approach that can handle both tasks. Our experience replay method is fueled by a centroid-driven memory storing diverse instances of incrementally arriving classes. This is enhanced with a reactive subspace buffer that tracks concept drift occurrences in previously seen classes and adapts clusters accordingly. The proposed architecture is thus capable of both remembering valid and forgetting outdated information, offering a holistic framework for continual learning under concept drift.

关键词： computer vision conferences computer architecture Machine learning Data mining Task analysis

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Adversarial Threats to DeepFake Detection: A Practical Perspective

Adversarial Threats to DeepFake Detection: A Practical Persp...

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

作者： Neekhara, Paarth Dolhansky, Brian Bitton, Joanna Ferrer, Cristian Canton Univ Calif San Diego La Jolla CA 92093 USA Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781665448994

Facially manipulated images and videos or DeepFakes can be used maliciously to fuel misinformation or defame individuals. Therefore, detecting DeepFakes is crucial to increase the credibility of social media platforms and other media sharing web sites. State-of-the art DeepFake detection techniques rely on neural network based classification models which are known to be vulnerable to adversarial examples. In this work, we study the vulnerabilities of state-of-the-art DeepFake detection methods from a practical stand point. We perform adversarial attacks on DeepFake detectors in a black box setting where the adversary does not have complete knowledge of the classification models. We study the extent to which adversarial perturbations transfer across different models and propose techniques to improve the transferability of adversarial examples. We also create more accessible attacks using Universal Adversarial Perturbations which pose a very feasible attack scenario since they can be easily shared amongst attackers. We perform our evaluations on the winning entries of the DeepFake Detection Challenge (DFDC) and demonstrate that they can be easily bypassed in a practical attack scenario by designing transferable and accessible adversarial attacks.(1)

关键词： Social networking (online) Perturbation methods Neural networks Multimedia Web sites Detectors pattern recognition Security

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Towards Fair Federated Learning with Zero-Shot Data Augmentation

Towards Fair Federated Learning with Zero-Shot Data Augmenta...

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

作者： Hao, Weituo El-Khamy, Mostafa Lee, Jungwon Zhang, Jianyi Liang, Kevin J. Chen, Changyou Carin, Lawrence Duke Univ Durham NC 27706 USA Samsung Suwon South Korea SUNY Buffalo Buffalo NY USA

ISBN: (纸本)9781665448994

Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models, while having no access to the client data. Although it is recognized that statistical heterogeneity of the client local data yields slower global model convergence, it is less commonly recognized that it also yields a biased federated global model with a high variance of accuracy across clients. In this work, we aim to provide federated learning schemes with improved fairness. To tackle this challenge, we propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity, and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server). Empirical results on a suite of datasets demonstrate the effectiveness of our methods on simultaneously improving the test accuracy and fairness.

关键词： computer vision computer aided instruction Distance learning conferences Distributed databases Collaborative work Data models

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PupilTAN: A Few-Shot Adversarial Pupil Localizer

PupilTAN: A Few-Shot Adversarial Pupil Localizer

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

作者： Poulopoulos, Nikolaos Psarakis, Emmanouil Z. Kosmopoulos, Dimitrios Univ Patras Comp Engn & Informat Patras Greece

ISBN: (纸本)9781665448994

The eye center localization is a challenging problem faced by many computer vision applications. The challenges typically stem from the scene variability, such as, the wide range of shapes, the lighting conditions, the view angles and the occlusions. Nowadays, the increasing interest on deep neural networks requires a large volume of training data. However, a significant issue is the dependency on labeled data, which are expensive to obtain and susceptible to errors. To address these issues, we propose a deep network, dubbed PupilTAN, that performs image-to-heatmap Translation and an Adversarial training framework that solves the eye localization problem in a few-shot unsupervised way. The key idea is to estimate, by using only a few ground-truth shots, the heatmaps centers' pdf and use it as a generator to create random heatmaps that follow the same probability distribution of the real ones. We showcase that training the deep network with these artificial heatmaps in an adversarial framework not only makes us less dependent on labeled data, but also leads to a significant accuracy improvement. The proposed network achieves real-time performance in a general-purpose computer environment and improves the state-of-the-art accuracy for both MUCT and BioID datasets, even compared with supervised techniques. Furthermore, our model is robust even in the case of reducing its size of up to 1/16 of the original network (0.2M parameters), demonstrating comparable accuracy to the state-of-the-art with high practical value to real-time applications.

关键词： Location awareness Heating systems Training computer vision Shape Training data Real-time systems

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QFabric: Multi-Task Change Detection Dataset

QFabric: Multi-Task Change Detection Dataset

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

作者： Verma, Sagar Panigrahi, Akash Gupta, Siddharth Univ Paris Saclay INRIA Cent Supelec Ctr Vis Numer Paris France Granular AI Somerville NJ USA

ISBN: (纸本)9781665448994

Detecting change through multi-image, multi-date remote sensing is essential to developing and understanding of global conditions. Despite recent advancements in remote sensing realized through deep learning, novel methods for accurate multi-image change detection remain unrealized. Recently, several promising methods have been proposed to address this topic, but a paucity of publicly available data limits the methods that can be assessed. In particular, there exists limited work on categorizing the nature and status of change across an observation period. This paper introduces the first labeled dataset available for such a task. We present an open-source change detection dataset, termed QFabric, with 450,000 change polygons annotated across 504 locations in 100 different cities covering a wide range of geographies and urban fabrics. QFabric is a temporal multi-task dataset with 6 change types and 9 change status classes. The geography and environment metadata around each polygon provides context that can be leveraged to build robust deep neural networks. We apply multiple benchmarks on our dataset for change detection, change type and status classification tasks. Project page: https://***/qfabric

关键词： Deep learning Urban areas Predictive models Benchmark testing Metadata Fabrics pattern recognition

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