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Towards Fair Federated Learning with Zero-Shot Data Augmentation

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

引用

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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BasisNet: Two-stage Model Synthesis for Efficient Inference

BasisNet: Two-stage Model Synthesis for Efficient Inference

引用

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

作者： Zhang, Mingda Chu, Chun-Te Zhmoginov, Andrey Howard, Andrew Jou, Brendan Zhu, Yukun Zhang, Li Hwa, Rebecca Kovashka, Adriana Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665448994

In this work, we present BasisNet which combines recent advancements in efficient neural network architectures, conditional computation, and early termination in a simple new form. Our approach incorporates a lightweight model to preview the input and generate input-dependent combination coefficients, which later controls the synthesis of a more accurate specialist model to make final prediction. The two-stage model synthesis strategy can be applied to any network architectures and both stages are jointly trained. We also show that proper training recipes are critical for increasing generalizability for such high capacity neural networks. On ImageNet classification benchmark, our BasisNet with MobileNets as backbone demonstrated clear advantage on accuracy-efficiency trade-off over several strong baselines. Specifically, BasisNet-MobileNetV3 obtained 80.3% top-1 accuracy with only 290M MultiplyAdd operations, halving the computational cost of previous state-of-the-art without sacrificing accuracy. With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80.0% on ImageNet.

关键词： Training computer vision Computational modeling conferences Neural networks computer architecture Predictive models

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Isolated Sign recognition from RGB Video using Pose Flow and Self-Attention

Isolated Sign Recognition from RGB Video using Pose Flow and...

引用

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

作者： De Coster, Mathieu Van Herreweghe, Mieke Dambre, Joni Univ Ghent IMEC IDLab AIRO Technol Pk Zwijnaarde 126 Ghent Belgium Univ Ghent Blandijnberg 2 Ghent Belgium

ISBN: (纸本)9781665448994

Automatic sign language recognition lies at the intersection of natural language processing (NLP) and computer vision. The highly successful transformer architectures, based on multi-head attention, originate from the field of NLP. The Video Transformer Network (VTN) is an adaptation of this concept for tasks that require video understanding, e.g., action recognition. However, due to the limited amount of labeled data that is commonly available for training automatic sign (language) recognition, the VTN cannot reach its full potential in this domain. In this work, we reduce the impact of this data limitation by automatically pre-extracting useful information from the sign language videos. In our approach, different types of information are offered to a VTN in a multi-modal setup. It includes per-frame human pose keypoints (extracted by OpenPose) to capture the body movement and hand crops to capture the (evolution of) hand shapes. We evaluate our method on the recently released AUTSL dataset for isolated sign recognition and obtain 92.92% accuracy on the test set using only RGB data. For comparison: the VTN architecture without hand crops and pose flow achieved 82% accuracy. A qualitative inspection of our model hints at further potential of multi-modal multi-head attention in a sign language recognition context.

关键词： computer vision Analytical models Shape Assistive technology Computational modeling Gesture recognition computer architecture

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Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

Dynamic Distinction Learning: Adaptive Pseudo Anomalies for ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Demetris Lappas Vasileios Argyriou Dimitrios Makris School of Computer Science and Mathematics Kingston University London UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection, a novel video anomaly detection methodology that combines pseudo-anomalies, dynamic anomaly weighting, and a distinction loss function to improve detection accuracy. By training on pseudo-anomalies, our approach adapts to the variability of normal and anomalous behaviors without fixed anomaly thresholds. Our model showcases superior performance on the Ped2, Avenue and ShanghaiTech datasets, where individual models are tailored for each scene. These achievements highlight DDL’s effectiveness in advancing anomaly detection, offering a scalable and adaptable solution for video surveillance challenges. Our work can be found on: https://***/demetrislappas/***

关键词： Training Adaptation models Technological innovation computer vision Accuracy conferences Refining

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Wide Receptive Field and Channel Attention Network for JPEG Compressed Image Deblurring

Wide Receptive Field and Channel Attention Network for JPEG ...

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

作者： Lee, Donghyeon Lee, Chulhee Kim, Taesung Samsung Elect Hwaseong South Korea Sunmoon Univ Asan South Korea

ISBN: (纸本)9781665448994

A motion blurred image stored in the joint photographic experts group (JPEG) image compression format contains both motion blur and JPEG artifacts. Therefore, it is very difficult to restore the original image from a blurred and JPEG-compressed image. To address this problem, this paper proposes two methods: a wide receptive field and channel attention network (WRCAN), and JPEG autoencoder loss. First, the WRCAN utilizes a large receptive field and considers the interdependencies among channels of a feature map. Second, the proposed JPEG auto-encoder loss enables the WRCAN to learn prior knowledge of JPFG compression artifacts such that the proposed WRCAN can effectively restore the original image from JPEG-compressed images. The proposed methods are evaluated on the JPEG-compressed REDS dataset by participating in the NTIRE 2021 workshop challenges on Image Deblurring Track 2 JPEG artifacts. The WRCAN trained with the proposed loss ranked third with an output of 29.60dB on the REDS test set, indicating that the proposed methods provide state-of-the-art results. The source codes, model, and data are available at https://***/dhyeonlee/WRCAN-PyTorch.

关键词： computer vision Image coding Convolution conferences Transform coding computer architecture Data models

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Less is More: Pursuing the Visual Turing Test with the Kuleshov Effect

Less is More: Pursuing the Visual Turing Test with the Kules...

引用

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

作者： Olague, Gustavo Olague, Matthieu Jacobo-Lopez, Angel R. Ibarra-Vazquez, Gerardo CICESE Res Ctr Ensenada Baja California Mexico Anahuac Univ Queretaro Queretaro Mexico Autonomous Univ San Luis Potosi San Luis Potosi San Luis Potosi Mexico

ISBN: (纸本)9781665448994

The Turing test centers on the idea that if a computer could trick a human into believing that it was human, then the machine was deemed to be intelligent or indistinguishable from people. Designing a visual Turing test involves recognizing objects and their relationships on images and creating a method to derive new concepts from the visual information. Until now, the proposed visual tests heavily use natural language processing to conduct the questionnaire or storytelling. We deviate from the mainstream, and we propose to reframe the visual Turing test through the Kuleshov effect to avoid written or spoken language. The idea resides on elucidating a method that creates the concept of montage synthetically. Like the first days of cinema, we would like to convey messages with the interpretation of image shots that a machine could decipher while comparing it with those scored by humans. The first implementation of this new test uses images from a psychology study where the circumplex model is applied to rate each image. We consider five deep learning methodologies and eight optimizers, and through semiotics, we derive an emotional state in the computer. The results are promising since we confirm that this version of the visual Turing test is challenging as a new research avenue.

关键词： Deep learning Visualization computer vision Image recognition conferences Psychology Motion pictures

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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...

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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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Learned Video Compression with Intra-Guided Enhancement and Implicit Motion Information

Learned Video Compression with Intra-Guided Enhancement and ...

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

作者： Zou, Nannan Zhang, Honglei Cricri, Francesco Tavakoli, Hamed R. Lainema, Jani Aksu, Emre Hannuksela, Miska Rahtu, Esa Nokia Technol Tampere Finland Tampere Univ Tampere Finland

ISBN: (纸本)9781665448994

Although learned approaches to video compression have been proposed with promising results, hand-engineered video codecs are still unbeaten. On the other hand, learned image compression has already surpassed traditional image codecs. In this paper, we propose a learned video compression system that mimics part of the pipeline of traditional codecs while leveraging learned image compression. It comprises two main modules: a learned intra-frame compression module, and a learned inter-frame compression module that is conditioned on intra-coded frames. These modules use separate learned probability models for entropy coding. The intra-frame codec uses a variant of nonlocal attention layers. Regarding the inter-frame codec, we propose an implicit motion information mechanism, and an enhancement of the inter-frame predictions by leveraging the high quality information of intra-coded frames. On the learned probability model side, we propose to use the reference frames as additional conditioning information. We used this system as our submitted entry for the 2021 Challenge on Learned Image Compression (CLIC). In our experiments, we show the effectiveness of our system and its components via a set of ablation studies.

关键词： computer vision Image coding conferences Computational modeling Pipelines MIMICs Video compression

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Efficient Skeleton-Based Action recognition for Real-Time Embedded Systems

Efficient Skeleton-Based Action Recognition for Real-Time Em...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nadhira Noor Fabianaugie Jametoni Jinbeom Kim Hyunsu Hong In Kyu Park Dept. of Electrical and Computer Engineering Inha University Incheon Korea Finedigital Inc Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Action recognition is vital for various real-world applications, yet its implementation on embedded systems or edge devices faces challenges due to limited computing and memory resources. Our goal is to facilitate lightweight action recognition on embedded systems by utilizing skeleton-based techniques, which naturally require less computing and memory resources. To achieve this, we propose innovative methodologies and optimizations tailored for embedded deployment, including post-training quantization, optimized model architectures, and efficient resource utilization. By enabling real-time and lightweight action recognition on resource-constrained embedded systems, our research opens up new possibilities for applications in areas like autonomous surveillance, driving, and indoor safety monitoring.

关键词： Embedded systems Accuracy Quantization (signal) Surveillance Memory management Network architecture Real-time systems

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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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