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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是161-170 订阅

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Dual Attention Poser: Dual Path Body Tracking Based on Attention

Dual Attention Poser: Dual Path Body Tracking Based on Atten...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Di, Xinhan Dai, Xiaokun Zhang, Xinkang Chen, Xinrong Fudan Universiry Academy for Engineering&Technology China Deepearthgo Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China

ISBN: (纸本)9798350302493

Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users' virtual representation and experience is limited due to high reconstruction error when simple transformer network architecture is applied. In this paper, we present a novel model, named Dual Attention Poser, which can learn the whole body reconstruction at a high accuracy. The proposed model consists of three key modules. Among them, dual-path attention encoder is designed to extract feature of the sparse signals. Cross attention mixer module enable the fusion of representation in the double path. Attention-gated-mlp decoder is applied to decode the hidden feature from the sparse input through attention gate. Test results on the AMASS dataset show that Dual Attention Poser can reduce the error by up to 18.2% in comparison with the state-of-the-art results. © 2023 ieee.

关键词： Network architecture

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Asynchronous Federated Continual Learning

Asynchronous Federated Continual Learning

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Shenaj, Donald Toldo, Marco Rigon, Alberto Zanuttigh, Pietro University of Padova Italy

ISBN: (纸本)9798350302493

The standard class-incremental continual learning setting assumes a set of tasks seen one after the other in a fixed and pre-defined order. This is not very realistic in federated learning environments where each client works independently in an asynchronous manner getting data for the different tasks in time-frames and orders totally uncorrelated with the other ones. We introduce a novel federated learning setting (AFCL) where the continual learning of multiple tasks happens at each client with different orderings and in asynchronous time slots. We tackle this novel task using prototype-based learning, a representation loss, fractal pre-training, and a modified aggregation policy. Our approach, called FedSpace, effectively tackles this task as shown by the results on the CIFAR-100 dataset using 3 different federated splits with 50, 100, and 500 clients, respectively. The code and federated splits are available at https://***/LTTM/FedSpace. © 2023 ieee.

关键词： computer aided instruction

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Localized Latent Updates for Fine-Tuning vision-Language Models

Localized Latent Updates for Fine-Tuning Vision-Language Mod...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Ibing, Moritz Lim, Isaak Kobbelt, Leif Rwth Aachen University Visual Computing Institute Germany

ISBN: (纸本)9798350302493

Although massive pre-trained vision-language models like CLIP show impressive generalization capabilities for many tasks, still it often remains necessary to fine-tune them for improved performance on specific datasets. When doing so, it is desirable that updating the model is fast and that the model does not lose its capabilities on data outside of the dataset, as is often the case with classical fine-tuning approaches. In this work we suggest a lightweight adapter that only updates the models predictions close to seen datapoints. We demonstrate the effectiveness and speed of this relatively simple approach in the context of few-shot learning, where our results both on classes seen and unseen during training are comparable with or improve on the state of the art. © 2023 ieee.

关键词： computer vision

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Ensemble Spatial and Temporal vision Transformer for Action Units Detection

Ensemble Spatial and Temporal Vision Transformer for Action ...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Tu Vu, Ngoc Thong Huynh, Van Nghia Nguyen, Trong Kim, Soo-Hyung Chonnam National University Department of Ai Convergence Korea Republic of

ISBN: (纸本)9798350302493

Facial Action Units detection (FAUs) represents a fine-grained classification problem that involves identifying different units on the human face, as defined by the Facial Action Coding System. In this paper, we present a simple yet efficient vision Transformer-based approach for addressing the task of Action Units (AU) detection in the context of Affective Behavior Analysis in-the-wild (ABAW) competition. We employ the Video vision Transformer(ViViT) Network to capture the temporal facial change in the video. Besides, to reduce massive size of the vision Transformers model, we replace the ViViT feature extraction layers with the CNN backbone (Regnet). Our model outperform the baseline model of ABAW 2023 challenge [8], with a notable 14% difference in result. Our team has achieved a position within the top 5 teams in the ABAW 2023 competition, scoring slightly below the top three and four teams by a narrow margin of 0.27% and 0.43%, respectively. © 2023 ieee.

关键词： computer vision

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Self-Supervised Normalizing Flows for Image Anomaly Detection and Localization

Self-Supervised Normalizing Flows for Image Anomaly Detectio...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Chiu, Li-Ling Lai, Shang-Hong National Tsing Hua University Department of Computer Science Taiwan

ISBN: (纸本)9798350302493

Image anomaly detection aims to detect out-of-distribution instances. Most existing methods treat anomaly detection as an unsupervised task because anomalous training data and labels are usually scarce or unavailable. Recently, image synthesis has been used to generate anomalous samples which deviate from normal sample distribution for model training. By using the synthesized anomalous training samples, we present a novel self-supervised normalizing flow-based density estimation model, which is trained by maximizing the likelihood of normal images and minimizing the likelihood of synthetic anomalous images. By adding constraints to abnormal samples in our loss function, our model training is focused on normal samples rather than synthetic samples. Moreover, we improve the transformation subnet of the affine coupling layers in our flow-based model by dynamic stacking convolution and self-attention blocks. We evaluate our method on MVTec-AD, BTAD, and DAGM datasets and achieve state-of-the-art performance compared to flow-based and self-supervised methods on both anomaly detection and localization tasks. © 2023 ieee.

关键词： Anomaly detection

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Simulating Task-Free Continual Learning Streams from Existing Datasets

Simulating Task-Free Continual Learning Streams from Existin...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Chrysakis, Aristotelis Moens, Marie-Francine KU Leuven Department of Computer Science Leuven Belgium

ISBN: (纸本)9798350302493

Task-free continual learning is the subfield of machine learning that focuses on learning online from a stream whose distribution changes continuously over time. In contrast, previous works evaluate task-free continual learning using streams with distributions that change not continuously, but only at a few distinct points in time. In order to address the discrepancy between the definition and evaluation of task-free continual learning, we propose a principled algorithm that can permute any labeled dataset into a stream that is continuously nonstationary. We empirically show that the streams generated by our algorithm are less structured than the ones conventionally used in the literature. Moreover, we use our simulated task-free streams to benchmark multiple methods applicable to the task-free setting. We hope that our work will allow other researchers to better evaluate learning performance on continuously nonstationary streams. © 2023 ieee.

关键词： Learning systems

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MARRS: Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation

MARRS: Modern Backbones Assisted Co-training for Rapid and R...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Jain, Saurabh Kumar Das, Sukhendu Indian Institute of Technology Visualization and Perception Lab Department of Computer Science Engineering Madras India

ISBN: (纸本)9798350302493

Semi-Supervised Domain Adaptation (SSDA) aims to develop domain invariant models from scarcely labeled target domain in addition to the fully labeled source domain. Current SSDA works are often applied in conjunction with ResNet34 backbone, which makes them overlook the advantages of utilizing other backbones. Hence, in this paper, we investigate the impact of employing different modern backbones in SSDA and propose a novel solution named Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation (MARRS), that uses discriminative features of two modern backbones for training linear classifiers using the well established co-training framework. To induce diversity among classifiers for effective co-training, we propose a novel module that produces diversity at three levels, namely image, backbone, and feature distribution levels. Experiments reveal that MARRS not only achieves state-of-the-art performance across all popular SSDA datasets, but also drastically cuts the computation time compared to other SSDA approaches, making MARRS a rapid and robust solution for SSDA. We also provide extensive ablation experiments to verify our framework's vitality and primary design choices. © 2023 ieee.

关键词： computer vision

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Ante-Hoc Generation of Task-Agnostic Interpretation Maps

Ante-Hoc Generation of Task-Agnostic Interpretation Maps

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Akash Guna, R.T. Benitez, Raul Sikha, O.K. Amrita School of Engineering Department of Computer Science and Engineering Coimbatore India Universitat Politècnica de Catalunya BarcelonaTech Departament of Automatic Control Spain

ISBN: (纸本)9798350302493

Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more reliable. However, current ante-hoc explanation methods mainly generate inexplicit concept-based explanations tailored to specific tasks. To address these limitations, we propose a task-agnostic ante-hoc framework that can generate interpretation maps to visually explain any CNN. Our framework simultaneously trains the CNN and a weighting network - an explanation generation module. The generated maps are self-explanatory, eliminating the need for manual identification of concepts. We demonstrate that our method can interpret classification, facial landmark detection, and image captioning tasks. We show that our framework is explicit, faithful, and stable through experiments. To the best of our knowledge, this is the first ante-hoc CNN explanation strategy that produces visual explanations generic to CNNs for different tasks. © 2023 ieee.

关键词： computer vision

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EFE: End-to-end Frame-to-Gaze Estimation

EFE: End-to-end Frame-to-Gaze Estimation

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Balim, Haldun Park, Seonwook Wang, Xi Zhang, Xucong Hilliges, Otmar ETH Zürich Department of Computer Science Switzerland Lunit Inc. Delft University of Technology Computer Vision Lab Netherlands

ISBN: (纸本)9798350302493

Despite the recent development of learning-based gaze estimation methods, most methods require one or more eye or face region crops as inputs and produce a gaze direction vector as output. Cropping results in a higher resolution in the eye regions and having fewer confounding factors (such as clothing and hair) is believed to benefit the final model performance. However, this eye/face patch cropping process is expensive, erroneous, and implementation-specific for different methods. In this paper, we propose a frame-to-gaze network that directly predicts both 3D gaze origin and 3D gaze direction from the raw frame out of the camera without any face or eye cropping. Our method demonstrates that direct gaze regression from the raw downscaled frame, from FHD/HD to VGA/HVGA resolution, is possible despite the challenges of having very few pixels in the eye region. The proposed method achieves comparable results to state-of-the-art methods in Point-of-Gaze (PoG) estimation on three public gaze datasets: GazeCapture, MPIIFaceGaze, and EVE, and generalizes well to extreme camera view changes. © 2023 ieee.

关键词： Cameras

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TempT: Temporal consistency for Test-time adaptation

TempT: Temporal consistency for Test-time adaptation

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Mutlu, Onur Cezmi Honarmand, Mohammadmahdi Surabhi, Saimourya Wall, Dennis P. Stanford University United States

ISBN: (纸本)9798350302493

We introduce Temporal consistency for Test-time adaptation (TempT), a novel method for test-time adaptation on videos through the use of temporal coherence of predictions across sequential frames as a self-supervision signal. TempT is an approach with broad potential applications in computer vision tasks, including facial expression recognition (FER) in videos. We evaluate TempT's performance on the AffWild2 dataset. Our approach focuses solely on the unimodal visual aspect of the data and utilizes a popular 2D CNN backbone, in contrast to larger sequential or attention-based models used in other approaches. Our preliminary experimental results demonstrate that TempT has competitive performance compared to the previous years' reported performances, and its efficacy provides a compelling proof-of-concept for its use in various real-world applications. © 2023 ieee.

关键词： computer vision

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