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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

共 52944 条记录，以下是4601-4610 订阅

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Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos

Ego-Exo: Transferring Visual Representations from Third-pers...

引用

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

作者： Li, Yanghao Nagarajan, Tushar Xiong, Bo Grauman, Kristen Facebook AI Res Mountain View CA 94127 USA UT Austin Austin TX USA

ISBN: (纸本)9781665445092

We introduce an approach for pre-training egocentric video models using large-scale third-person video datasets. Learning from purely egocentric data is limited by low dataset scale and diversity, while using purely exocentric (third-person) data introduces a large domain mismatch. Our idea is to discover latent signals in third-person video that are predictive of key egocentric-specific properties. Incorporating these signals as knowledge distillation losses during pre-training results in models that benefit from both the scale and diversity of third-person video data, as well as representations that capture salient egocentric properties. Our experiments show that our "Ego-Exo" framework can be seamlessly integrated into standard video models;it outperforms all baselines when fine-tuned for egocentric activity recognition, achieving state-of-the-art results on Charades-Ego and EPIC-Kitchens-100.

关键词： Training Visualization computer vision Computational modeling Pipelines Activity recognition Data models

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ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows

ArtFlow: Unbiased Image Style Transfer via Reversible Neural...

引用

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

作者： An, Jie Huang, Siyu Song, Yibing Dou, Dejing Liu, Wei Luo, Jiebo Univ Rochester Rochester NY 14627 USA Baidu Res Beijing Peoples R China Tencent AI Lab Shenzhen Peoples R China Tencent Data Platform Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Universal style transfer retains styles from reference images in content images. While existing methods have achieved state-of-the-art style transfer performance, they are not aware of the content leak phenomenon that the image content may corrupt after several rounds of stylization process. In this paper, we propose ArtFlow to prevent content leak during universal style transfer. ArtFlow consists of reversible neural flows and an unbiased feature transfer module. It supports both forward and backward inferences and operates in a projection-transfer-reversion scheme. The forward inference projects input images into deep features, while the backward inference remaps deep features back to input images in a lossless and unbiased way. Extensive experiments demonstrate that ArtFlow achieves comparable performance to state-of-the-art style transfer methods while avoiding content leak.

关键词： computer vision Neural networks Feature extraction Inference algorithms pattern recognition

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Generalized Domain Adaptation

Generalized Domain Adaptation

引用

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

作者： Mitsuzumi, Yu Irie, Go Ikami, Daiki Shibata, Takashi NTT Corp NTT Commun Sci Labs Tokyo Japan

ISBN: (纸本)9781665445092

Many variants of unsupervised domain adaptation (UDA) problems have been proposed and solved individually. Its side effect is that a method that works for one variant is often ineffective for or not even applicable to another, which has prevented practical applications. In this paper, we give a general representation of UDA problems, named Generalized Domain Adaptation (GDA). GDA covers the major variants as special cases, which allows us to organize them in a comprehensive framework. Moreover, this generalization leads to a new challenging setting where existing methods fail, such as when domain labels are unknown, and class labels are only partially given to each domain. We propose a novel approach to the new setting. The key to our approach is self-supervised class-destructive learning, which enables the learning of class-invariant representations and domain-adversarial classifiers without using any domain labels. Extensive experiments using three benchmark datasets demonstrate that our method outperforms the state-of-the-art UDA methods in the new setting and that it is competitive in existing UDA variations as well.

关键词： computer vision Benchmark testing pattern recognition

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Truly shift-invariant convolutional neural networks

Truly shift-invariant convolutional neural networks

引用

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

作者： Chaman, Anadi Dokmanic, Ivan Univ Illinois Champaign IL 61820 USA Univ Basel Basel Switzerland

ISBN: (纸本)9781665445092

Thanks to the use of convolution and pooling layers, convolutional neural networks were for a long time thought to be shift-invariant. However, recent works have shown that the output of a CNN can change significantly with small shifts in input-a problem caused by the presence of downsampling (stride) layers. The existing solutions rely either on data augmentation or on anti-aliasing, both of which have limitations and neither of which enables perfect shift invariance. Additionally, the gains obtained from these methods do not extend to image patterns not seen during training. To address these challenges, we propose adaptive polyphase sampling (APS), a simple sub-sampling scheme that allows convolutional neural networks to achieve 100% consistency in classification performance under shifts, without any loss in accuracy. With APS, the networks exhibit perfect consistency to shifts even before training, making it the first approach that makes convolutional neural networks truly shift-invariant.

关键词： Training computer vision Adaptive systems Convolution pattern recognition Convolutional neural networks

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Generative Interventions for Causal Learning

Generative Interventions for Causal Learning

引用

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

作者： Mao, Chengzhi Cha, Augustine Gupta, Amogh Wang, Hao Yang, Junfeng Vondrick, Carl Columbia Univ New York NY 10027 USA Rutgers State Univ New Brunswick NJ USA

ISBN: (纸本)9781665445092

We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts. Discriminative models often learn naturally occurring spurious correlations, which cause them to fail on images outside of the training distribution. In this paper;we show that we can steer generative models to manufacture interventions on features caused by confounding factors. Experiments, visualizations, and theoretical results show this method learns robust representations more consistent with the underlying causal relationships. Our approach improves performance on multiple datasets demanding out-of-distribution generalization, and we demonstrate state-of-the-art performance generalizing from ImageNet to ObjectNet dataset.

关键词： Training Visualization computer vision Correlation Computational modeling Control systems pattern recognition

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Pedestrian and Ego-vehicle Trajectory Prediction from Monocular Camera

Pedestrian and Ego-vehicle Trajectory Prediction from Monocu...

引用

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

作者： Neumann, Lukas Vedaldi, Andrea Czech Tech Univ Fac Elect Engn Visual Recognit Grp Prague Czech Republic Univ Oxford Dept Engn Sci Visual Geometry Grp Oxford England

ISBN: (纸本)9781665445092

Predicting future pedestrian trajectory is a crucial component of autonomous driving systems, as recognizing critical situations based only on current pedestrian position may come too late for any meaningful corrective action (e.g. breaking) to take place. In this paper, we propose a new method to predict future position of pedestrians, with respect to a predicted future position of the ego-vehicle, thus giving a assistive/autonomous driving system sufficient time to respond. The method explicitly disentangles actual movement of pedestrians in real world from the ego-motion of the vehicle, using a future pose prediction network trained in self-supervised fashion, which allows the method to observe and predict the intrinsic pedestrian motion in a normalised view, that captures the same real-world location across multiple frames. The method is evaluated on two public datasets, where it achieves state-of-the-art results in pedestrian trajectory prediction from an on-board camera.

关键词： Training computer vision Drives Cameras Trajectory pattern recognition Autonomous vehicles

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Points as Queries: Weakly Semi-supervised Object Detection by Points

Points as Queries: Weakly Semi-supervised Object Detection b...

引用

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

作者： Chen, Liangyu Yang, Tong Zhang, Xiangyu Zhang, Wei Sun, Jian MEGVII Technol Beijing Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annotated images by points. It achieves a balance between tremendous annotation burden and detection performance. Based on this setting, we analyze existing detectors and find that these detectors have difficulty in fully exploiting the power of the annotated points. To solve this, we introduce a new detector, Point DETR, which extends DETR by adding a point encoder. Extensive experiments conducted on MS-COCO dataset in various data settings show the effectiveness of our method. In particular, when using 20% fully labeled data from COCO, our detector achieves a promising performance, 33.3 AP, which outperforms a strong baseline (FCOS) by 2.0 AP, and we demonstrate the point annotations bring over 10 points in various AR metrics.

关键词： Measurement computer vision Annotations Detectors Object detection pattern recognition Task analysis

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Look Before you Speak: Visually Contextualized Utterances

Look Before you Speak: Visually Contextualized Utterances

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

作者： Seo, Paul Hongsuck Nagrani, Arsha Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

While most conversational AI systems focus on textual dialogue only, conditioning utterances on visual context (when it's available) can lead to more realistic conversations. Unfortunately, a major challenge for incorporating visual context into conversational dialogue is the lack of large-scale labeled datasets. We provide a solution in the form of a new visually conditioned Future Utterance Prediction task. Our task involves predicting the next utterance in a video, using both visual frames and transcribed speech as context. By exploiting the large number of instructional videos online, we train a model to solve this task at scale, without the need for manual annotations. Leveraging recent advances in multimodal learning, our model consists of a novel co-attentional multimodal video transformer, and when trained on both textual and visual context, outperforms baselines that use textual inputs alone. Further, we demonstrate that our model trained for this task on unlabelled videos achieves state-of-the-art performance on a number of downstream VideoQA benchmarks such as MSRVTT-QA, MSVD-QA, ActivityNet-QA and How2QA.

关键词： Visualization computer vision Annotations Manuals Benchmark testing Transformers pattern recognition

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PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting

PhySG: Inverse Rendering with Spherical Gaussians for Physic...

引用

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

作者： Zhang, Kai Luan, Fujun Wang, Qianqian Bala, Kavita Snavely, Noah Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781665445092

We present PhySG, an end-to-end inverse rendering pipeline that includes a filly differentiable renderer, and can reconstruct geometry, materials, and illumination from scratch from a set of images. Our framework represents specular BRDFs and environmental illumination using mixtures of spherical Gaussians, and represents geometry as a signed distance function parameterized as a Multi-Layer Perceptron. The use of spherical Gaussians allows us to efficiently solve for approximate light transport, and our method works on scenes with challenging non-Lambertian reflectance captured under natural, static illumination. We demonstrate, with both synthetic and real data, that our reconstructions not only enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination.

关键词： Geometry Reflectivity Pipelines Fitting Lighting Rendering (computer graphics) Mathematical models

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Research on Face recognition based on Grey Wolf algorithm optimization 4

Research on Face recognition based on Grey Wolf algorithm op...

引用

4th International conference on computer vision, Image and Deep Learning, CVIDL 2023

作者： Chen, Yuanqiong Wang, Pingping Ling, Jing Wu, Zhengpeng Ding, Lei School of Computer Science and Engineering Jishou University Zhangjiajie427000 China School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9798350326444

Face recognition technology has made significant progress and has been widely applied in various fields due to its advantages of non-contact, non-intrusive, and concurrency, which ensures real-time performance while improving efficiency and accuracy. However, the classic Convolutional Neural Network (CNN) algorithm model used in face recognition lacks adaptability to facial changes, is difficult to handle interference factors, and has limited generalization ability. To address these issues, we propose a CNN prediction model based on the Grey Wolf Optimizer (GWO) algorithm. Compared with traditional optimization algorithms, GWO has faster convergence speed and better global search capability, which avoids falling into local optima and improves the model's generalization ability. Therefore, it can optimize the performance of CNN and improve the accuracy of CNN algorithm models in face recognition tasks. We conducted comparative experiments based on the public dataset CASIA-FaceV5 Dataset. Compared with the classic CNN algorithm, our optimization algorithm in this paper improved the original prediction accuracy from 79.81% to 96%. © 2023 ieee.

关键词： Face recognition

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