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

共 30983 条记录，以下是4601-4610 订阅

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Learning to Select Views for Efficient Multi-View Understanding

Learning to Select Views for Efficient Multi-View Understand...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yunzhong Hou Stephen Gould Liang Zheng Australian National University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Multiple camera view (multi-view) setups have proven useful in many computer vision applications. However, the high computational cost associated with multiple views creates a significant challenge for end devices with limited computational resources. In modern CPU, pipelining breaks a longer job into steps and enables parallelism over sequential steps from multiple jobs. Inspired by this, we study selective view pipelining for efficient multi-view understanding, which breaks computation of multiple views into steps, and only computes the most helpful views/steps in a parallel manner for the best efficiency. To this end, we use reinforcement learning to learn a very light view selection module that analyzes the target object or scenario from initial views and selects the next-best-view for recognition or detection for pipeline computation. Experimental results on multi-view classification and detection tasks show that our approach achieves promising performance while using only 2 or 3 out of N available views, significantly reducing computational costs while maintaining parallelism over GPU through selective view pipelining 1 1 Code available at https://***/hou-yz/MVSelect.

关键词： Training computer vision Image resolution Target recognition Layout Reinforcement learning Cameras

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Information Bottleneck Disentanglement for Identity Swapping

Information Bottleneck Disentanglement for Identity Swapping

引用

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

作者： Gao, Gege Huang, Huaibo Fu, Chaoyou Li, Zhaoyang He, Ran Univ Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Sch Artificial Intelligence Natl Lab Pattern RecognitCASIACAS Beijing Peoples R China

ISBN: (纸本)9781665445092

Improving the performance of face forgery detectors often requires more identity-swapped images of higher-quality. One core objective of identity swapping is to generate identity-discriminative faces that are distinct from the target while identical to the source. To this end, properly disentangling identity and identity-irrelevant information is critical and remains a challenging endeavor. In this work, we propose a novel information disentangling and swapping network, called InfoSwap, to extract the most expressive information for identity representation from a pre-trained face recognition model. The key insight of our method is to formulate the learning of disentangled representations as optimizing an information bottleneck tradeoff, in terms of finding an optimal compression of the pretrained latent features. Moreover, a novel identity contrastive loss is proposed for further disentanglement by requiring a proper distance between the generated identity and the target. While the most prior works have focused on using various loss functions to implicitly guide the learning of representations, we demonstrate that our model can provide explicit supervision for learning disentangled representations, achieving impressive performance in generating more identity-discriminative swapped faces.

关键词： computer vision Face recognition Buildings Detectors Benchmark testing Feature extraction Forgery

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General Instance Distillation for Object Detection

General Instance Distillation for Object Detection

引用

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

作者： Dai, Xing Jiang, Zeren Wu, Zhao Bao, Yiping Wang, Zhicheng Liu, Si Zhou, Erjin MEGVII Technol Beijing Peoples R China BeiHang Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

In recent years, knowledge distillation has been proved to be an effective solution for model compression. This approach can make lightweight student models acquire the knowledge extracted from cumbersome teacher models. However, previous distillation methods of detection have weak generalization for different detection frameworks and rely heavily on ground truth (GT), ignoring the valuable relation information between instances. Thus, we propose a novel distillation method for detection tasks based on discriminative instances without considering the positive or negative distinguished by GT, which is called general instance distillation (GID). Our approach contains a general instance selection module (GISM) to make full use of feature-based, relation-based and response-based knowledge for distillation. Extensive results demonstrate that the student model achieves significant AP improvement and even outperforms the teacher in various detection frameworks. Specifically, RetinaNet with ResNet-50 achieves 39.1% in mAP with GID on COCO dataset, which surpasses the baseline 36.2% by 2.9%, and even better than the ResNet-101 based teacher model with 38.1% AP.

关键词： computer vision Adaptation models Object detection Feature extraction pattern recognition Task analysis

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Global2Local: Efficient Structure Search for Video Action Segmentation

Global2Local: Efficient Structure Search for Video Action Se...

引用

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

作者： Gao, Shang-Hua Han, Qi Li, Zhong-Yu Peng, Pai Wang, Liang Cheng, Ming-Ming Nankai Univ TKLNDST CS Tianjin Peoples R China Tencent Shenzhen Peoples R China NLPR Beijing Peoples R China

ISBN: (纸本)9781665445092

Temporal receptive fields of models play an important role in action segmentation. Large receptive fields facilitate the long-term relations among video clips while small receptive fields help capture the local details. Existing methods construct models with hand-designed receptive fields in layers. Can we effectively search for receptive field combinations to replace hand-designed patterns? To answer this question, we propose to find better receptive field combinations through a global-to-local search scheme. Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combination patterns further. The global search finds possible coarse combinations other than human-designed patterns. On top of the global search, we propose an expectation guided iterative local search scheme to refine combinations effectively. Our global-to-local search can be plugged into existing action segmentation methods to achieve state-of-the-art performance.

关键词： computer vision Codes Probabilistic logic pattern recognition Task analysis Forecasting

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What If We Only Use Real Datasets for Scene Text recognition? Toward Scene Text recognition With Fewer Labels

What If We Only Use Real Datasets for Scene Text Recognition...

引用

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

作者： Baek, Jeonghun Matsui, Yusuke Aizawa, Kiyoharu Univ Tokyo Tokyo Japan

ISBN: (纸本)9781665445092

Scene text recognition (STR) task has a common practice: All state-of-the-art STR models are trained on large synthetic data. In contrast to this practice, training STR models only on fewer real labels (STR with fewer labels) is important when we have to train STR models without synthetic data: for handwritten or artistic texts that are difficult to generate synthetically and for languages other than English for which we do not always have synthetic data. However, there has been implicit common knowledge that training STR models on real data is nearly impossible because real data is insufficient. We consider that this common knowledge has obstructed the study of STR with fewer labels. In this work, we would like to reactivate STR with fewer labels by disproving the common knowledge. We consolidate recently accumulated public real data and show that we can train STR models satisfactorily only with real labeled data. Subsequently, we find simple data augmentation to fully exploit real data. Furthermore, we improve the models by collecting unlabeled data and introducing semi- and self-supervised methods. As a result, we obtain a competitive model to state-of-the-art methods. To the best of our knowledge, this is the first study that 1) shows sufficient performance by only using real labels and 2) introduces semi- and self-supervised methods into STR with fewer labels.

关键词： Training computer vision Codes Text recognition Data models Task analysis

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Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaption

Rethinking Ensemble-Distillation for Semantic Segmentation B...

引用

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

作者： Chao, Chen-Hao Cheng, Bo-Wun Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan

ISBN: (纸本)9781665448994

Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks. Nevertheless, these end-to-end ensemble learning methods often lack flexibility as any modification to the ensemble requires retraining of their frameworks. To address this problem, we propose a flexible ensemble-distillation framework for performing semantic segmentation based UDA, allowing any arbitrary composition of the members in the ensemble while still maintaining its superior performance. To achieve such flexibility, our framework is designed to be robust against the output inconsistency and the performance variation of the members within the ensemble. To examine the effectiveness and the robustness of our method, we perform an extensive set of experiments on both GTA5 -> Cityscapes and SYNTHIA -> Cityscapes benchmarks to quantitatively inspect the improvements achievable by our method. We further provide detailed analyses to validate that our design choices are practical and beneficial. The experimental evidence validates that the proposed method indeed offer superior performance, robustness and flexibility in semantic segmentation based UDA tasks against contemporary baseline methods.

关键词： Learning systems computer vision conferences Semantics Benchmark testing Robustness pattern recognition

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Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising

Recorrupted-to-Recorrupted: Unsupervised Deep Learning for I...

引用

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

作者： Pang, Tongyao Zheng, Huan Quan, Yuhui Ji, Hui Natl Univ Singapore Dept Math Singapore 119076 Singapore South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

ISBN: (纸本)9781665445092

Deep denoiser, the deep network for denoising, has been the focus of the recent development on image denoising. In the last few years, there is an increasing interest in developing unsupervised deep denoisers which only call unorganized noisy images without ground truth for training. Nevertheless, the performance of these unsupervised deep denoisers is not competitive to their supervised counterparts. Aiming at developing a more powerful unsupervised deep denoiser, this paper proposed a data augmentation technique, called recorrupted-to-recorrupted (R2R), to address the overfitting caused by the absence of truth images. For each noisy image, we showed that the cost function defined on the noisy/noisy image pairs constructed by the R2R method is statistically equivalent to its supervised counterpart defined on the noisy/truth image pairs. Extensive experiments showed that the proposed R2R method noticeably outperformed existing unsupervised deep denoisers, and is competitive to representative supervised deep denoisers.

关键词： Training Deep learning computer vision AWGN Noise reduction Cost function pattern recognition

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StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

StyleSpace Analysis: Disentangled Controls for StyleGAN Imag...

引用

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

作者： Wu, Zongze Lischinski, Dani Shechtman, Eli Hebrew Univ Jerusalem Jerusalem Israel Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works. Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and disentangled manner. Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images. Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works. To show this, we make use of a newly proposed Attribute Dependency metric. Finally, we demonstrate the applicability of StyleSpace controls to the manipulation of real images. Our findings pave the way to semantically meaningful and well-disentangled image manipulations via simple and intuitive interfaces.

关键词： Measurement Visualization computer vision Analytical models Image synthesis computer architecture Aerospace electronics

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One-shot action recognition in challenging therapy scenarios

One-shot action recognition in challenging therapy scenarios

引用

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

作者： Sabater, Alberto Santos, Laura Santos-Victor, Jose Bernardino, Alexandre Montesano, Luis Murillo, Ana C. Univ Lisbon Inst Super Tecn ISR Lisboa Lisbon Portugal Univ Zaragoza DIIS I3A Zaragoza Spain Bitbrain Technol Zaragoza Spain Politecn Milan Milan Italy

ISBN: (纸本)9781665448994

One-shot action recognition aims to recognize new action categories from a single reference example, typically referred to as the anchor example. This work presents a novel approach for one-shot action recognition in the wild that computes motion representations robust to variable kinematic conditions. One-shot action recognition is then performed by evaluating anchor and target motion representations. We also develop a set of complementary steps that boost the action recognition performance in the most challenging scenarios. Our approach is evaluated on the public NTU-120 one-shot action recognition benchmark, outperforming previous action recognition models. Besides, we evaluate our framework on a real use-case of therapy with autistic people. These recordings are particularly challenging due to high-level artifacts from the patient motion. Our results provide not only quantitative but also online qualitative measures, essential for the patient evaluation and monitoring during the actual therapy.

关键词： Target recognition conferences Dynamics Medical treatment Kinematics pattern recognition Noise measurement

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Gated Spatio-Temporal Attention-Guided Video Deblurring

Gated Spatio-Temporal Attention-Guided Video Deblurring

引用

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

作者： Suin, Maitreya Rajagopalan, A. N. Indian Inst Technol Madras Chennai Tamil Nadu India

ISBN: (纸本)9781665445092

Video deblurring remains a challenging task due to the complexity of spatially and temporally varying blur. Most of the existing works depend on implicit or explicit alignment for temporal information fusion, which either increases the computational cost or results in suboptimal performance due to misalignment. In this work, we investigate two key factors responsible for deblurring quality: how to fuse spatio-temporal information and from where to collect it. We propose a factorized gated spatio-temporal attention module to perform non-local operations across space and time to fully utilize the available information without depending on alignment. First, we perform spatial aggregation followed by a temporal aggregation step. Next, we adaptively distribute the global spatio-temporal information to each pixel. It shows superior performance compared to existing non-local fusion techniques while being considerably more efficient. To complement the attention module, we propose a reinforcement learning-based framework for selecting keyframes from the neighborhood with the most complementary and useful information. Moreover, our adaptive approach can increase or decrease the frame usage at inference time, depending on the user's need. Extensive experiments on multiple datasets demonstrate the superiority of our method.

关键词： computer vision Adaptation models Fuses Computational modeling Logic gates pattern recognition Computational efficiency

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