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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21010 条记录，以下是1501-1510 订阅

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On the Road to Online Adaptation for Semantic Image Segmentation

On the Road to Online Adaptation for Semantic Image Segmenta...

引用

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

作者： Volpi, Riccardo De Jorge, Pau Larlus, Diane Csurka, Gabriela NAVER LABS Europe Meylan France Univ Oxford Oxford England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a new problem formulation and a corresponding evaluation framework to advance research on unsupervised domain adaptation for semantic image segmentation. The overall goal is fostering the development of adaptive learning systems that will continuously learn, without supervision, in ever-changing environments. Typical protocols that study adaptation algorithms for segmentation models are limited to few domains, adaptation happens offline, and human intervention is generally required, at least to annotate data for hyper-parameter tuning. We argue that such constraints are incompatible with algorithms that can continuously adapt to different real-world situations. To address this, we propose a protocol where models need to learn online, from sequences of temporally correlated images, requiring continuous, frame-by-frame adaptation. We accompany this new protocol with a variety of baselines to tackle the proposed formulation, as well as an extensive analysis of their behaviors, which can serve as a starting point for future research.

关键词： Representation learning Image segmentation Adaptation models computer vision Protocols Roads Semantics

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Hallucinated Neural Radiance Fields in the Wild

Hallucinated Neural Radiance Fields in the Wild

引用

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

作者： Chen, Xingyu Zhang, Qi Li, Xiaoyu Chen, Yue Feng, Ying Wang, Xuan Wang, Jue Xi An Jiao Tong Univ Xian Shaanxi Peoples R China Tencent AI Lab Bellevue WA 98004 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Neural Radiance Fields (NeRF) has recently gained popularity for its impressive novel view synthesis ability. This paper studies the problem of hallucinated NeRF: i.e., recovering a realistic NeRF at a different time of day from a group of tourism images. Existing solutions adopt NeRF with a controllable appearance embedding to render novel views under various conditions, but they cannot render view-consistent images with an unseen appearance. To solve this problem, we present an end-to-end framework for constructing a hallucinated NeRF, dubbed as Ha-NeRF. Specifically, we propose an appearance hallucination module to handle time-varying appearances and transfer them to novel views. Considering the complex occlusions of tourism images, we introduce an anti-occlusion module to decompose the static subjects for visibility accurately. Experimental results on synthetic data and real tourism photo collections demonstrate that our method can hallucinate the desired appearances and render occlusion-free images from different views.

关键词： computer vision Codes pattern recognition

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End-to-end Generative Pretraining for Multimodal Video Captioning

End-to-end Generative Pretraining for Multimodal Video Capti...

引用

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

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

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent video and language pretraining frameworks lack the ability to generate sentences. We present Multimodal Video Generative Pretraining (MV-GPT), a new pretraining framework for learning from unlabelled videos which can be effectively used for generative tasks such as multimodal video captioning. Unlike recent video-language pretraining frameworks, our framework trains both a multimodal video encoder and a sentence decoder jointly. To overcome the lack of captions in unlabelled videos, we leverage the future utterance as an additional text source and propose a bidirectional generation objective-we generate future utterances given the present mulitmodal context, and also the present utterance given future observations. With this objective, we train an encoder-decoder model end-to-end to generate a caption from raw pixels and transcribed speech directly. Our model achieves state-of-the-art performance for multimodal video captioning on four standard benchmarks, as well as for other video understanding tasks such as VideoQA, video retrieval and action classification.

关键词： Representation learning computer vision Computational modeling Bidirectional control Benchmark testing Decoding pattern recognition

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Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction

Hierarchical Nearest Neighbor Graph Embedding for Efficient ...

引用

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

作者： Sarfraz, M. Saquib Koulakis, Marios Seibold, Constantin Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany Mercedes Benz Tech Innovat Stuttgart Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Dimensionality reduction is crucial both for visualization and preprocessing high dimensional data for machine learning. We introduce a novel method based on a hierarchy built on 1-nearest neighbor graphs in the original space which is used to preserve the grouping properties of the data distribution on multiple levels. The core of the proposal is an optimization-free projection that is competitive with the latest versions of t-SNE and UMAP in performance and visualization quality while being an order of magnitude faster at run-time. Furthermore, its interpretable mechanics, the ability to project new data, and the natural separation of data clusters in visualizations make it a general purpose unsupervised dimension reduction technique. In the paper, we argue about the soundness of the proposed method and evaluate it on a diverse collection of datasets with sizes varying from 1K to 11M samples and dimensions from 28 to 16K. We perform comparisons with other state-of-the-art methods on multiple metrics and target dimensions highlighting its efficiency and performance.

关键词： Dimensionality reduction Measurement computer vision Codes Data visualization pattern recognition Proposals

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SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention

SASIC: Stereo Image Compression with Latent Shifts and Stere...

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

作者： Woedlinger, Matthias Kotera, Jan Xu, Jan Sablatnig, Robert TU Wien Vienna Austria Deep Render London England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a learned method for stereo image compression that leverages the similarity of the left and right images in a stereo pair due to overlapping fields of view. The left image is compressed by a learned compression method based on an autoencoder with a hyperprior entropy model. The right image uses this information from the previously encoded left image in both the encoding and decoding stages. In particular, for the right image, we encode only the residual of its latent representation to the optimally shifted latent of the left image. On top of that, we also employ a stereo attention module to connect left and right images during decoding. The performance of the proposed method is evaluated on two benchmark stereo image datasets (Cityscapes and InStereo2K) and outperforms previous stereo image compression methods while being significantly smaller in model size.

关键词： computer vision Image coding Codes Computational modeling Bit rate Performance gain Benchmark testing

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Learning Pixel Trajectories with Multiscale Contrastive Random Walks

Learning Pixel Trajectories with Multiscale Contrastive Rand...

引用

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

作者： Bian, Zhangxing Jabri, Allan Efros, Alexei A. Owens, Andrew Univ Michigan Ann Arbor MI 48109 USA Univ Calif Berkeley Berkeley CA USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A range of video modeling tasks, from optical flow to multiple object tracking, share the same fundamental challenge: establishing space-time correspondence. Yet, approaches that dominate each space differ. We take a step towards bridging this gap by extending the recent contrastive random walk formulation to much denser, pixel-level space-time graphs. The main contribution is introducing hierarchy into the search problem by computing the transition matrix between two frames in a coarse-to-fine manner, forming a multiscale contrastive random walk when extended in time. This establishes a unified technique for self-supervised learning of optical flow, keypoint tracking, and video object segmentation. Experiments demonstrate that, for each of these tasks, the unified model achieves performance competitive with strong self-supervised approaches specific to that task.(1)

关键词： computer vision Computational modeling Self-supervised learning Object segmentation Search problems Trajectory pattern recognition

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Plenoxels: Radiance Fields without Neural Networks

Plenoxels: Radiance Fields without Neural Networks

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

作者： Fridovich-Keil, Sara Yu, Alex Tancik, Matthew Chen, Qinhong Recht, Benjamin Kanazawa, Angjoo Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We introduce Plenoxels (plenoptic voxels), a system for photorealistic view synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical harmonics. This representation can be optimized from calibrated images via gradient methods and regularization without any neural components. On standard, benchmark tasks, Plenoxels are optimized two orders of magnitude faster than Neural Radiance Fields with no loss in visual quality. For video and code, please see https://***/plenoxels.

关键词： Visualization Gradient methods computer vision Three-dimensional displays Codes Neural networks Benchmark testing

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V-Doc : Visual questions answers with Documents

V-Doc : Visual questions answers with Documents

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

作者： Ding, Yihao Huang, Zhe Wang, Runlin Zhang, Yanhang Chen, Xianru Ma, Yuzhong Chung, Hyunsuk Han, Soyeon Caren Univ Sydney Sydney NSW Australia FortifyEdge Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks. The V-Doc supports generating and using both extractive and abstractive question-answer pairs using documents images. The extractive QA selects a subset of tokens or phrases from the document contents to predict the answers, while the abstractive QA recognises the language in the content and generates the answer based on the trained model. Both aspects are crucial to understanding the documents, especially in an image format. We include a detailed scenario of question generation for the abstractive QA task. V-Doc supports a wide range of datasets and models, and is highly extensible through a declarative, framework-agnostic platform.(1)

关键词： Deep learning Visualization computer vision Computational modeling Predictive models Portable document format Question answering (information retrieval)

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Multi-modal Alignment using Representation Codebook

Multi-modal Alignment using Representation Codebook

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

作者： Duan, Jiali Chen, Liqun Tran, Son Yang, Jinyu Xu, Yi Zeng, Belinda Chilimbi, Trishul Amazon Seattle WA 98109 USA Univ Texas Arlington Arlington TX 76019 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Aligning signals from different modalities is an important step in vision-language representation learning as it affects the performance of later stages such as cross-modality fusion. Since image and text typically reside in different regions of the feature space, directly aligning them at instance level is challenging especially when features are still evolving during training. In this paper, we propose to align at a higher and more stable level using cluster representation. Specifically, we treat image and text as two "views" of the same entity, and encode them into a joint vision-language coding space spanned by a dictionary of cluster centers (codebook). We contrast positive and negative samples via their cluster assignments while simultaneously optimizing the cluster centers. To further smooth out the learning process, we adopt a teacher-student distillation paradigm, where the momentum teacher of one view guides the student learning of the other. We evaluated our approach on common vision language benchmarks and obtain new SoTA on zero-shot cross modality retrieval while being competitive on various other transfer tasks.

关键词： Training Representation learning computer vision Image coding Dictionaries Benchmark testing pattern recognition

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Detecting Deepfakes with Self-Blended Images

Detecting Deepfakes with Self-Blended Images

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

作者： Shiohara, Kaede Yamasaki, Toshihiko Univ Tokyo Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes. SBIs are generated by blending pseudo source and target images from single pristine images, reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images). The key idea behind SBIs is that more general and hardly recognizable fake samples encourage classifiers to learn generic and robust representations without overfitting to manipulation specific artifacts. We compare our approach with state-ofthe-art methods on FF++, CDF, DFD, DFDC, DFDCP, and FFIW datasets by following the standard cross-dataset and cross-manipulation protocols. Extensive experiments show that our method improves the model generalization to unknown manipulations and scenes. In particular, on DFDC and DFDCP where existing methods suffer from the domain gap between the training and test sets, our approach outperforms the baseline by 4.90% and 11.78% points in the cross-dataset evaluation, respectively.

关键词： Training Deepfakes Protocols Image recognition Face recognition Training data Detectors

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