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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是4731-4740 订阅

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VicTR: Video-conditioned Text Representations for Activity recognition

VicTR: Video-conditioned Text Representations for Activity R...

引用

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

作者： Kahatapitiya, Kumara Arnab, Anurag Nagrani, Arsha Ryoo, Michael S. SUNY Stony Brook Stony Brook NY 11794 USA Google Res Mountain View CA USA

ISBN: (纸本)9798350353006

vision-Language models (VLMs) have excelled in the image-domain- especially in zero-shot settings- thanks to the availability of vast pretraining data (i.e., paired image-text samples). However for videos, such paired data is not as abundant. Therefore, video- VLMs are usually designed by adapting pretrained image- VLMs to the video-domain, instead of training from scratch. All such recipes rely on aug-menting visual embeddings with temporal information (i.e., image -+ video), often keeping text embeddings unchanged or even being discarded. In this paper, we argue the contrary, that better video- VLMs can be designed by focusing more on augmenting text, rather than visual information. More specifically, we introduce Video-conditioned Text Representations (Vi c TR): a form of text embeddings optimized w.r.t. vi-sual embeddings, creating a more-flexible contrastive latent space. Our model canfurther make use offreely-available semantic information, in the form of visually- grounded aux-iliary text (e.g. object or scene information). We evaluate our model on few-shot, zero-shot (HMDB-51, UCF-10l), short-form (Kinetics-400) and long-form (Charades) activ-ity recognition benchmarks, showing strong performance among video-VLMs.

关键词： Activity recognition Video Understanding Video-conditioned Text vision-language models

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RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs

RegNeRF: Regularizing Neural Radiance Fields for View Synthe...

引用

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

作者： Niemeyer, Michael Barron, Jonathan T. Mildenhall, Ben Sajjadi, Mehdi S. M. Geiger, Andreas Radwan, Noha Max Planck Inst Intelligent Syst Tubingen Germany Univ Tubingen Tubingen Germany Google Res Tubingen Germany Google Tubingen Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Neural Radiance Fields (NeRF) have emerged as a powerful representation for the task of novel view synthesis due to their simplicity and state-of-the-art performance. Though NeRF can produce photorealistic renderings of unseen viewpoints when many input views are available, its performance drops significantly when this number is reduced. We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training. We address this by regularizing the geometry and appearance of patches rendered from unobserved viewpoints, and annealing the ray sampling space during training. We additionally use a normalizing flow model to regularize the color of unobserved viewpoints. Our model outperforms not only other methods that optimize over a single scene, but in many cases also conditional models that are extensively pre-trained on large multi-view datasets.

关键词： Geometry Training Solid modeling Annealing Three-dimensional displays Computational modeling Rendering (computer graphics)

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Squeeze-and-Excitation Networks 31

Squeeze-and-Excitation Networks

引用

31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hu, Jie Shen, Li Sun, Gang Momenta Beijing Peoples R China Univ Oxford Dept Engn Sci Oxford England

ISBN: (纸本)9781538664209

Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel- wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the " Squeezeand- Excitation" ( SE) block, that adaptively recalibrates channel- wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state- ofthe- art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top- 5 error to 2.251%, achieving a- 25% relative improvement over the winning entry of 2016. Code and models are available at https : //***/hujie-frank/SENet.

关键词： computer architecture Computational modeling Convolution Task analysis Convolutional codes Adaptation models Stacking

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Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation

Stacked Hybrid-Attention and Group Collaborative Learning fo...

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

作者： Dong, Xingning Gan, Tian Song, Xuemeng Wu, Jianlong Cheng, Yuan Nie, Liqiang Shandong Univ Jinan Peoples R China Ant Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Scene Graph Generation, which generally follows a regular encoder-decoder pipeline, aims to first encode the visual contents within the given image and then parse them into a compact summary graph. Existing SGG approaches generally not only neglect the insufficient modality fusion between vision and language, but also fail to provide informative predicates due to the biased relationship predictions, leading SGG far from practical. Towards this end, we first present a novel Stacked Hybrid-Attention network, which facilitates the intra-modal refinement as well as the intermodal interaction, to serve as the encoder. We then devise an innovative Group Collaborative Learning strategy to optimize the decoder. Particularly, based on the observation that the recognition capability of one classifier is limited towards an extremely unbalanced dataset, we first deploy a group of classifiers that are expert in distinguishing different subsets of classes, and then cooperatively optimize them from two aspects to promote the unbiased SGG. Experiments conducted on VG and GQA datasets demonstrate that, we not only establish a new state-of-the-art in the unbiased metric, but also nearly double the performance compared with two baselines. Our code is available at https://***/dongxingning/SHA-GCL-for-SGG.

关键词： Measurement Visualization computer vision Image analysis Codes Computational modeling Pipelines

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Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? 31

Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs an...

引用

31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hara, Kensho Kataoka, Hirokatsu Satoh, Yutaka Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan

ISBN: (纸本)9781538664209

The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional (3D) kernels. Recently, the performance levels of 3D CNNs in the field of action recognition have improved significantly. However, to date, conventional research has only explored relatively shallow 3D architectures. We examine the architectures of various 3D CNNs from relatively shallow to very deep ones on current video datasets. Based on the results of those experiments, the following conclusions could be obtained: (i) ResNet-18 training resulted in significant overfitting for UCF-101, HMDB-51, and ActivityNet but not for Kinetics. (ii) The Kinetics dataset has sufficient data for training of deep 3D CNNs, and enables training of up to 152 ResNets layers, interestingly similar to 2D ResNets on ImageNet. ResNeXt-101 achieved 78.4% average accuracy on the Kinetics test set. (iii) Kinetics pretrained simple 3D architectures outperforms complex 2D architectures, and the pretrained ResNeXt-101 achieved 94.5% and 70.2% on UCF-101 and HMDB-51, respectively. The use of 2D CNNs trained on ImageNet has produced significant progress in various tasks in image. We believe that using deep 3D CNNs together with Kinetics will retrace the successful history of 2D CNNs and ImageNet, and stimulate advances in computer vision for videos. The codes and pretrained models used in this study are publicly available(1).

关键词： Three-dimensional displays Kinetic theory Two dimensional displays Training Task analysis Kernel computer vision

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A Neural Rendering Framework for Free-Viewpoint Relighting

A Neural Rendering Framework for Free-Viewpoint Relighting

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

作者： Chen, Zhang Chen, Anpei Zhang, Guli Wang, Chengyuan Ji, Yu Kutulakos, Kiriakos N. Yu, Jingyi ShanghaiTech Univ Shanghai Peoples R China Shanghai Inst Microsyst & Informat Technol Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai Univ Shanghai Peoples R China DGene Inc Baton Rouge LA USA Univ Toronto Toronto ON Canada

ISBN: (纸本)9781728171685

We present a novel Relightable Neural Renderer (RNR) for simultaneous view synthesis and relighting using multi-view image inputs. Existing neural rendering (NR) does not explicitly model the physical rendering process and hence has limited capabilities on relighting. RNR instead models image formation in terms of environment lighting, object intrinsic attributes, and light transport function (LTF), each corresponding to a learnable component. In particular, the incorporation of a physically based rendering process not only enables relighting but also improves the quality of view synthesis. Comprehensive experiments on synthetic and real data show that RNR provides a practical and effective solution for conducting free-viewpoint relighting.

关键词： Rendering (computer graphics)

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BACON: Band-limited Coordinate Networks for Multiscale Scene Representation

BACON: Band-limited Coordinate Networks for Multiscale Scene...

引用

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

作者： Lindell, David B. Van Veen, Dave Park, Jeong Joon Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Coordinate-based networks have emerged as a powerful tool for 3D representation and scene reconstruction. These networks are trained to map continuous input coordinates to the value of a signal at each point. Still, current architectures are black boxes: their spectral characteristics cannot be easily analyzed, and their behavior at unsupervised points is difficult to predict. Moreover, these networks are typically trained to represent a signal at a single scale, so naive downsampling or upsampling results in artifacts. We introduce band-limited coordinate networks (BACON), a network architecture with an analytical Fourier spectrum. BACON has constrained behavior at unsupervised points, can be designed based on the spectral characteristics of the represented signal, and can represent signals at multiple scales without per-scale supervision. We demonstrate BACON for multiscale neural representation of images, radiance fields, and 3D scenes using signed distance functions and show that it outperforms conventional single-scale co-ordinate networks in terms of interpretability and quality.

关键词： Time-frequency analysis Three-dimensional displays Fitting Bandwidth Network architecture Rendering (computer graphics) Behavioral sciences

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HEAT: Holistic Edge Attention Transformer for Structured Reconstruction

HEAT: Holistic Edge Attention Transformer for Structured Rec...

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

作者： Chen, Jiacheng Qian, Yiming Furukawa, Yasutaka Simon Fraser Univ Burnaby BC Canada Univ Manitoba Winnipeg MB Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure. The approach detects corners and classifies edge candidates between corners in an end-to-end manner. Our contribution is a holistic edge classification architecture, which 1) initializes the feature of an edge candidate by a trigonometric positional encoding of its end-points;2) fides image feature to each edge candidate by deformable attention;3) employs two weight-sharing Transformer decoders to learn holistic structural patterns over the graph edge candidates;and 4) is trained with a masked learning strategy. The corner detector is a variant of the edge classification architecture, adapted to operate on pixels as corner candidates. We conduct experiments on two structured reconstruction tasks: outdoor building architecture and indoor floorplan planar graph reconstruction. Extensive qualitative and quantitative evaluations demonstrate the superiority of our approach over the state of the art. Code and pre-trained models are available at https://heat-structured-reconstruction. github io/

关键词： Heating systems Codes Three-dimensional displays Image edge detection Architecture computer architecture Transformer cores

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Partially Shared Multi-Task Convolutional Neural Network with Local Constraint for Face Attribute Learning 31

Partially Shared Multi-Task Convolutional Neural Network wit...

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31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cao, Jiajiong Li, Yingming Zhang, Zhongfei Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538664209

In this paper, we study the face attribute learning problem by considering the identity information and attribute relationships simultaneously. In particular, we first introduce a Partially Shared Multi-task Convolutional Neural Network (PS-MCNN), in which four Task Specific Networks (TSNets) and one Shared Network (SNet) are connected by Partially Shared (PS) structures to learn better shared and task specific representations. To utilize identity information to further boost the performance, we introduce a local learning constraint which minimizes the difference between the representations of each sample and its local geometric neighbours with the same identity. Consequently, we present a local constraint regularized multitask network, called Partially Shared Multi-task Convolutional Neural Network with Local Constraint (PS-MCNN-LC), where PS structure and local constraint are integrated together to help the framework learn better attribute representations. The experimental results on CelebA and LFWA demonstrate the promise of the proposed methods.

关键词： Task analysis Face Hair Convolutional neural networks computer architecture Feature extraction Bellows

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SonicvisionLM: Playing Sound with vision Language Models

SonicVisionLM: Playing Sound with Vision Language Models

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

作者： Xie, Zhifeng Yu, Shengye He, Qile Li, Mengtian Shanghai Univ Shanghai Peoples R China Shanghai Engn Res Ctr Mot Picture Special Effects Shanghai Peoples R China

ISBN: (纸本)9798350353006

There has been a growing interest in the task of generating sound for silent videos, primarily because of its practicality in streamlining video post-production. However, existing methods for video-sound generation attempt to directly create sound from visual representations, which can be challenging due to the difficulty of aligning visual representations with audio representations. In this paper, we present SonicvisionLM, a novel framework aimed at generating a wide range of sound effects by leveraging vision-language models(VLMs). Instead of generating audio directly from video, we use the capabilities of powerful VLMs. When provided with a silent video, our approach first identifies events within the video using a VLM to suggest possible sounds that match the video content. This shift in approach transforms the challenging task of aligning image and audio into more well-studied sub-problems of aligning image-to-text and text-to-audio through the popular diffusion models. To improve the quality of audio recommendations with LLMs, we have collected an extensive dataset that maps text descriptions to specific sound effects and developed a time-controlled audio adapter. Our approach surpasses current state-of-the-art methods for converting video to audio, enhancing synchronization with the visuals, and improving alignment between audio and video components. Project page: https://***. io/***/

关键词： audio generation multi-modal vision-language model

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