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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11889 条记录，以下是51-60 订阅

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Label Propagation for Zero-shot Classification with vision-Language Models

Label Propagation for Zero-shot Classification with Vision-L...

引用

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

作者： Stojnic, Vladan Kalantidis, Yannis Tolias, Giorgos Czech Tech Univ FEE VRG Prague Czech Republic NAVER LABS Europe Meylan France

ISBN: (纸本)9798350353006

vision-Language Models (VLMs) have demonstrated impressive performance on zero-shot classification, i.e. classification when provided merely with a list of class names. In this paper, we tackle the case of zero-shot classification in the presence of unlabeled data. We leverage the graph structure of the unlabeled data and introduce ZLaP, a method based on label propagation (LP) that utilizes geodesic distances for classification. We tailor LP to graphs containing both text and image features and further propose an efficient method for performing inductive inference based on a dual solution and a sparsification step. We perform extensive experiments to evaluate the effectiveness of our method on 14 common datasets and show that ZLaP outperforms the latest related works. Code: https://***/vladan-stojnic/ZLaP

关键词： label propagation vision-language models zero-shot classification

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Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting

Attention-Propagation Network for Egocentric Heatmap to 3D P...

引用

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

作者： Kang, Taeho Lee, Youngki Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

We present EgoTAP, a heatmap-to-3D pose lifting method for highly accurate stereo egocentric 3D pose estimation. Severe self-occlusion and out-of-view limbs in egocentric camera views make accurate pose estimation a challenging problem. To address the challenge, prior methods employ joint heatmaps-probabilistic 2D representations of the body pose, but heatmap-to-3D pose conversion still remains an inaccurate process. We propose a novel heatmap-to-3D lifting method composed of the Grid ViT Encoder and the Propagation Network. The Grid ViT Encoder summarizes joint heatmaps into effective feature embedding using self-attention. Then, the Propagation Network estimates the 3D pose by utilizing skeletal information to better estimate the position of obscure joints. Our method significantly outperforms the previous state-of-the-art qualitatively and quantitatively demonstrated by a 23.9% reduction of error in an MPJPE metric. Our source code is available on GitHub (1).

关键词： 3D pose estimation egocentric vision stereo vision

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A vision Check-up for Language Models

A Vision Check-up for Language Models

引用

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

作者： Sharma, Pratyusha Shaham, Tamar Rott Baradad, Manel Fu, Stephanie Rodriguez-Munoz, Adrian Duggal, Shivam Isola, Phillip Torralba, Antonio MIT CSAIL Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9798350353006

What does learning to model relationships between strings teach Large Language Models (LLMs) about the visual world? We systematically evaluate LLMs' abilities to generate and recognize an assortment of visual concepts of increasing complexity and then demonstrate how a preliminary visual representation learning system can be trained using models of text. As language models lack the ability to consume or output visual information as pixels, we use code to represent images in our study. Although LLM-generated images do not look like natural images, results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world. Furthermore, experiments on self-supervised visual representation learning, utilizing images generated with text models, highlight the potential to train vision models capable of making semantic assessments of natural images using just LLMs.

关键词： Contrastive Learning

来源：评论

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Learning vision from Models Rivals Learning vision from Data

Learning Vision from Models Rivals Learning Vision from Data

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

作者： Tian, Yonglong Fan, Lijie Chen, Kaifeng Katabi, Dina Krishnan, Dilip Isola, Phillip Google Res Mountain View CA 94043 USA MIT CSAIL Cambridge MA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9798350353006

We introduce SynCLR, a novel approach for learning visual representations exclusively from synthetic images and synthetic captions, without any real data. We synthesize a large dataset of image captions using LLMs, then use an off-the-shelf text-to-image model to generate multiple images corresponding to each synthetic caption. We perform visual representation learning on these synthetic images via contrastive learning, treating images sharing the same caption as positive pairs. The resulting representations transfer well to many downstream tasks, competing favorably with other general-purpose visual representation learners such as CLIP and DINO v2 in image classification tasks. Furthermore, in dense prediction tasks such as semantic segmentation, SynCLR outperforms previous self-supervised methods by a significant margin, e.g., improving over MAE and iBOT by 6.2 and 4.3 mIoU on ADE20k for ViT-B/16.

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3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surfaces

3DFIRES: Few Image 3D REconstruction for Scenes with Hidden ...

引用

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

作者： Jin, Linyi Kulkarni, Nilesh Fouhey, David F. Univ Michigan Ann Arbor MI 48109 USA NYU New York NY USA

ISBN: (纸本)9798350353006

This paper introduces 3DFIRES, a novel system for scene-level 3D reconstruction from posed images. Designed to work with as few as one view, 3DFIRES reconstructs the complete geometry of unseen scenes, including hidden surfaces. With multiple view inputs, our method produces full reconstruction within all camera frustums. A key feature of our approach is the fusion of multi-view information at the feature level, enabling the production of coherent and comprehensive 3D reconstruction. We train our system on non-watertight scans from large-scale real scene dataset. We show it matches the efficacy of single-view reconstruction methods with only one input and surpasses existing techniques in both quantitative and qualitative measures for sparse-view 3D reconstruction. Project page: https://***/3DFIRES/

关键词： 3D vision hidden surfaces scene reconstruction

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PixelRNN: In-pixel Recurrent Neural Networks for End-to-end-optimized Perception with Neural Sensors

PixelRNN: In-pixel Recurrent Neural Networks for End-to-end-...

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

作者： So, Haley M. Bose, Laurie Dudek, Piotr Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA Univ Manchester Manchester Lancs England

ISBN: (纸本)9798350353006

Conventional image sensors digitize high-resolution images at fast frame rates, producing a large amount of data that needs to be transmitted off the sensor for further processing. This is challenging for perception systems operating on edge devices, because communication is power inefficient and induces latency. Fueled by innovations in stacked image sensor fabrication, emerging sensor-processors offer programmability and processing capabili-ties directly on the sensor. We exploit these capabilities by developing an efficient recurrent neural network architecture, PixelRNN, that encodes spatio-temporal features on the sensor using purely binary operations. PixelRNN reduces the amount of data to be transmitted off the sensor by factors up to 256 compared to the raw sensor data while offering competitive accuracy for hand gesture recognition and lip reading tasks. We experimentally validate PixelRNN using a prototype implementation on the SCAMP-5 sensor-processor platform.

关键词： In-pixel compute machine perception neural sensors sensor-processors vision sensor

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Exploring vision Transformers for 3D Human Motion-Language Models with Motion Patches

Exploring Vision Transformers for 3D Human Motion-Language M...

引用

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

作者： Yu, Qing Tanaka, Mikihiro Fujiwara, Kent LY Corp Tokyo Japan

ISBN: (纸本)9798350353013;9798350353006

To build a cross-modal latent space between 3D human motion and language, acquiring large-scale and high-quality human motion data is crucial. However, unlike the abundance of image data, the scarcity of motion data has limited the performance of existing motion-language models. To counter this, we introduce "motion patches", a new representation of motion sequences, and propose using vision Transformers (ViT) as motion encoders via transfer learning, aiming to extract useful knowledge from the image domain and apply it to the motion domain. These motion patches, created by dividing and sorting skeleton joints based on body parts in motion sequences, are robust to varying skeleton structures, and can be regarded as color image patches in ViT. We find that transfer learning with pre-trained weights of ViT obtained through training with 2D image data can boost the performance of motion analysis, presenting a promising direction for addressing the issue of limited motion data. Our extensive experiments show that the proposed motion patches, used jointly with ViT, achieve state-of-the-art performance in the benchmarks of text-to-motion retrieval, and other novel challenging tasks, such as cross-skeleton recognition, zero-shot motion classification, and human interaction recognition, which are currently impeded by the lack of data.

关键词： Motion Representation Motion-Language Models Text-Motion Retrieval

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StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN

StyleCineGAN: Landscape Cinemagraph Generation using a Pre-t...

引用

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

作者： Choi, Jongwoo Seo, Kwanggyoon Ashtari, Amirsaman Noh, Junyong Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinemagraphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN, our approach utilizes its deep feature space for both GAN inversion and cinemagraph generation. Specifically, we propose multi-scale deep feature warping (MSDFW), which warps the intermediate features of a pre-trained StyleGAN at different resolutions. By using MSDFW, the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

关键词： Image and video synthesis and generation vision + graphics vision applications and systems

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Insights from the Use of Previously Unseen Neural Architecture Search Datasets

Insights from the Use of Previously Unseen Neural Architectu...

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

作者： Geada, Rob Towers, David Forshaw, Matthew Atapour-Abarghouei, Amir McGough, A. Stephen Newcastle Univ Newcastle England Univ Durham Durham England Alan Turing Inst London England

ISBN: (纸本)9798350353006

The boundless possibility of neural networks which can be used to solve a problem - each with different performance - leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of removing the need for experts. Neural Architecture Search (NAS) offers a solution to this by automatically identifying the best architecture. However, to date, NAS work has focused on a small set of datasets which we argue are not representative of real-world problems. We introduce eight new datasets created for a series of NAS Challenges: AddNIST, Language, MultNIST, CIFARTile, Gutenberg, Isabella, GeoClassing, and Chesseract. These datasets and challenges are developed to direct attention to issues in NAS development and to encourage authors to consider how their models will perform on datasets unknown to them at development time. We present experimentation using standard Deep Learning methods as well as the best results from challenge participants.

关键词： Classification Datasets Image recognition Machine Learning NAS Neural Architecture Search

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Cinematic Behavior Transfer via NeRF-based Differentiable Filming

Cinematic Behavior Transfer via NeRF-based Differentiable Fi...

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

作者： Jiang, Xuekun Rao, Anyi Wang, Jingbo Lin, Dahua Dai, Bo Shanghai AI Lab Shanghai Peoples R China Stanford Univ Stanford CA 94305 USA Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In the evolving landscape of digital media and video production, the precise manipulation and reproduction of visual elements like camera movements and character actions are highly desired. Existing SLAM methods face limitations in dynamic scenes and human pose estimation often focuses on 2D projections, neglecting 3D statuses. To address these issues, we first introduce a reverse filming behavior estimation technique. It optimizes camera trajectories by leveraging NeRF as a differentiable renderer and refining SMPL tracks. We then introduce a cinematic transfer pipeline that is able to transfer various shot types to a new 2D video or a 3D virtual environment. The incorporation of 3D engine workflow enables superior rendering and control abilities, which also achieves a higher rating in the user study.

关键词： Three dimensional computer graphics

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