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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 11890 条记录，以下是491-500 订阅

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Large-Scale Bidirectional Training for Zero-Shot Image Captioning

Large-Scale Bidirectional Training for Zero-Shot Image Capti...

引用

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

作者： Kim, Taehoon Marsden, Mark Ahn, Pyunghwan Kim, Sangyun Lee, Sihaeng Sala, Alessandra Kim, Seung Hwan LG AI Res Seoul South Korea Shutterstock New York NY USA

ISBN: (纸本)9798350365474

When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.

关键词： image captioning large-scale multimodal transformers vision-language zero-shot

来源：评论

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Residential floor plan recognition and reconstruction

Residential floor plan recognition and reconstruction

引用

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

作者： Lv, Xiaolei Zhao, Shengchu Yu, Xinyang Zhao, Binqiang Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665445092

recognition and reconstruction of residential floor plan drawings are important and challenging in design, decoration, and architectural remodeling fields. An automatic framework is provided that accurately recognizes the structure, type, and size of the room, and outputs vectorized 3D reconstruction results. Deep segmentation and detection neural networks are utilized to extract room structural information. Key points detection network and cluster analysis are utilized to calculate scales of rooms. The vectorization of room information is processed through an iterative optimization-based method. The system significantly increases accuracy and generalization ability, compared with existing methods. It outperforms other systems in floor plan segmentation and vectorization process, especially inclined wall detection.

关键词： computer vision Three-dimensional displays Neural networks Benchmark testing Iterative methods Data mining Floors

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Single-Shot Freestyle Dance Reenactment

Single-Shot Freestyle Dance Reenactment

引用

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

作者： Gafni, Oran Ashual, Oron Wolf, Lior Facebook AI Res Menlo Pk CA 94025 USA Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781665445092

The task of motion transfer between a source dancer and a target person is a special case of the pose transfer problem, in which the target person changes their pose in accordance with the motions of the dancer. In this work, we propose a novel method that can reanimate a single image by arbitrary video sequences, unseen during training. The method combines three networks: (i) a segmentation-mapping network, (ii) a realistic frame-rendering network, and (iii) a face refinement network. By separating this task into three stages, we are able to attain a novel sequence of realistic frames, capturing natural motion and appearance. Our method obtains significantly better visual quality than previous methods and is able to animate diverse body types and appearances, which are captured in challenging poses.

关键词： Training Visualization Image segmentation computer vision Motion segmentation Face recognition Biological system modeling

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Topological Planning with Transformers for vision-and-Language Navigation

Topological Planning with Transformers for Vision-and-Langua...

引用

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

作者： Chen, Kevin Chen, Junshen K. Chuang, Jo Vazquez, Marynel Savarese, Silvio Stanford Univ Stanford CA 94305 USA Yale Univ New Haven CT 06520 USA

ISBN: (纸本)9781665445092

Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end but struggle to perform well in freely traversable environments. Inspired by the robotics community, we propose a modular approach to VLN using topological maps. Given a natural language instruction and topological map, our approach leverages attention mechanisms to predict a navigation plan in the map. The plan is then executed with low-level actions (e.g. FORWARD, ROTATE) using a robust controller. Experiments show that our method outperforms previous end-to-end approaches, generates interpretable navigation plans, and exhibits intelligent behaviors such as backtracking.

关键词： computer vision Backtracking Navigation Natural languages Buildings Transformers Planning

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AIM: an Auto-Augmenter for Images and Meshes

AIM: an Auto-Augmenter for Images and Meshes

引用

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

作者： Singh, Vinit Veerendraveer Kambhamettu, Chandra Univ Delaware Dept Comp & Informat Sci Video Image Modeling & Synth VIMS Lab Newark DE 19716 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Data augmentations are commonly used to increase the robustness of deep neural networks. In most contemporary research, the networks do not decide the augmentations;they are task-agnostic, and grid search determines their magnitudes. Furthermore, augmentations applicable to lower-dimensional data do not easily extend to higher-dimensional data and vice versa. This paper presents an auto-augmenter for images and meshes (AIM) that easily incorporates into neural networks at training and inference times. It jointly optimizes with the network to produce constrained, non-rigid deformations in the data. AIM predicts sample-aware deformations suited for a task, and our experiments confirm its effectiveness with various networks.

关键词： Deep learning Training Visualization computer vision Image recognition Neural networks Robustness

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Generating Diverse Agricultural Data for vision-Based Farming Applications

Generating Diverse Agricultural Data for Vision-Based Farmin...

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

作者： Cieslak, Mikolaj Govindarajan, Umabharathi Garcia, Alejandro Chandrashekar, Anuradha Haedrich, Torsten Mendoza-Drosik, Aleksander Michels, Dominik L. Pirk, Soeren Fu, Chia-Chun Palubicki, Wojciech GreenMatterAI Berlin Germany Blue River Technol Santa Clara CA USA King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Tech Univ Darmstadt Darmstadt Germany Christian Albrecht Univ Kiel Kiel Germany Adam Mickiewicz Univ Poznan Poland

ISBN: (纸本)9798350365474

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. The model simulates distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. We validate our model's effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.

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SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping

SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full...

引用

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

作者： Stone, Austin Maurer, Daniel Ayvaci, Alper Angelova, Anelia Jonschkowski, Rico Google Robot Mountain View CA 94043 USA Waymo Mountain View CA USA

ISBN: (纸本)9781665445092

We present SMURF, a method for unsupervised learning of optical flow that improves state of the art on all benchmarks by 36% to 40% (over the prior best method UFlow) and even outperforms several supervised approaches such as PWC-Net and FlowNet2. Our method integrates architecture improvements from supervised optical flow, i.e. the RAFT model, with new ideas for unsupervised learning that include a sequence-aware self-supervision loss, a technique for handling out-of-frame motion, and an approach for learning effectively from multi-frame video data while still only requiring two frames for inference.

关键词： Optical losses computer vision Computational modeling Estimation computer architecture Benchmark testing Data models

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Coarse or Fine? Recognising Action End States without Labels

Coarse or Fine? Recognising Action End States without Labels

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

作者： Moltisanti, Davide Bilen, Hakan Sevilla-Lara, Laura Keller, Frank Univ Bath Bath Avon England Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9798350365474

We focus on the problem of recognising the end state of an action in an image, which is critical for understanding what action is performed and in which manner. We study this focusing on the task of predicting the coarseness of a cut, i.e., deciding whether an object was cut "coarsely" or "finely". No dataset with these annotated end states is available, so we propose an augmentation method to synthesise training data. We apply this method to cutting actions extracted from an existing action recognition dataset. Our method is object agnostic, i.e., it presupposes the location of the object but not its identity. Starting from less than a hundred images of a whole object, we can generate several thousands images simulating visually diverse cuts of different coarseness. We use our synthetic data to train a model based on UNet and test it on real images showing coarsely/finely cut objects. Results demonstrate that the model successfully recognises the end state of the cutting action despite the domain gap between training and testing, and that the model generalises well to unseen objects.

关键词： adverb recognition fine-grained recognition object end-state recognition

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Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion Models

Generate Like Experts: Multi-Stage Font Generation by Incorp...

引用

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

作者： Fu, Bin Yu, Fanghua Liu, Anran Wang, Zixuan Wen, Jie He, Junjun Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples, which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-content disentanglement paradigm and employ the Generative Adversarial Network (GAN) to generate target fonts by combining the decoupled content and style representations. The complicated structure and detailed style are simultaneously generated in those methods, which may be the sub-optimal solutions for FFG task. Inspired by most manual font design processes of expert designers, in this paper, we model font generation as a multi-stage generative process. Specifically, as the injected noise and the data distribution in diffusion models can be well-separated into different sub-spaces, we are able to incorporate the font transfer process into these models. Based on this observation, we generalize diffusion methods to model font generative process by separating the reverse diffusion process into three stages with different functions: The structure construction stage first generates the structure information for the target character based on the source image, and the font transfer stage subsequently transforms the source font to the target font. Finally, the font refinement stage enhances the appearances and local details of the target font images. Based on the above multi-stage generative process, we construct our font generation framework, named MSD-Font, with a dual-network approach to generate font images. The superior performance demonstrates the effectiveness of our model. The code is available at: https://***/fubinfb/MSD-Font

关键词： Diffusion Model Few-shot Font Generation Probabilistic Generative Model

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SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction

SIFU: Side-view Conditioned Implicit Function for Real-world...

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

作者： Zhang, Zechuan Yang, Zongxin Yang, Yi Zhejiang Univ CCAL ReLER Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Creating high-quality 3D models of clothed humans from single images for real-world applications is crucial. Despite recent advancements, accurately reconstructing humans in complex poses or with loose clothing from in-the-wild images, along with predicting textures for unseen areas, remains a significant challenge. A key limitation of previous methods is their insufficient prior guidance in transitioning from 2D to 3D and in texture prediction. In response, we introduce SIFU (Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction), a novel approach combining a Side-view Decoupling Transformer with a 3D Consistent Texture Refinement pipeline. SIFU employs a cross-attention mechanism within the transformer, using SMPL-X normals as queries to effectively decouple side-view features in the process of mapping 2D features to 3D. This method not only improves the precision of the 3D models but also their robustness, especially when SMPL-X estimates are not perfect. Our texture refinement process leverages text-to-image diffusion-based prior to generate realistic and consistent textures for invisible views. Through extensive experiments, SIFU surpasses SOTA methods in both geometry and texture reconstruction, showcasing enhanced robustness in com-lex scenarios and achieving an unprecedented Chamfer and P2S measurement. Our approach extends to practical applications such as 3D printing and scene building, demonstrating its broad utility in real-world scenarios.

关键词： 3D reconstruction 3D vision Clothed human diffusion model single image to 3D

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