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

共 22907 条记录，以下是4641-4650 订阅

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Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction

Fully Understanding Generic Objects: Modeling, Segmentation,...

引用

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

作者： Liu, Feng Tran, Luan Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781665445092

Inferring 3D structure of a generic object from a 2D image is a long-standing objective of computer vision. Conventional approaches either learn completely from CAD-generated synthetic data, which have difficulty in inference from real images, or generate 2.5D depth image via intrinsic decomposition, which is limited compared to the full 3D reconstruction. One fundamental challenge lies in how to leverage numerous real 2D images without any 3D ground truth. To address this issue, we take an alternative approach with semi-supervised learning. That is, for a 2D image of a generic object, we decompose it into latent representations of category, shape, albedo, lighting and camera projection matrix, decode the representations to segmented 3D shape and albedo respectively, and fuse these components to render an image well approximating the input image. Using a category-adaptive 3D joint occupancy field (JOF), we show that the complete shape and albedo modeling enables us to leverage real 2D images in both modeling and model fitting. The effectiveness of our approach is demonstrated through superior 3D reconstruction from a single image, being either synthetic or real, and shape segmentation. Code is available at http://***/***.

关键词： Training Solid modeling Image segmentation computer vision Three-dimensional displays Shape Fitting

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Learnable Global Spatio-Temporal Adaptive Aggregation for Bracketing Image Restoration and Enhancement

Learnable Global Spatio-Temporal Adaptive Aggregation for Br...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Xinwei Dai Yuanbo Zhou Xintao Qiu Hui Tang Wei Deng Qingquan Gao Tong Tong Fuzhou University Imperial Vision Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Employing specific networks to address different types of degradation often proved to be complex and time-consuming in practical applications. The Bracket Image Restoration and Enhancement (BIRE) aimed to address various image restoration tasks in a unified manner by restoring clear single-frame images from multiple-frame shots, including denoising, deblurring, enhancing high dynamic range (HDR), and achieving super-resolution under various degradation conditions. In this paper, we propose LGSTANet, an efficient aggregation restoration network for BIRE. Specifically, inspired by video restoration methods, we adopt an efficient architecture comprising alignment, aggregation, and reconstruction. Additionally, we introduce a Learnable Global Spatio-Temporal Adaptive (LGSTA) aggregation module to effectively aggregate inter-frame complementary information. Furthermore, we propose an adaptive restoration modulator to address specific degradation disturbances of various types, thereby achieving high-quality restoration outcomes. Extensive experiments demonstrate the effectiveness of our method. LGSTANet outperforms other state-of-the-art methods in Bracket Image Restoration and Enhancement and achieves competitive results in the NTIRE2024 BIRE challenge.

关键词： Degradation Aggregates Perturbation methods Superresolution Noise reduction Modulation computer architecture

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Meta Batch-Instance Normalization for Generalizable Person Re-Identification

Meta Batch-Instance Normalization for Generalizable Person R...

引用

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

作者： Choi, Seokeon Kim, Taekyung Jeong, Minki Park, Hyoungseob Kim, Changick Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, generalizable Re-ID has recently attracted growing attention. Many existing methods have employed an instance normalization technique to reduce style variations, but the loss of discriminative information could not be avoided. In this paper, we propose a novel generalizable Re-ID framework, named Meta Batch-Instance Normalization (MetaBIN). Our main idea is to generalize normalization layers by simulating unsuccessful generalization scenarios beforehand in the meta-learning pipeline. To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers. Moreover, we diversify the virtual simulations via our meta-train loss accompanied by a cyclic inner-updating manner to boost generalization capability. After all, the MetaBIN framework prevents our model from overfitting to the given source styles and improves the generalization capability to unseen domains without additional data augmentation or complicated network design. Extensive experimental results show that our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark and the cross-domain Re-ID problem. The source code is available at: https://***/bismex/MetaBIN.

关键词： computer vision Codes Computational modeling Pipelines Benchmark testing Data models pattern recognition

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Prompting Foundational Models for Omni-supervised Instance Segmentation

Prompting Foundational Models for Omni-supervised Instance S...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Arnav M. Das Ritwick Chaudhry Kaustav Kundu Davide Modolo University of Washington Seattle AWS AI Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation. Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ideal for omnisupervised learning, where weak labels are used to derive supervisory signals for complex tasks. In our work, we use SAM and GDino as teacher models and prompt them with weak annotations to create high-quality pseudomasks. These pseudomasks are then used to train student instance segmentation models, which do not require prompts at inference time. We explore various weak annotations, such as bounding boxes, points, and image-level class labels, and show that a student model can achieve roughly 95% of a fully-supervised model’s performance while reducing annotation costs by 7×. We show the effectiveness of our approach on challenging instance segmentation benchmarks such as COCO [15], ADE20K [30], Cityscapes [9]. Our approach can be used to reduce annotation cost to train instance segmentation models, making it more accessible to a wider range of applications.

关键词： Instance segmentation Training computer vision Costs Annotations Computational modeling conferences

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Unsupervised Domain Adaptation for Weed Segmentation Using Greedy Pseudo-labelling

Unsupervised Domain Adaptation for Weed Segmentation Using G...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Yingchao Huang Abdul Bais University of Regina Regina Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Automatic weed identification based on RGB images with convolutional neural networks (CNN) is a new frontier of precision agriculture. However, the CNN models expect a large volume of labelled data. Their performance deteriorates across different fields due to varied agricultural contexts. To address this, we propose an unsupervised domain adaptation (DA) framework leveraging pseudo-labelling. Our method involves co-training labelled source data with pseudo-labelled target data. We introduce a novel greedy pseudo-labelling strategy to optimize pseudo-label selection, maximizing gains while minimizing overfitting risks. Monitoring overfitting with covariance helps detect fluctuations in class pixel counts during co-training, enhancing target performance. The proposed framework has demonstrated superior performance by evaluation against literature approaches, including the input-level DA methods with Fourier Transform, feature-level with CycleGAN methods and AdaptSegNet, and output-level with self-training. It is tested with the ROSE challenge dataset from different cameras and years with diverse plant stages. Particularly in challenging conditions for plants across different years with varied plant stages, the proposed method outperforms existing literature that struggles to surpass the baseline.

关键词： Precision agriculture Solid modeling computer vision Fourier transforms Fluctuations conferences Data models

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MonoSelfRecon: Purely Self-Supervised Explicit Generalizable 3D Reconstruction of Indoor Scenes from Monocular RGB Views

MonoSelfRecon: Purely Self-Supervised Explicit Generalizable...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Runfa Li Upal Mahbub Vasudev Bhaskaran Truong Nguyen UC San Diego Qualcomm

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Current monocular 3D scene reconstruction (3DR) works are either fully-supervised, or not generalizable, or implicit in 3D representation. We propose a novel framework - MonoSelfRecon that for the first time achieves explicit 3D mesh reconstruction for generalizable indoor scenes with monocular RGB views by purely self-supervision on voxel-SDF (signed distance function). MonoSelfRecon follows an Autoencoder-based architecture, decodes voxel-SDF and a generalizable Neural Radiance Field (NeRF), which is used to guide voxel-SDF in self-supervision. We propose novel self-supervised losses, which not only support pure self-supervision, but can be used together with supervised signals to further boost supervised training. Our experiments show that "MonoSelfRecon" trained in pure self-supervision outperforms current best self-supervised indoor depth estimation models and is comparable to 3DR models trained in fully supervision with depth annotations. MonoSelfRecon is not restricted by specific model design, which can be used to any models with voxel-SDF for purely self-supervised manner.

关键词： Training Solid modeling computer vision Three-dimensional displays Annotations conferences Estimation

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Learning by Watching

Learning by Watching

引用

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

作者： Zhang, Jimuyang Ohn-Bar, Eshed Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781665445092

When in a new situation or geographical location, human drivers have an extraordinary ability to watch others and learn maneuvers that they themselves may have never performed. In contrast, existing techniques for learning to drive preclude such a possibility as they assume direct access to an instrumented ego-vehicle with fully known observations and expert driver actions. However, such measurements cannot be directly accessed for the non-ego vehicles when learning by watching others. Therefore, in an application where data is regarded as a highly valuable asset, current approaches completely discard the vast portion of the training data that can be potentially obtained through indirect observation of surrounding vehicles. Motivated by this key insight, we propose the Learning by Watching (LbW) framework which enables learning a driving policy without requiring full knowledge of neither the state nor expert actions. To increase its data, i.e., with new perspectives and maneuvers, LbW makes use of the demonstrations of other vehicles in a given scene by (1) transforming the ego-vehicle's observations to their points of view, and (2) inferring their expert actions. Our LbW agent learns more robust driving policies while enabling data-efficient learning, including quick adaptation of the policy to rare and novel scenarios. In particular, LbW drives robustly even with a fraction of available driving data required by existing methods, achieving an average success rate of 92% on the original CARLA benchmark with only 30 minutes of total driving data and 82% with only 10 minutes.

关键词： computer vision Instruments Current measurement Training data Benchmark testing pattern recognition Noise measurement

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AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries

AdCo: Adversarial Contrast for Efficient Learning of Unsuper...

引用

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

作者： Hu, Qianjiang Wang, Xiao Hu, Wei Qi, Guo-Jun Peking Univ Beijing Peoples R China Purdue Univ W Lafayette IN 47907 USA Lab MAchine Percept & LEarning MAPLE Beijing Peoples R China

ISBN: (纸本)9781665445092

Contrastive learning relies on constructing a collection of negative examples that are sufficiently hard to discriminate against positive queries when their representations are self-trained. Existing contrastive learning methods either maintain a queue of negative samples over minibatches while only a small portion of them are updated in an iteration, or only use the other examples from the current minibatch as negatives. They could not closely track the change of the learned representation over iterations by updating the entire queue as a whole, or discard the useful information from the past minibatches. Alternatively, we present to directly learn a set of negative adversaries playing against the self-trained representation. Two players, the representation network and negative adversaries, are alternately updated to obtain the most challenging negative examples against which the representation of positive queries will be trained to discriminate. We further show that the negative adversaries are updated towards a weighted combination of positive queries by maximizing the adversarial contrastive loss, thereby allowing them to closely track the change of representations over time. Experiment results demonstrate the proposed Adversarial Contrastive (AdCo) model not only achieves superior performances (a top-1 accuracy of 73.2% over 200 epochs and 75.7% over 800 epochs with linear evaluation on ImageNet), but also can be pre-trained more efficiently with much shorter GPU time and fewer epochs.

关键词： Learning systems computer vision Codes Graphics processing units pattern recognition Task analysis

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Improving Object Detection to Fisheye Cameras with Open-Vocabulary Pseudo-Label Approach

Improving Object Detection to Fisheye Cameras with Open-Voca...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Long Hoang Pham Quoc Pham-Nam Ho Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Huy-Hung Nguyen Duong Khac Vu Chi Dai Tran Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Fish-eye cameras have long been employed in traffic surveillance systems to allow for wider observation of the roads. Despite their widespread use, limited computer vision research is tailored explicitly to images captured by fish-eye cameras. The AI City Challenge 2024 - Track 4 introduces a novel fish-eye camera dataset for the 2D road object detection task. This paper proposes a framework designed to detect objects in fish-eye camera images. Our approach involves several key steps: first, we generate image data to bridge the representation gap between day and night images. Next, we leverage zero-shot open vocabulary detection to produce pseudo-labels, aiding in training supervised object detection models. Additionally, we optimize the model’s hyper-parameters and inference configuration for better performance. Finally, we apply various post-processing techniques to enhance detection performance. Our solution achieves a final F1 score of 0.6194 in the AI City Challenge 2024 - Track 4, ranking third among competing teams. The source code is available at GitHub Repo.

关键词： Training computer vision Adaptation models Vocabulary Roads Urban areas Object detection

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Learning Tracking Representations from Single Point Annotations

Learning Tracking Representations from Single Point Annotati...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Qiangqiang Wu Antoni B. Chan Department of Computer Science City University of Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing deep trackers are typically trained with large-scale video frames with annotated bounding boxes. However, these bounding boxes are expensive and time-consuming to annotate, in particular for large scale datasets. In this paper, we propose to learn tracking representations from single point annotations (i.e., 4.5 × faster to annotate than the traditional bounding box) in a weakly supervised manner. Specifically, we propose a soft contrastive learning (SoCL) framework that incorporates target objectness prior into end-to-end contrastive learning. Our SoCL consists of adaptive positive and negative sample generation, which is memory-efficient and effective for learning tracking representations. We apply the learned representation of SoCL to visual tracking and show that our method can 1) achieve better performance than the fully supervised baseline trained with box annotations under the same annotation time cost; 2) achieve comparable performance of the fully supervised baseline by using the same number of training frames and meanwhile reducing annotation time cost by 78% and total fees by 85%; 3) be robust to annotation noise.

关键词： Training Visualization Target tracking Costs Correlation Annotations Noise

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