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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4631-4640 订阅

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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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Semantic-Aware Video Text Detection

Semantic-Aware Video Text Detection

引用

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

作者： Feng, Wei Yin, Fei Zhang, Xu-Yao Liu, Cheng-Lin Chinese Acad Sci Natl Lab Pattern Recognit NLPR Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China

ISBN: (纸本)9781665445092

Most existing video text detection methods track texts with appearance features, which are easily influenced by the change of perspective and illumination. Compared with appearance features, semantic features are more robust cues for matching text instances. In this paper, we propose an end-to-end trainable video text detector that tracks texts based on semantic features. First, we introduce a new character center segmentation branch to extract semantic features, which encode the category and position of characters. Then we propose a novel appearance-semanticgeometry descriptor to track text instances, in which semantic features can improve the robustness against appearance changes. To overcome the lack of character-level annotations, we propose a novel weakly-supervised character center detection module, which only uses word-level annotated real images to generate character-level labels. The proposed method achieves state-of-the-art performance on three video text benchmarks ICDAR 2013 Video, Minetto and RT-IK, and two Chinese scene text benchmarks CA-SIA10K and MSRA-TD500.

关键词： computer vision Text recognition Semantics Lighting Detectors Benchmark testing Feature extraction

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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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Neighborhood Contrastive Learning for Novel Class Discovery

Neighborhood Contrastive Learning for Novel Class Discovery

引用

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

作者： Zhong, Zhun Fini, Enrico Roy, Subhankar Luo, Zhiming Ricci, Elisa Sebe, Nicu Univ Trento Trento Italy Xiamen Univ Xiamen Peoples R China Fdn Bruno Kessler Povo Italy

ISBN: (纸本)9781665445092

In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the peculiarities of NCD to build a new framework, named Neighborhood Contrastive Learning (NCL), to learn discriminative representations that are important to clustering performance. Our contribution is twofold. First, we find that a feature extractor trained on the labeled set generates representations in which a generic query sample and its neighbors are likely to share the same class. We exploit this observation to retrieve and aggregate pseudo-positive pairs with contrastive learning, thus encouraging the model to learn more discriminative representations. Second, we notice that most of the instances are easily discriminated by the network, contributing less to the contrastive loss. To overcome this issue, we propose to generate hard negatives by mixing labeled and unlabeled samples in the feature space. We experimentally demonstrate that these two ingredients significantly contribute to clustering performance and lead our model to outperform state-of-the-art methods by a large margin (e.g., clustering accuracy +13% on CIFAR-100 and +8% on ImageNet).

关键词： computer vision Aggregates Feature extraction pattern recognition Task analysis

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View Generalization for Single Image Textured 3D Models

View Generalization for Single Image Textured 3D Models

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

作者： Bhattad, Anand Dundar, Aysegul Liu, Guilin Tao, Andrew Catanzaro, Bryan Univ Illinois Urbana IL 61801 USA Bilkent Univ Ankara Turkey NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Humans can easily infer the underlying 3D geometry and texture of an object only from a single 2D image. Current computer vision methods can do this, too, but suffer from view generalization problems - the models inferred tend to make poor predictions of appearance in novel views. As for generalization problems in machine learning, the difficulty is balancing single-view accuracy (cf training error;bias) with novel view accuracy (cf test error;variance). We describe a class of models whose geometric rigidity is easily controlled to manage this tradeoff. We describe a cycle consistency loss that improves view generalization (roughly, a model from a generated view should predict the original view well). View generalization of textures requires that models share texture information, so a car seen from the back still has headlights because other cars have headlights. We describe a cycle consistency loss that encourages model textures to be aligned, so as to encourage sharing. We compare our method against the state-of-the-art method and show both qualitative and quantitative improvements.

关键词： Training Solid modeling computer vision Adaptation models Three-dimensional displays Shape Predictive models

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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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Bottleneck Transformers for Visual recognition

Bottleneck Transformers for Visual Recognition

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

作者： Srinivas, Aravind Lin, Tsung-Yi Parmar, Niki Shlens, Jonathon Abbeel, Pieter Vaswani, Ashish Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in the final three bottleneck blocks of a ResNet and no other changes, our approach improves upon the baselines significantly on instance segmentation and object detection while also reducing the parameters, with minimal overhead in latency. Through the design of BoTNet, we also point out how ResNet bottleneck blocks with self-attention can be viewed as Transformer blocks. Without any bells and whistles, BoTNet achieves 44.4% Mask AP and 49.7% Box AP on the COCO Instance Segmentation benchmark using the Mask R-CNN framework;surpassing the previous best published single model and single scale results of ResNeSt [67] evaluated on the COCO validation set. Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84.7% top-1 accuracy on the ImageNet benchmark while being up to 1.64x faster in "compute"(1) time than the popular EfficientNet models on TPU-v3 hardware. We hope our simple and effective approach will serve as a strong baseline for future research in self-attention models for vision.(2)

关键词： computer vision Image segmentation Adaptation models Computational modeling Botnet computer architecture Object detection

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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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Personalized Outfit Recommendation with Learnable Anchors

Personalized Outfit Recommendation with Learnable Anchors

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

作者： Lu, Zhi Hu, Yang Chen, Yan Zeng, Bing Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9781665445092

The multimedia community has recently seen a tremendous surge of interest in the fashion recommendation problem. A lot of efforts have been made to model the compatibility between fashion items. Some have also studied users' personal preferences for the outfits. There is, however, another difficulty in the task that hasn't been dealt with carefully by previous work. Users that are new to the system usually only have several (less than 5) outfits available for learning. With such a limited number of training examples, it is challenging to model the user's preferences reliably. In this work, we propose a new solution for personalized outfit recommendation that is capable of handling this case. We use a stacked self-attention mechanism to model the high-order interactions among the items. We then embed the items in an outfit into a single compact representation within the outfit space. To accommodate the variety of users' preferences, we characterize each user with a set of anchors, i.e. a group of learnable latent vectors in the outfit space that are the representatives of the outfits the user likes. We also learn a set of general anchors to model the general preference shared by all users. Based on this representation of the outfits and the users, we propose a simple but effective strategy for the new user profiling tasks. Extensive experiments on large scale real-world datasets demonstrate the performance of our proposed method.

关键词： Training computer vision Computational modeling pattern recognition Reliability Task analysis Surges

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