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

共 53128 条记录，以下是4421-4430 订阅

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YOLOv7-ConvTrans: Hybrid vision Transformer with YOLOv7 for Underwater Object Detection 4

YOLOv7-ConvTrans: Hybrid Vision Transformer with YOLOv7 for ...

引用

4th ieee International conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Delsey, M.J. Bibal Benifa, J.V. Indian Institute of Information Technology Department of Computer Science and Engineering Kottayam India

ISBN: (数字)9798331528348

ISBN: (纸本)9798350352931

Underwater object detection plays a significant role in marine exploration activities such as ecological monitoring, conservation of undersea ecosystems, and underwater robotics. In contrast to detection in the atmosphere on land, underwater detection is challenging due to color distortion and attenuation, which render the detection process more difficult and imprecise. To address these issues, this paper proposes an improved detection framework, YOLOv7-ConvTrans. The YOLOv7 architecture is optimized by integrating a convolution vision transformer in the neck region to enhance the detection accuracy of small and occluded objects. The proposed method reduces redundancy, computational cost, and recognition time to extract significant feature maps from the imagery. In the experimental assessment on the URPC2019 dataset, a mAP of 80.39 and on the Brackish dataset, 92.61, indicate high detection accuracy. Finally, our proposed approach provides a valuable framework for designing underwater vision systems that can be utilized in various scientific and exploratory domains. © 2024 ieee.

关键词： Machine vision

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Distilling Knowledge via Knowledge Review

Distilling Knowledge via Knowledge Review

引用

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

作者： Chen, Pengguang Liu, Shu Zhao, Hengshuang Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network. Previous methods mostly focus on proposing feature transformation and loss functions between the same level's features to improve the effectiveness. We differently study the factor of connection path cross levels between teacher and student networks, and reveal its great importance. For the first time in knowledge distillation, cross-stage connection paths are proposed. Our new review mechanism is effective and structurally simple. Our finally designed nested and compact framework requires negligible computation overhead, and outperforms other methods on a variety of tasks. We apply our method to classification, object detection, and instance segmentation tasks. All of them witness significant student network performance improvement.

关键词： Knowledge engineering computer vision Object detection pattern recognition Task analysis

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Learning Video Representations of Human Motion from Synthetic Data

Learning Video Representations of Human Motion from Syntheti...

引用

2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Guo, Xi Wu, Wei Wang, Dongliang Su, Jing Su, Haisheng Gan, Weihao Huang, Jian Yang, Qin Beihang University China SenseTime Research

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we take an early step towards video representation learning of human actions with the help of large-scale synthetic videos, particularly for human motion representation enhancement. Specifically, we first introduce an automatic action-related video synthesis pipeline based on a photorealistic video game. A large-scale human action dataset named GATA (GTA Animation Transformed Actions) is then built by the proposed pipeline, which includes 8.1 million action clips spanning over 28K action classes. Based on the presented dataset, we design a contrastive learning framework for human motion representation learning, which shows significant performance improvements on several typical video datasets for action recognition, e.g., Charades, HAA 500 and NTU-RGB. Besides, we further explore a domain adaptation method based on cross-domain positive pairs mining to alleviate the domain gap between synthetic and realistic data. Extensive properties analyses of learned representation are conducted to demonstrate the effectiveness of the proposed dataset for enhancing human motion representation learning. © 2022 ieee.

关键词： Representation learning computer vision Pipelines Games Animation Data mining

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Positive-Congruent Training: Towards Regression-Free Model Updates

Positive-Congruent Training: Towards Regression-Free Model U...

引用

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

作者： Yan, Sijie Xiong, Yuanjun Kundu, Kaustav Yang, Shuo Deng, Siqi Wang, Meng Xia, Wei Soatto, Stefano Amazon AI AWS Seattle WA 98195 USA

ISBN: (纸本)9781665445092

Reducing inconsistencies in the behavior of different versions of an AI system can be as important in practice as reducing its overall error. In image classification, sample-wise inconsistencies appear as "negative flips": A new model incorrectly predicts the output for a test sample that was correctly classified by the old (reference) model. Positive-congruent (PC) training aims at reducing error rate while at the same time reducing negative flips, thus maximizing congruency with the reference model only on positive predictions, unlike model distillation. We propose a simple approach for PC training, Focal Distillation, which enforces congruence with the reference model by giving more weights to samples that were correctly classified. We also found that, if the reference model itself can be chosen as an ensemble of multiple deep neural networks, negative flips can be further reduced without affecting the new model's accuracy.

关键词： Training Deep learning computer vision Error analysis Computational modeling Predictive models pattern recognition

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Learning Semantic-Aware Dynamics for Video Prediction

Learning Semantic-Aware Dynamics for Video Prediction

引用

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

作者： Bei, Xinzhu Yang, Yanchao Soatto, Stefano Univ Calif Los Angeles Vis Lab Los Angeles CA 90024 USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

We propose an architecture and training scheme to predict video frames by explicitly modeling dis-occlusions and capturing the evolution of semantically consistent regions in the video. The scene layout (semantic map) and motion (optical flow) are decomposed into layers, which are predicted and fused with their context to generate future layouts and motions. The appearance of the scene is warped from past frames using the predicted motion in co-visible regions;dis-occluded regions are synthesized with content-aware inpainting utilizing the predicted scene layout. The result is a predictive model that explicitly represents objects and learns their class-specific motion, which we evaluate on video prediction benchmarks.

关键词： Training computer vision Layout Semantics Dynamics computer architecture Predictive models

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UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning

UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Lea...

引用

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

作者： Luo, Kunming Wang, Chuan Liu, Shuaicheng Fan, Haoqiang Wang, Jue Sun, Jian Megvii Technol Beijing Peoples R China Univ Elect Sci & Technol China Beijing Peoples R China

ISBN: (纸本)9781665445092

We present an unsupervised learning approach for optical flow estimation by improving the upsampling and learning of pyramid network. We design a self-guided upsample module to tackle the interpolation blur problem caused by bilinear upsampling between pyramid levels. Moreover, we propose a pyramid distillation loss to add supervision for intermediate levels via distilling the finest flow as pseudo labels. By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015. In particular, we achieve EPE=1.4 on KITTI 2012 and F1=9.38% on KITTI 2015, which outperform the previous state-of-the-art methods by 22.2% and 15.7%, respectively.

关键词： Optical losses Interpolation computer vision Estimation Benchmark testing pattern recognition Optical flow

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VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of Experts

VDSM: Unsupervised Video Disentanglement with State-Space Mo...

引用

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

作者： Vowels, Matthew J. Camgoz, Necati Cihan Bowden, Richard Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9781665445092

Disentangled representations support a range of downstream tasks including causal reasoning, generative modeling, and fair machine learning. Unfortunately, disentanglement has been shown to be impossible without the incorporation of supervision or inductive bias. Given that supervision is often expensive or infeasible to acquire, we choose to incorporate structural inductive bias and present an unsupervised, deep State-Space-Model for Video Disentanglement (VDSM). The model disentangles latent time-varying and dynamic factors via the incorporation of hierarchical structure with a dynamic prior and a Mixture of Experts decoder. VDSM learns separate disentangled representations for the identity of the object or person in the video, and for the action being performed. We evaluate VDSM across a range of qualitative and quantitative tasks including identity and dynamics transfer;sequence generation, Frechet Inception Distance, and factor classification. VDSM achieves state-of-the-art performance and exceeds adversarial methods, even when the methods use additional supervision.

关键词： computer vision Computational modeling Machine learning Cognition pattern recognition Decoding Task analysis

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Information-Theoretic Segmentation by Inpainting Error Maximization

Information-Theoretic Segmentation by Inpainting Error Maxim...

引用

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

作者： Savarese, Pedro Kim, Sunnie S. Y. Maire, Michael Shakhnarovich, Greg McAllester, David TTI Chicago Chicago IL 60637 USA Princeton Univ Princeton NJ 08544 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9781665445092

We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. More specifically, we group image pixels into foreground and background, with the goal of minimizing predictability of one set from the other. An easily computed loss drives a greedy search process to maximize inpainting error over these partitions. Our method does not involve training deep networks, is computationally cheap, class-agnostic, and even applicable in isolation to a single unlabeled image. Experiments demonstrate that it achieves a new state-of-the-art in unsupervised segmentation quality, while being substantially faster and more general than competing approaches.

关键词： Training Deep learning Image segmentation computer vision Computational modeling pattern recognition Task analysis

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Image Dense Captioning of Irregular Regions Based on Visual Saliency

Image Dense Captioning of Irregular Regions Based on Visual ...

引用

2023 International conference on pattern recognition, Machine vision and Intelligent Algorithms, PRMVIA 2023

作者： Wen, Xiaosheng Jian, Ping Beijing Institute of Technology School of Computer Science Technology Beijing100081 China

ISBN: (纸本)9798350346596

Traditional Dense Captioning intends to describe local details of image with natural language. It usually uses target detection first and then describes the contents in the detected bounding box, which will make the description content rich. But captioning based on target detection often lacks the attention to the association between objects and the environment, or between the objects. And for now, there is no dense captioning method has the ability to deal with irregular areas. To solve these problems, we propose a visual-saliency based region division method. It focuses more on areas than just on objects. Based on the division, the local description of the irregular region is carried out. For each area, we combine the image with the target area to generate features, which are put into the caption model. We used the Visual Genome dataset for training and testing. Through experiments, our model is comparable to the baseline under the traditional bounding box. And the description of irregular region generated by our method is equally good. Our model performs well in image retrieval experiments and has less information redundancy. In the application, we support to manually select the region of interest on the image for description, for assist in expanding the dataset. © 2023 ieee.

关键词： Redundancy

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Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning

Exponential Moving Average Normalization for Self-supervised...

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

作者： Cai, Zhaowei Ravichandran, Avinash Maji, Subhransu Fowlkes, Charless Tu, Zhuowen Soatto, Stefano Amazon Web Serv Seattle WA 98109 USA

ISBN: (纸本)9781665445092

We present a plug-in replacement for batch normalization (BN) called exponential moving average normalization (EMAN), which improves the performance of existing student-teacher based self- and semi-supervised learning techniques. Unlike the standard BN, where the statistics are computed within each batch, EMAN, used in the teacher, updates its statistics by exponential moving average from the BN statistics of the student. This design reduces the intrinsic cross-sample dependency of BN and enhances the generalization of the teacher. EMAN improves strong baselines for self-supervised learning by 4-6/1-2 points and semi-supervised learning by about 7/2 points, when 1%/10% supervised labels are available on ImageNet. These improvements are consistent across methods, network architectures, training duration, and datasets, demonstrating the general effectiveness of this technique. The code will be made available online.

关键词： Training computer vision Codes Semisupervised learning Network architecture pattern recognition Standards

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