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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是321-330 订阅

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NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation 31

NU-Net: Deep Residual Wide Field of View Convolutional Neura...

引用

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

作者： Samy, Mohamed Amer, Karim Eissa, Kareem Shaker, Mahmoud ElHelw, Mohamed Nile Univ Ctr Informat Sci Giza Egypt

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Semantic Segmentation of satellite images is one of the most challenging problems in computer vision as it requires a model capable of capturing both local and global information at each pixel. Current state of the art methods are based on Fully Convolutional Neural Networks (FCNN) with mostly two main components: an encoder which is a pretrained classification model that gradually reduces the input spatial size and a decoder that transforms the encoder's feature map into a predicted mask with the original size. We change this conventional architecture to a model that makes use of full resolution information. NU-Net is a deep FCNN that is able to capture wide field of view global information around each pixel while maintaining localized full resolution information throughout the model. We evaluate our model on the Land Cover Classification and Road Extraction tracks in the DeepGlobe competition.

关键词： Image segmentation Semantics Roads computer architecture Convolution computer vision pattern recognition

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RIT-18: A Novel Dataset for Compositional Group Activity Understanding

RIT-18: A Novel Dataset for Compositional Group Activity Und...

引用

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

作者： Chen, Junwen Hao, Haiting Hong, Hanbin Kong, Yu Rochester Inst Technol Golisano Coll Comp & Informat Sci Rochester NY 14623 USA

ISBN: (纸本)9781728193601

Group activity understanding is a challenging task as multiple people are involved, and their relations may vary over time. Currently, the literature of group activity is limited to group activity recognition, because videos are trimmed in very short duration and focus on a single activity. This slows down the progress in the group activity domain. In this paper, we propose a new large-scale untrimmed compositional group activity dataset RIT-18 based on the volleyball games captured from YouTube. Each clip in our dataset depicts an entire rally which spans the duration from serve to a point being scored. Comprehensive annotations including group activity labels, temporal boundaries of activities, key persons, and winning teams are provided. We describe group activity recognition, future activity anticipation, and rally-level winner prediction challenges, and evaluate several baseline methods over these challenges. We report their performance on our dataset and demonstrate further efforts need to be made. The dataset is available at https://***/actionlab/rit-18.

关键词： computer vision

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Pay Attention to Virality: understanding popularity of social media videos with the attention mechanism 31

Pay Attention to Virality: understanding popularity of socia...

引用

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

作者： Bielski, Adam Trzcinski, Tomasz Tooploox Wroclaw Poland Warsaw Univ Technol Tooploox Warsaw Poland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Predicting popularity of social media videos before they are published is a challenging task, mainly due to the complexity of content distribution network as well as the number of factors that play part in this process. As solving this task provides tremendous help for media content creators, many successful methods were proposed to solve this problem with machine learning. In this work, we change the viewpoint and postulate that it is not only the predicted popularity that matters, but also, maybe even more importantly, understanding of how individual parts influence the final popularity score. To that end, we propose to combine the Grad-CAM visualization method with a soft attention mechanism. Our preliminary results show that this approach allows for more intuitive interpretation of the content impact on video popularity, while achieving competitive results in terms of prediction accuracy.

关键词： Videos Visualization Social network services conferences Task analysis Heating systems computer vision

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AAPL: Adding Attributes to Prompt Learning for vision-Language Models

AAPL: Adding Attributes to Prompt Learning for Vision-Langua...

引用

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

作者： Kim, Gahyeon Kim, Sohee Lee, Seokju Korea Inst Energy Technol KENTECH Naju South Korea

ISBN: (纸本)9798350365474

Recent advances in large pre-trained vision-language models have demonstrated remarkable performance on zero-shot downstream tasks. Building upon this, recent studies, such as CoOp and CoCoOp, have proposed the use of prompt learning, where context within a prompt is replaced with learnable vectors, leading to significant improvements over manually crafted prompts. However, the performance improvement for unseen classes is still marginal, and to tackle this problem, data augmentation has been frequently used in traditional zero-shot learning techniques. Through our experiments, we have identified important issues in CoOp and CoCoOp: the context learned through traditional image augmentation is biased toward seen classes, negatively impacting generalization to unseen classes. To address this problem, we propose adversarial token embedding to disentangle low-level visual augmentation features from highlevel class information when inducing bias in learnable prompts. Through our novel mechanism called "Adding Attributes to Prompt Learning", AAPL, we guide the learnable context to effectively extract text features by focusing on high-level features for unseen classes. We have conducted experiments across 11 datasets, and overall, AAPL shows favorable performances compared to the existing methods in few-shot learning, zero-shot learning, cross-dataset, and domain generalization tasks.

关键词： prompt learning vision language model VLMs

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Learning Optimized Low-Light Image Enhancement for Edge vision Tasks

Learning Optimized Low-Light Image Enhancement for Edge Visi...

引用

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

作者： Sharif, S. M. A. Myrzabekov, Azamat Khujaev, Nodirkhuja Tsoy, Roman Kim, Seongwan Lee, Jaeho LG Sciencepk Seoul South Korea

ISBN: (纸本)9798350365474

Low-light image enhancement (LLIE) has a significant role in edge vision applications (EVA). Despite its widespread practicability, the existing LLIE methods are impractical due to their high computational costs. This study proposed a framework to learn optimized low-light image enhancement to tackle the limitations of existing enhancement methods for accelerating EVA. The proposed framework incorporates a lightweight and mobile-friendly deep network. We optimized our proposed model with INT8 precision with a post-training quantization strategy and deployed it on an edge device. The LLIE model has achieved over 199 frames per second (FPS) on a low-power edge board. Additionally, we evaluated the practicability of an optimized model for accelerating the vision application of an edge environment. The experimental results illustrate that our optimized method can significantly accelerate the performance of SOTA vision algorithms in challenging low-light conditions for numerous everyday vision tasks, including object detection and image registration.

关键词： Edge Device Edge LLIE Edge vision Task Low-light Image Enhancement Optimized LLIE Quantization

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Guidance Network with Staged Learning for Image enhancement

Guidance Network with Staged Learning for Image enhancement

引用

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

作者： Liang, Luming Zharkov, Ilya Amjadi, Faezeh Joze, Hamid Reza Vaezi Pradeep, Vivek Microsoft One Microsoft Way Redmond WA 98052 USA

ISBN: (纸本)9781665448994

Many important yet not fully resolved problems in computational photography and image enhancement, e.g. generating well-lit images from their low-light counterparts or producing RGB images from their RAW camera inputs share a common nature: discovering a color mapping between input pixels to output pixels based on both global information and local details. We propose a novel deep neural network architecture to learn the RAW to RGB mapping based on this common nature. This architecture consists of both global and local sub-networks, where the first sub-network focuses on determining illumination and color mapping, the second sub-network deals with recovering image details. The result of the global network serves as a guidance to the local network to form the final RGB images. Our method outperforms state-of-the-art with a significantly smaller size of network features on various image enhancement tasks.

关键词： Training Photography Image color analysis Lighting computer architecture Cameras Service-oriented architecture

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Cascaded Siamese Self-supervised Audio to Video GAN

Cascaded Siamese Self-supervised Audio to Video GAN

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

作者： Aldausari, Nuha Sowmya, Arcot Marcus, Nadine Mohammadi, Gelareh Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generating meaningful videos that are synchronised to audio signals is a complex synthesis task that requires generation of not only realistic videos but also coherent video motions that conform to the provided audio signals. While tremendous effort has been expended on audio-to-video generative models, these models rely heavily on supervised signals such as face/body key points or 3D meshes. However, key point annotation requires time and effort. Besides, some dataset domains do not have predictable structure, which makes the extraction of points of interest infeasible. Our proposed model consists of a cascaded generator-discriminator architecture that works at the pixel level to generate videos according to the associated soundtracks. It adopts a new self-supervised temporal augmentation technique to optimise the correlation between the audio signal and the generated video instead of relying on supervised signals. The proposed architecture has proven its effectiveness in extensive experiments that compared different models across two datasets.

关键词： Solid modeling Correlation Three-dimensional displays Computational modeling conferences computer architecture pattern recognition

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Joint Forecasting of Panoptic Segmentations with Difference Attention

Joint Forecasting of Panoptic Segmentations with Difference ...

引用

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

作者： Graber, Colin Jazra, Cyril Luo, Wenjie Gui, Liangyan Schwing, Alexander Univ Illinois Urbana IL 61801 USA Waymo Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665487399

Forecasting of a representation is important for safe and effective autonomy. For this, panoptic segmentations have been studied as a compelling representation in recent work. However, recent state-of-the-art on panoptic segmentation forecasting suffers from two issues: first, individual object instances are treated independently of each other;second, individual object instance forecasts are merged in a heuristic manner. To address both issues, we study a new panoptic segmentation forecasting model that jointly forecasts all object instances in a scene using a transformer model based on 'difference attention.' It further refines the predictions by taking depth estimates into account. We evaluate the proposed model on the Cityscapes and AIODrive datasets. We find difference attention to be particularly suitable for forecasting because the difference of quantities like locations enables a model to explicitly reason about velocities and acceleration. Because of this, we attain state-of-the-art on panoptic segmentation forecasting metrics.

关键词： Measurement computer vision conferences Predictive models Transformers pattern recognition Forecasting

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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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Symmetric Network with Spatial Relationship Modeling for Natural Language-based Vehicle Retrieval

Symmetric Network with Spatial Relationship Modeling for Nat...

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

作者： Zhao, Chuyang Chen, Haobo Zhang, Wenyuan Chen, Junru Zhang, Sipeng Li, Yadong Li, Boxun MEGVII Technol Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Natural language (NL) based vehicle retrieval aims to search specific vehicle given text description. Different from the image-based vehicle retrieval, NL-based vehicle retrieval requires considering not only vehicle appearance, but also surrounding environment and temporal relations. In this paper, we propose a Symmetric Network with Spatial Relationship Modeling (SSM) method for NL-based vehicle retrieval. Specifically, we design a symmetric network to learn the unified cross-modal representations between text descriptions and vehicle images, where vehicle appearance details and vehicle trajectory global information are preserved. Besides, to make better use of location information, we propose a spatial relationship modeling methods to take surrounding environment and mutual relationship between vehicles into consideration. The qualitative and quantitative experiments verify the effectiveness of the proposed method. We achieve 43.92% MRR accuracy on the test set of the 6th AI City Challenge on natural language-based vehicle retrieval track, yielding the 4th place on the public leaderboard.

关键词： Data privacy Visualization conferences Urban areas Linguistics Trajectory pattern recognition

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