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

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Diverse Branch Block: Building a Convolution as an Inception-like Unit

Diverse Branch Block: Building a Convolution as an Inception...

引用

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

作者： Ding, Xiaohan Zhang, Xiangyu Han, Jungong Ding, Guiguang Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China MEGVII Technol Beijing Peoples R China Aberystwyth Univ Comp Sci Dept Aberystwyth SY23 3FL Dyfed Wales MEGVII Beijing Peoples R China

ISBN: (纸本)9781665445092

We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexities to enrich the feature space, including sequences of convolutions, multi-scale convolutions, and average pooling. After training, a DBB can be equivalently converted into a single conv layer for deployment. Unlike the advancements of novel ConvNet architectures, DBB complicates the training-time microstructure while maintaining the macro architecture, so that it can be used as a drop-in replacement for regular conv layers of any architecture. In this way, the model can be trained to reach a higher level of performance and then transformed into the original inference-time structure for inference. DBB improves ConvNets on image classification (up to 1.9% higher top-1 accuracy on ImageNet), object detection and semantic segmentation. The PyTorch code and models are released at https:// ***/ DingXiaoH/DiverseBranchBlock.

关键词： Training Image segmentation Costs Convolution Semantics computer architecture Object detection

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ReMOVE: A Reference-free Metric for Object Erasure

ReMOVE: A Reference-free Metric for Object Erasure

引用

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

作者： Aditya Chandrasekar Goirik Chakrabarty Jai Bardhan Ramya Hebbalaguppe Prathosh AP TCS Research IISc Bangalore IIT Delhi

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce ReMOVE, a novel reference-free metric for assessing object erasure efficacy in diffusion-based image editing models post-generation. Unlike existing measures such as LPIPS and CLIPScore, ReMOVE addresses the challenge of evaluating inpainting without a reference image, common in practical scenarios. It effectively distinguishes between object removal and replacement. This is a key issue in diffusion models due to stochastic nature of image generation. Traditional metrics fail to align with the intuitive definition of inpainting, which aims for (1) seamless object removal within masked regions (2) while preserving the background continuity. ReMOVE not only correlates with state-of-the-art metrics and aligns with human perception but also captures the nuanced aspects of the inpainting process, providing a finer-grained evaluation of the generated outputs.

关键词： Measurement computer vision Image synthesis conferences Stochastic processes Diffusion models pattern recognition

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Phenology Alignment Network: A Novel Framework for Cross-Regional Time Series Crop Classification

Phenology Alignment Network: A Novel Framework for Cross-Reg...

引用

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

作者： Wang, Ziqiao Zhang, Hongyan He, Wei Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China RIKEN Ctr Adv Intelligence Project AIP Tokyo 1030027 Japan

ISBN: (纸本)9781665448994

Timely and accurate crop type classification plays an essential role in the study of agricultural application. However, large area or cross-regional crop classification confronts huge challenges owing to dramatic phenology discrepancy among training and test regions. In this work, we propose a novel framework to address these challenges based on deep recurrent network and unsupervised domain adaptation (DA). Specifically, we firstly propose a Temporal Spatial Network (TSNet) for pixelwise crop classification, which contains stacked RNN and self-attention module to adaptively extract multi-level features from crop samples under various planting conditions. To deal with the cross-regional challenge, an unsupervised DA-based framework named Phenology Alignment Network (PAN) is proposed. PAN consists of two branches of two identical TSNet pre-trained on source domain;one branch takes source samples while the other takes target samples as input. Through aligning the hierarchical deep features extracted from two branches, the discrepancy between two regions is decreased and the pre-trained model is adapted to the target domain without using target label information. As another contribution, a time series dataset based on Sentinel-2 was annotated containing winter crop samples collected on three study sites of China. Cross-regional experiments demonstrate that TSNet shows comparable accuracy to state-of-the-art methods, and PAN further improves the overall accuracy by 5.62%, and macro average F1 score by 0.094 unsupervisedly.

关键词： Training computer vision Adaptation models conferences Time series analysis Feature extraction Agriculture

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MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions

MotionRNN: A Flexible Model for Video Prediction with Spacet...

引用

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

作者： Wu, Haixu Yao, Zhiyu Wang, Jianmin Long, Mingsheng Tsinghua Univ Sch Software BNRist Beijing Peoples R China

ISBN: (纸本)9781665445092

This paper tackles video prediction from a new dimension of predicting spacetime-varying motions that are incessantly changing across both space and time. Prior methods mainly capture the temporal state transitions but overlook the complex spatiotemporal variations of the motion itself, making them difficult to adapt to ever-changing motions. We observe that physical world motions can be decomposed into transient variation and motion trend, while the latter can be regarded as the accumulation of previous motions. Thus, simultaneously capturing the transient variation and the motion trend is the key to make spacetime-varying motions more predictable. Based on these observations, we propose the MotionRNN framework, which can capture the complex variations within motions and adapt to spacetime-varying scenarios. MotionRNN has two main contributions. The first is that we design the MotionGRU unit, which can model the transient variation and motion trend in a unified way. The second is that we apply the MotionGRU to RNN-based predictive models and indicate a new flexible video prediction architecture with a Motion Highway, which can significantly improve the ability to predict changeable motions and avoid motion vanishing for stacked multiple-layer predictive models. With high flexibility, this framework can adapt to a series of models for deterministic spatiotemporal prediction. Our MotionRNN can yield significant improvements on three challenging benchmarks for video prediction with spacetime-varying motions.

关键词： Road transportation Adaptation models computer vision computer architecture Predictive models Market research Spatiotemporal phenomena

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Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration

Know Your Surroundings: Panoramic Multi-Object Tracking by M...

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

作者： He, Yuhang Yu, Wentao Han, Jie Wei, Xing Hong, Xiaopeng Gong, Yihong Xi An Jiao Tong Univ Coll Artificial Intelligence Xian Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian Peoples R China Peng Cheng Lab Res Ctr Artificial Intelligence Shenzhen Peoples R China

ISBN: (纸本)9781665448994

In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation. Most existing MOT methods track multiple objects using a singular RGB camera, which are prone to camera field-of-view and suffer tracking failures in complex scenarios due to background clutters and poor light conditions. To meet these challenges, we propose a MultiModality PAnoramic multi-object Tracking framework (MMPAT), which takes both 2D panorama images and 3D point clouds as input and then infers target trajectories using the multimodality data. The proposed method contains four major modules, a panorama image detection module, a multimodality data fusion module, a data association module and a trajectory inference model. We evaluate the proposed method on the JRDB dataset, where the MMPAT achieves the top performance in both the detection and tracking tasks and significantly outperforms state-of-the-art methods by a large margin (15.7 and 8.5 improvement in terms of AP and MOTA, respectively).

关键词： Three-dimensional displays Target tracking Navigation Robot vision systems Object detection Cameras Data models

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Image Enhancement for Iris Analysis Using Deep Learning Algorithms - A Brief Survey 2

Image Enhancement for Iris Analysis Using Deep Learning Algo...

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2nd International conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Poovayar Priya, M. Ezhilarasan, M. Pondicherry Engineering College Dept. of Computer Science and Engineering Puducherry India Pondicherry Engineering College Dept. of Information Technology Puducherry India

ISBN: (纸本)9798350328202

With applications including medical image analysis, picture sharpening and restoration, robot vision, pattern recognition, and video processing, among many others, image enhancement is the main topic in image processing and computer vision. There have been many algorithms for improving images established in the past. More extensive work on image-enhancing techniques has been done utilizing deep learning models in recent years, as these algorithms have achieved their peak in vision applications. In this study, we present a thorough analysis of machine learning, deep learning, and literature. The literature on image enhancement is then summarised, and future approaches for iris analysis are discussed. © 2024 ieee.

关键词： Image enhancement

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Improved Image Matting via Real-time User Clicks and Uncertainty Estimation

Improved Image Matting via Real-time User Clicks and Uncerta...

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

作者： Wei, Tianyi Chen, Dongdong Zhou, Wenbo Liao, Jing Zhao, Hanqing Zhang, Weiming Yu, Nenghai Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Cloud AI Hefei Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high quality trimap itself is arduous, thus restricting the application of these methods. Recently, some trimap free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, without the trimap guidance in some cases, the target network is ambiguous about which is the foreground target. In fact, choosing the foreground is a subjective procedure and depends on the user's intention. To this end, this paper proposes an improved deep image matting framework which is trimap free and only needs several user click interactions to eliminate the ambiguity. Moreover, we introduce a new uncertainty estimation module that can predict which parts need polishing and a following local refinement module. Based on the computation budget, users can choose how many local parts to improve with the uncertainty guidance. Quantitative and qualitative results show that our method performs better than existing trimap free methods and comparably to state-of-the-art trimap-based methods with minimal user effort.

关键词： Graphics computer vision Uncertainty Estimation Real-time systems pattern recognition

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ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring

ARVo: Learning All-Range Volumetric Correspondence for Video...

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

作者： Li, Dongxu Xu, Chenchen Zhang, Kaihao Yu, Xin Zhong, Yiran Ren, Wenqi Suominen, Hanna Li, Hongdong Australian Natl Univ Canberra ACT Australia DATA61 CSIRO Sydney NSW Australia UTS Sydney NSW Australia Chinese Acad Sci IIE Beijing Peoples R China Univ Turku Turku Finland

ISBN: (纸本)9781665445092

Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions. In order to utilize neighboring sharp patches, typical methods rely mainly on homography or optical flows to spatially align neighboring blurry frames. However, such explicit approaches are less effective in the presence of fast motions with large pixel displacements. In this work, we propose a novel implicit method to learn spatial correspondence among blurry frames in the feature space. To construct distant pixel correspondences, our model builds a correlation volume pyramid among all the pixel-pairs between neighboring frames. To enhance the features of the reference frame, we design a correlative aggregation module that maximizes the pixel-pair correlations with its neighbors based on the volume pyramid. Finally, we feed the aggregated features into a reconstruction module to obtain the restored frame. We design a generative adversarial paradigm to optimize the model progressively. Our proposed method is evaluated on the widely-adopted DVD dataset, along with a newly collected High-Frame-Rate (1000 fps) Dataset for Video Deblurring (HFR-DVD). Quantitative and qualitative experiments show that our model performs favorably on both datasets against previous state-of-the-art methods, confirming the benefit of modeling all-range spatial correspondence for video deblurring.

关键词： Training Solid modeling computer vision Correlation DVD Computational modeling Cameras

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ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search

ViPNAS: Efficient Video Pose Estimation via Neural Architect...

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

作者： Xu, Lumin Guan, Yingda Jin, Sheng Liu, Wentao Qian, Chen Luo, Ping Ouyang, Wanli Wang, Xiaogang Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Beijing Peoples R China Univ Hong Kong Hong Kong Peoples R China Tetras AI Beijing Peoples R China Univ Sydney Camperdown NSW Australia

ISBN: (纸本)9781665445092

Human pose estimation has achieved significant progress in recent years. However, most of the recent methods focus on improving accuracy using complicated models and ignoring real-time efficiency. To achieve a better trade-off between accuracy and efficiency, we propose a novel neural architecture search (NAS) method, termed ViP-NAS, to search networks in both spatial and temporal levels for fast online video pose estimation. In the spatial level, we carefully design the search space with five different dimensions including network depth, width, kernel size, group number, and attentions. In the temporal level, we search from a series of temporal feature fusions to optimize the total accuracy and speed across multiple video frames. To the best of our knowledge, we are the first to search for the temporal feature fusion and automatic computation allocation in videos. Extensive experiments demonstrate the effectiveness of our approach on the challenging COCO2017 and PoseTrack2018 datasets. Our discovered model family, S-ViPNAS and T-ViPNAS, achieve significantly higher inference speed (CPU real-time) without sacrificing the accuracy compared to the previous state-of-the-art methods.

关键词： Training computer vision Costs Pose estimation computer architecture Streaming media Real-time systems

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An End-to-End vision Transformer Approach for Image Copy Detection

An End-to-End Vision Transformer Approach for Image Copy Det...

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

作者： Jiahe Steven Lee Wynne Hsu Mong Li Lee Institute of Data Science National University of Singapore Centre for Trusted Internet and Community National University of Singapore

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image copy detection is one of the pivotal tools to safeguard online information integrity. The challenge lies in determining whether a query image is an edited copy, which necessitates the identification of candidate source images through a retrieval process. The process requires discriminative features comprising of both global descriptors that are designed to be augmentation-invariant and local descriptors that can capture salient foreground objects to assess whether a query image is an edited copy of some source reference image. This work describes an end-to-end solution that leverage a vision Transformer model to learn such discriminative features and perform implicit matching between the query image and the reference image. Experimental results on two benchmark datasets demonstrate that the proposed solution outperforms state-of-the-art methods. Case studies illustrate the effectiveness of our approach in matching reference images from which the query images have been copy-edited.

关键词： computer vision conferences Benchmark testing Transformers Feature extraction pattern recognition Fake news

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