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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4721-4730 订阅

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Online Multi-camera People Tracking with Spatial-temporal Mechanism and Anchor-feature Hierarchical Clustering

Online Multi-camera People Tracking with Spatial-temporal Me...

引用

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

作者： Riu Cherdchusakulchai Sasin Phimsiri Visarut Trairattanapa Suchat Tungjitnob Wasu Kudisthalert Pornprom Kiawjak Ek Thamwiwatthana Phawat Borisuitsawat Teepakorn Tosawadi Pakcheera Choppradit Kasisdis Mahakijdechachai Supawit Vatathanavaro Worawit Saetan Vasin Suttichaya AI and Robotics Ventures (ARV) Thailand

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-camera Multi-object tracking (MTMC) surpasses conventional single-camera tracking by enabling seamless object tracking across multiple camera views. This capability is critical for security systems and improving situational awareness in various environments. This paper proposes a novel MTMC framework designed for online operation. The framework employs a three-stage pipeline: Multiobject Tracking (MOT), Multi-target Multi-camera Tracking (MTMC), and Cross Interval Synchronization (CIS). In the MOT stage, ReID features are extracted and localized tracklets are created. MTMC links these tracklets across cameras using spatial-temporal constraints and constraint hierarchical clustering with anchor features for improved inter-camera association. Finally, CIS ensures the temporal coherence of tracklets across time intervals. The proposed framework achieves robust tracking performance, validated on the challenging 2024 AI City Challenge with a HOTA score of 51.0556%, ranking sixth. The code is available at: https://***/ARV-MLCORE/AIC2024Track1ARV

关键词： Urban areas Pipelines Cameras Feature extraction Real-time systems pattern recognition Synchronization

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Relevance-CAM: Your Model Already Knows Where to Look

Relevance-CAM: Your Model Already Knows Where to Look

引用

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

作者： Lee, Jeong Ryong Kim, Sewon Park, Inyong Eo, Taejoon Hwang, Dosik Yonsei Univ Sch Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9781665445092

With increasing fields of application for neural networks and the development of neural networks, the ability to explain deep learning models is also becoming increasingly important. Especially, prior to practical applications, it is crucial to analyze a model's inference and the process of generating the results. A common explanation method is Class Activation Mapping(CAM) based method where it is often used to understand the last layer of the convolutional neural networks popular in the field of computer vision. In this paper, we propose a novel CAM method named Relevance-weighted Class Activation Mapping(Relevance-CAM) that utilizes Layer-wise Relevance Propagation to obtain the weighting components. This allows the explanation map to be faithful and robust to the shattered gradient problem, a shared problem of the gradient based CAM methods that causes noisy saliency maps for intermediate layers. Therefore, our proposed method can better explain a model by correctly analyzing the intermediate layers as well as the last convolutional layer. In this paper, we visualize how each layer of the popular image processing models extracts class specific features using Relevance-CAM, evaluate the localization ability, and show why the gradient based CAM cannot be used to explain the intermediate layers, proven by experimenting the weighting component. Relevance-CAM outperforms other CAM-based methods in recognition and localization evaluation in layers of any depth. The source code is available at: https://***/mongeoroo/Relevance-CAM

关键词： Deep learning Location awareness Analytical models computer vision Visualization Computational modeling Transfer learning

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Densely connected multidilated convolutional networks for dense prediction tasks

Densely connected multidilated convolutional networks for de...

引用

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

作者： Takahashi, Naoya Mitsufuji, Yuki Sony Corp Tokyo Japan

ISBN: (纸本)9781665445092

Tasks that involve high-resolution dense prediction require a modeling of both local and global patterns in a large input field. Although the local and global structures often depend on each other and their simultaneous modeling is important, many convolutional neural network (CNN)based approaches interchange representations in different resolutions only a few times. In this paper, we claim the importance of a dense simultaneous modeling of multiresolution representation and propose a novel CNN architecture called densely connected multidilated DenseNet (D3Net). D3Net involves a novel multidilated convolution that has different dilation factors in a single layer to model different resolutions simultaneously. By combining the multidilated convolution with the DenseNet architecture, D3Net incorporates multiresolution learning with an exponentially growing receptive field in almost all layers, while avoiding the aliasing problem that occurs when we naively incorporate the dilated convolution in DenseNet. Experiments on the image semantic segmentation task using Cityscapes and the audio source separation task using MUSDB18 show that the proposed method has superior performance over state-of-the-art methods.

关键词： Image segmentation Image resolution Source separation Convolution Semantics computer architecture Topology

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Rainbow Memory: Continual Learning with a Memory of Diverse Samples

Rainbow Memory: Continual Learning with a Memory of Diverse ...

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

作者： Bang, Jihwan Kim, Heesu Yoo, YoungJoon Ha, Jung-Woo Choi, Jonghyun Search Solut Inc Thousand Oaks CA USA NAVER CLOVA Seongnam South Korea NAVER AI Lab Seongnam South Korea GIST Gwangju South Korea

ISBN: (纸本)9781665445092

Continual learning is a realistic learning scenario for AI models. Prevalent scenario of continual learning, however;assumes disjoint sets of classes as tasks and is less realistic rather artificial. Instead, we focus on 'blurry' task boundary;where tasks shares classes and is more realistic and practical. To address such task, we argue the importance of diversity of samples in an episodic memory. To enhance the sample diversity in the memory, we propose a novel memory management strategy based on persample classification uncertainty and data augmentation, named Rainbow Memory (RM). With extensive empirical validations on MNIST CIFAR10, CIFAR100, and ImageNet datasets, we show that the proposed method significantly improves the accuracy in blurry continual learning setups, outperforming state of the arts by large margins despite its simplicity.

关键词： Training computer vision Uncertainty Codes Memory management Learning (artificial intelligence) Sampling methods

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Improving Unsupervised Image Clustering With Robust Learning

Improving Unsupervised Image Clustering With Robust Learning

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

作者： Park, Sungwon Han, Sungwon Kim, Sundong Kim, Danu Park, Sungkyu Hong, Seunghoon Cha, Meeyoung Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea Inst for Basic Sci Korea Data Sci Grp Daejeon South Korea

ISBN: (纸本)9781665445092

Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC's novelty is at utilizing pseudo-labels of existing image clustering models as a noisy dataset that may include misclassified samples. Its retraining process can revise misaligned knowledge and alleviate the overconfidence problem in predictions. The model's flexible structure makes it possible to be used as an add-on module to other clustering methods and helps them achieve better performance on multiple datasets. Extensive experiments show that the proposed model can adjust the model confidence with better calibration and gain additional robustness against adversarial noise.

关键词： Training computer vision Clustering methods Computational modeling Predictive models Robustness Calibration

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Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization

Coming Down to Earth: Satellite-to-Street View Synthesis for...

引用

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

作者： Toker, Aysim Zhou, Qunjie Maximov, Maxim Leal-Taixe, Laura Tech Univ Munich Munich Germany

ISBN: (纸本)9781665445092

The goal of cross-view image based geo-localization is to determine the location of a given street view image by matching it against a collection of geo-tagged satellite images. This task is notoriously challenging due to the drastic viewpoint and appearance differences between the two domains. We show that we can address this discrepancy explicitly by learning to synthesize realistic street views from satellite inputs. Following this observation, we propose a novel multi-task architecture in which image synthesis and retrieval are considered jointly. The rationale behind this is that we can bias our network to learn latent feature representations that are useful for retrieval if we utilize them to generate images across the two input domains. To the best of our knowledge, ours is the first approach that creates realistic street views from satellite images and localizes the corresponding query street-view simultaneously in an end-to-end manner. In our experiments, we obtain state-of-the-art performance on the CVUSA and CVACT benchmarks. Finally, we show compelling qualitative results for satellite-to-street view synthesis.

关键词： Earth Location awareness Satellites Image synthesis Semantics Image retrieval computer architecture

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Pseudo Facial Generation with Extreme Poses for Face recognition

Pseudo Facial Generation with Extreme Poses for Face Recogni...

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

作者： Wang, Guoli Ma, Jiaqi Zhang, Qian Lu, Jiwen Zhou, Jie Tsinghua Univ Beijing Peoples R China Wuhan Univ Wuhan Peoples R China Horizon Robot Beijing Peoples R China

ISBN: (纸本)9781665445092

Face recognition has achieved a great success in recent years, it is still challenging to recognize those facial images with extreme poses. Traditional methods consider it as a domain gap problem. Many of them settle it by generating fake frontal faces from extreme ones, whereas they are tough to maintain the identity information with high computational consumption and uncontrolled disturbances. Our experimental analysis shows a dramatic precision drop with extreme poses. Meanwhile, those extreme poses just exist minor visual differences after small rotations. Derived from this insight, we attempt to relieve such a huge precision drop by making minor changes to the input images without modifying existing discriminators. A novel lightweight pseudo facial generation is proposed to relieve the problem of extreme poses without generating any frontal facial image. It can depict the facial contour information and make appropriate modifications to preserve the critical identity information. Specifically, the proposed method reconstructs pseudo profile faces by minimizing the pixel-wise differences with original profile faces and maintaining the identity consistent information from their corresponding frontal faces simultaneously. The proposed framework can improve existing discriminators and obtain a great promotion on several benchmark datasets.

关键词： Visualization computer vision Image recognition Face recognition Computational modeling Benchmark testing Generators

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Multi-modal Aerial View Image Challenge: Sensor Domain Translation

Multi-modal Aerial View Image Challenge: Sensor Domain Trans...

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

作者： Spencer Low Oliver Nina Dylan Bowald Angel D. Sappa Nathan Inkawhich Peter Bruns Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH ESPOL Polytechnic University Ecuador Computer Vision Center Spain Air Force Research Laboratory Rome NY University of Utah Salt Lake UT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper describes the design, outcomes, and top methods of the 2nd annual Multi-modal Aerial View Image Challenge (MAVIC) aimed at cross modality aerial image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. Specifically, the challenge centers on translation between synthetic aperture radar (SAR), electro-optical (EO), camera (RGB), and infrared (IR) sensor modalities, a budding area of research that has begun to garner attention. While last year’s inaugural challenge demonstrated the feasibility of SAR→EO translation, this year’s challenge made significant improvements in dataset coverage, sensor variation, experimental design, and methods covering the tasks of SAR→EO, SAR→RGB, SAR→IR, RGB→IR introducing a new dataset called translation. By Multi-modal Aerial Gathered Image Composites (MAGIC); multimodal image translation is available for different comparisons. With a more rigorous set of translation performance metrics, winners were determined from aggregation of L1-norm, LPIPS (Learned Perceptual Image Patch Similarity, and FID (Frechet Inception Distance) scores. The wining methods included the pix2pixHD and LPIPS metrics as loss functions with an aggregated score 5% better separated by the SAR→EO and RGB→IR translation scores.

关键词： Measurement computer vision Design methodology conferences Cameras pattern recognition Synthetic aperture radar

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Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations

Right for the Right Concept: Revising Neuro-Symbolic Concept...

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

作者： Stammer, Wolfgang Schramowski, Patrick Kersting, Kristian Tech Univ Darmstadt Comp Sci Dept Darmstadt Germany Tech Univ Darmstadt Ctr Cognit Sci Darmstadt Germany Hessian Ctr AI Hessian AI Darmstadt Germany

ISBN: (纸本)9781665445092

Most explanation methods in deep learning map importance estimates for a model's prediction back to the original input space. These "visual" explanations are often insufficient, as the model's actual concept remains elusive. Moreover, without insights into the model's semantic concept, it is difficult -if not impossible- to intervene on the model's behavior via its explanations, called Explanatory Interactive Learning. Consequently, we propose to intervene on a Neuro-Symbolic scene representation, which allows one to revise the model on the semantic level, e.g. "never focus on the color to make your decision". We compiled a novel confounded visual scene data set, the CLEVR-Hans data set, capturing complex compositions of different objects. The results of our experiments on CLEVR-Hans demonstrate that our semantic explanations, i.e. compositional explanations at a per-object level, can identify confounders that are not identifiable using "visual" explanations only. More importantly, feedback on this semantic level makes it possible to revise the model from focusing on these factors.

关键词： Training Deep learning Visualization computer vision Image color analysis Semantics Focusing

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NeRD: Neural 3D Reflection Symmetry Detector

NeRD: Neural 3D Reflection Symmetry Detector

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

作者： Zhou, Yichao Liu, Shichen Ma, Yi Univ Calif Berkeley Berkeley CA 94720 USA Univ Southern Calif Los Angeles CA 90089 USA

ISBN: (纸本)9781665445092

Recent advances have shown that symmetry, a structural prior that most objects exhibit, can support a variety of single-view 3D understanding tasks. However, detecting 3D symmetry from an image remains a challenging task. Previous works either assume the symmetry is given or detect the symmetry with a heuristic-based method. In this paper, we present NeRD, a Neural 3D Reflection Symmetry Detector, which combines the strength of learning-based recognition and geometry-based reconstruction to accurately recover the normal direction of objects' mirror planes. Specifically, we enumerate the symmetry planes with a coarse-to-fine strategy and find the best ones by building 3D cost volumes to examine the intra-image pixel correspondence from the symmetry. Our experiments show that the symmetry planes detected with our method are significantly more accurate than the planes from direct CNN regression on both synthetic and real datasets. More importantly, we also demonstrate that the detected symmetry can be used to improve the performance of downstream tasks such as pose estimation and depth map regression by a wide margin over existing methods. The code of this paper has been made public at https://***/zhou13/nerd.

关键词： computer vision Three-dimensional displays Costs Pose estimation Detectors Reflection pattern recognition

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