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CSG0: Continual Urban Scene Generation with Zero Forgetting

CSG0: Continual Urban Scene Generation with Zero Forgetting

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Himalaya Jain Tuan-Hung Vu Patrick Pé rez Matthieu Cord Valeo.ai Paris France Sorbonne University Paris France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains; ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, i.e., with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin.

关键词： Training Visualization Costs Semantics Memory management Generative adversarial networks pattern recognition

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Medium Scale Benchmark for Cricket Excited Actions Understanding

Medium Scale Benchmark for Cricket Excited Actions Understan...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Altaf Hussain Noman Khan Muhammad Munsif Min Je Kim Sung Wook Baik Sejong University Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Sports Action recognition (SAR) domain is of significant importance in research, with diverse applications, ranging from aiding coaches in strategic decision-making to empowering athletes and contributing to real-time commercial entertainment. Despite the existence of extensive large-scale and small-scale datasets, the direct application of these datasets to specific sports domains, such as cricket, poses challenges. Existing datasets predominantly center around daily life actions, lacking the necessary granularity for in-depth sports analyses. Current Cricket Action Analysis (CAA) datasets have limitations, including their small scale, modality constraints, and their narrow focus on specific aspects, such as cricket batting. Recognizing the need for a more comprehensive benchmark, this article introduces the Cricket Excited Actions (CEA) dataset. Developed in collaboration with professional cricket players, the CEA dataset encompasses challenging multi-person actions within realistic cricket scenarios. The selected activity classes, such as Clean Bowled, Six, Four, and Catches, adhere to official standards and represent pivotal moments in cricket matches. Through precise annotation and empirical studies, utilizing state-of-the-art action recognition model architectures, this study provides a valuable resource for further research and makes significant contributions by offering support essential to advancing CAA within the cricket sports community. The data and code are available at https://***/Altaf-hucn/Cricket-Excited-Actions-Benchmark.

关键词： Computational modeling Refining Collaboration computer architecture Benchmark testing Real-time systems pattern recognition

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Dformer: Learning Efficient Image Restoration with Perceptual Guidance

Dformer: Learning Efficient Image Restoration with Perceptua...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nodirkhuja Khudjaev Roman Tsoy S M A Sharif Azamat Myrzabekov Seongwan Kim Jaeho Lee Opt-AI Inc. LG Sciencepark Seoul South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image restoration tasks incorporate widespread real-world application. Apart from its significant practicability, generic deep image restoration methods still fail to handle complex tasks, like shadow removal, low-light enhancement, etc. This paper addresses the limitations of existing image restoration methods by introducing a novel deep architecture. The proposed model incorporates illumination mapping inspired by the Retinex theory within a double encoder-decoder network. Additionally, it utilizes a multi-head cross-attention mechanism to correlate input and reconstructed images to generate plausible and refined images. The proposed model employs a perceptual optimization strategy to tackle intricate restoration tasks effectively. It surpasses state-of-the-art methods in demanding tasks such as shadow removal, low-light image enhancement, and blind compress image enhancement, all while utilizing fewer trainable parameters. Our method is selected among the top solutions in the New Trends in Image Restoration and Enhancement’24 (NTIRE) challenge for shadow removal, securing a top position without resorting to score-boosting techniques such as ensembling.

关键词： Image coding Image edge detection Lighting Deep architecture Market research Image restoration pattern recognition

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Efficient Optimal Kernel Placement for Reliable Visual Tracking

Efficient Optimal Kernel Placement for Reliable Visual Track...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhimin Fan Ming Yang Ying Wu Gang Hua Ting Yu Department of Electrical & Computer Engineering Northwestern University USA

This paper describes a novel approach to optimal kernel placement in kernel-based tracking. If kernels are placed at arbitrary places, kernel-based methods are likely to be trapped in ill-conditioned locations, which prevents the reliable recovery of the motion parameters and jeopardizes the tracking performance. The theoretical analysis presented in this paper indicates that the optimal kernel placement can be evaluated based on a closed-form criterion, and achieved efficiently by a novel gradient-based algorithm. Based on that, new methods for temporal-stable multiple kernel placement and scale-invariant kernel placement are proposed. These new theoretical results and new algorithms greatly advance the study of kernel-based tracking in both theory and practice. Extensive real-time experimental results demonstrate the improved tracking reliability.

关键词： Kernel Target tracking Motion estimation Shape measurement Observability Reliability engineering Algorithm design and analysis Convolution Optimization methods Particle tracking

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Towards Learning Image Similarity from General Triplet Labels

Towards Learning Image Similarity from General Triplet Label...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Radu Dondera Greenfield Vision

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Metric learning for images has so far focused overwhelmingly on a class-based definition of similarity: two images are similar if they belong to the same class and dissimilar otherwise. Impressive results were achieved on datasets for fine grained categorization, but performance is nearing saturation. Recent work in neuroscience and psychology produced datasets with other types of similarity labels, e.g. the outlier in a group of three, but traditional metric learning methods are ill-suited to such data because of low density of labels. To overcome this difficulty, we propose a novel approach in the teacher-student learning paradigm. Multiple teacher models learn to embed images based only on relations with other images, and then a student model learns to embed images based on both content and dense relations provided by the teachers. We show significant improvement over existing triplet based metric learning methods, both in result quality and in training efficiency. Additionally, through experiments on class based datasets, we show the generality of approaching metric learning via knowledge transfer. Code is available at ***/greenfieldvision/tdml.

关键词： Measurement Training Learning systems Visualization Neuroscience Psychology Focusing

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Selecting Principal Components in a Two-Stage LDA Algorithm

Selecting Principal Components in a Two-Stage LDA Algorithm

引用

conference on computer vision and pattern recognition (CVPR)

作者： Manli Zhu A.M. Martinez Department of Electrical and Computer Engineering Ohio State Uinversity Atlanta OH USA

Linear Discriminant Analysis (LDA) is a well-known and important tool in pattern recognition with potential applications in many areas of research. The most famous and used formulation of LDA is that given by the Fisher-Rao criterion, where the problem reduces to a simple simultaneous diagonalization of two symmetric, positive-definite matrices, A and B; i.e. B^-1 AV = VA. Here, A defines the metric to be maximized, while B defines the metric to be minimized. However, when B has near-zero eigenvalues, the Fisher-Rao criterion gets dominated by these. While this works well when such small variances describe vectors where most of the discriminant information is, the results will be incorrect when these small variances are caused by noise. Knowing which of these near-zero values are to be used and which need to be eliminated is a challenging yet fundamental task in LDA. This paper presents a criterion for the selection of those vectors of B that are best for classification. The proposed solution is based on a simple factorization of B^-1 A that permits the re-ordering of the eigenvectors of B without the need to effect the end result. This allows us to readily eliminate the noisy vectors while keeping the most discriminant ones. A theoretical basis for these results is presented along with extensive experimental results to validate the claims.

关键词： Linear discriminant analysis Eigenvalues and eigenfunctions Symmetric matrices Covariance matrix Scattering Principal component analysis pattern recognition Application software Vectors Instruments

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UDAC: Under-Display Array Cameras

UDAC: Under-Display Array Cameras

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Chengyu Wang Jing Li Pavan C. Madhusudanarao Jinhan Hu Jitesh K. Singh WooJhon Choi Seok-Jun Lee Hamid R. Sheikh Samsung Research America Samsung Research India-Bangalore Samsung Electronics

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Under-display cameras (UDC) provide uninterrupted display experience on smartphones, but the captured images suffer from quality degradations such as diffraction blur, and reduced signal-to-noise ratio due to the presence of the OLED panel which obstructs the camera. In this work, we propose to use under-display array cameras (UDAC) consisting of two cameras placed under the OLED display panel to further reduce image artifacts and enhance image quality. We rotate the OLED panel by 45 degrees for one of the cameras to utilize the complimentary nature of the point spread function. We also propose an algorithm to combine images from the two cameras, which includes occlusion and optical flow analysis, image warping, color match and image blending. Experiments on real UDC devices show that the proposed method is superior in reducing the artifacts and improving the signal-to-noise ratio. The proposed method is simple but effective, and it can be easily extended to various configurations and incorporated with various algorithms.

关键词： Image quality Image color analysis Pipelines Organic light emitting diodes Cameras Image restoration pattern recognition

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Comparison of Methods for Hyperspherical Data Averaging and Parameter Estimation

Comparison of Methods for Hyperspherical Data Averaging and ...

引用

International conference on pattern recognition

作者： Kai Rothaus Xiaoyi Jiang M. Lambers Department of Computer Science University of Münster Germany

ISBN: (纸本)0769525210

Averaging is an important concept which has found numerous applications in general and in pattern recognition and computer vision in particular. In this paper we consider averaging directional vectors of arbitrary dimensions. Given a set of vectors, we intend to compute an average vector which optimally represents the input vectors according to some formal criterion. Several optimisation criteria are formulated. In particular, we present a class of robust estimators of up to 50% outlier tolerance. Furthermore, we propose a technique to estimate another distribution parameter. Experimental results on spherical data are presented to demonstrate the usefulness of the proposed methods

关键词： Parameter estimation Application software pattern recognition computer vision Shape Surface treatment Quaternions computer science Robustness Data engineering

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Contrastive Clothing and Pose Generation for Cloth-Changing Person Re-Identification

Contrastive Clothing and Pose Generation for Cloth-Changing ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Vuong D. Nguyen Pranav Mantini Shishir K. Shah Dept. of Computer Science Quantitative Imaging Lab University of Houston

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Cloth-Changing Person Re-Identification (CCRe-ID) aims at matching an individual across cameras after a long period of time, presenting variations in clothing compounded with changes in pose, viewpoint, etc. In this work, we propose CCPG: Contrastive Clothing and Pose Generation framework for CCRe-ID. Beyond appearance, CCPG captures cloth-invariant body shape information using a Relational Graph Attention Network. Training a robust CCRe-ID model requires a wide range of clothing variations and expensive cloth labeling, which is lacked in current CCRe-ID datasets. To address this, we propose a GAN-based model for clothing and pose transfer across identities to augment images of more clothing variations and of different persons wearing similar clothing. The augmented batch of images serve as inputs to our proposed Fine-grained Contrastive Losses, which not only supervise the Re-ID model to learn discriminative person embeddings under long-term scenarios but also ensure in-distribution data generation. Results on CCRe-ID datasets demonstrate the effectiveness of our CCPG framework. Code will be available here.

关键词： Training computer vision Shape conferences Computational modeling Clothing Data models

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H3Net: Irregular Posture Detection by Understanding Human Character and Core Structures

H3Net: Irregular Posture Detection by Understanding Human Ch...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Seungha Noh Kangmin Bae Yuseok Bae Byong-Dai Lee Kyonggi University Suwon South Korea ETRI Daejeon South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper proposes H 3 Net that considers detecting people in irregular postures by utilizing human structures and characters. To handle both features, we introduce two attention modules: 1) Human Structure Attention Module (HSAM), which is introduced to focus on the spatial aspects of a person, and 2) Human Character Attention Module (HCAM), which is designed to address the issue of repetitive appearance. HSAM effectively handles both foreground and background information about a human instance and utilizes keypoints to provide additional guidance to predict irregular postures. Meanwhile, HCAM employs ID information obtained from the tracking head, enriching the posture prediction with high-level semantic information. Furthermore, gathering images of people in irregular postures is a challenging task. Therefore, many conventional datasets consist of images with the same actors simulating varying postures in distinct images. To address this problem, we propose a Human ID Dependent Posture (HID 2 ) loss that handles repeated instances. The HID 2 loss generates a regularization term by considering duplicated instances to reduce bias. Our experiments demonstrate the effectiveness of H 3 Net compared to existing algorithms on irregular posture datasets. Furthermore, we show the qualitative results using color-coded masks and bounding boxes. We also provide ablation studies to highlight the significance of our proposed methods.

关键词： computer vision Head conferences Semantics pattern recognition

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