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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22886 条记录，以下是4711-4720 订阅

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NeX: Real-time View Synthesis with Neural Basis Expansion

NeX: Real-time View Synthesis with Neural Basis Expansion

引用

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

作者： Wizadwongsa, Suttisak Phongthawee, Pakkapon Yenphraphai, Jiraphon Suwajanakorn, Supasorn VISTEC Rayong Thailand

ISBN: (纸本)9781665445092

We present NeX, a new approach to novel view synthesis based on enhancements of multiplane image (MPI) that can reproduce next-level view-dependent effects-in real time. Unlike traditional MP! that uses a set of simple RGBcx planes, our technique models view-dependent effects by instead parameterizing each pixel as a linear combination of basis functions learned from a neural network. Moreover, we propose a hybrid implicit-explicit modeling strategy that improves upon fine detail and produces state-of-the-art results. Our method is evaluated on benchmark forward-facing datasets as well as our newly-introduced dataset designed to test the limit of view-dependent modeling with significantly more challenging effects such as the rainbow reflections on a CD. Our method achieves the best overall scores across all major metrics on these datasets with more than 1000x faster rendering time than the state of the art. For real-time demos, visit https://***/

关键词： Reflectivity Measurement Neural networks Rendering (computer graphics) Real-time systems Reflection Light fields

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Heterogeneous Grid Convolution for Adaptive, Efficient, and Controllable Computation

Heterogeneous Grid Convolution for Adaptive, Efficient, and ...

引用

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

作者： Hamaguchi, Ryuhei Furukawa, Yasutaka Onishi, Masaki Sakurada, Ken Natl Inst Adv Ind Sci & Technol Tokyo Japan Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

This paper proposes a novel heterogeneous grid convolution that builds a graph-based image representation by exploiting heterogeneity in the image content, enabling adaptive, efficient, and controllable computations in a convolutional architecture. More concretely, the approach builds a data-adaptive graph structure from a convolutional layer by a differentiable clustering method, pools features to the graph, performs a novel direction-aware graph convolution, and unpool features back to the convolutional layer. By using the developed module, the paper proposes heterogeneous grid convolutional networks, highly efficient yet strong extension of existing architectures. We have evaluated the proposed approach on four image understanding tasks, semantic segmentation, object localization, road extraction, and salient object detection. The proposed method is effective on three of the four tasks. Especially, the method outperforms a strong baseline with more than 90% reduction in floating-point operations for semantic segmentation, and achieves the state-of-the-art result for road extraction. We will share our code, model, and data.

关键词： Convolutional codes Location awareness Image segmentation Convolution Roads Semantics computer architecture

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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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Interpolation-based Semi-supervised Learning for Object Detection

Interpolation-based Semi-supervised Learning for Object Dete...

引用

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

作者： Jeong, Jisoo Verma, Vikas Hyun, Minsung Kannala, Juho Kwak, Nojun Seou1 Natl Univ Seoul South Korea Aalto Univ Espoo Finland Mila Quebec Artificial Intelligence Inst Montreal PQ Canada SK Hynix Icheon Si South Korea

ISBN: (纸本)9781665445092

Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much. In this paper, we propose an Interpolation-based Semi-supervised learning method for object Detection (ISD), which considers and solves the problems caused by applying conventional Interpolation Regularization (IR) directly to object detection. We divide the output of the model into two types according to the objectness scores of both original patches that are mixed in IR. Then, we apply a separate loss suitable for each type in an unsupervised manner. The proposed losses dramatically improve the performance of semi-supervised learning as well as supervised learning. In the supervised learning setting, our method improves the baseline methods by a significant margin. In the semi-supervised learning setting, our algorithm improves the performance on a benchmark dataset (PASCAL VOC and MSCOCO) in a benchmark architecture (SSD).

关键词： Interpolation Supervised learning Object detection Detectors computer architecture Semisupervised learning Benchmark testing

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Pixel Codec Avatars

Pixel Codec Avatars

引用

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

作者： Ma, Shugao Simon, Tomas Saragih, Jason Wang, Dawei Li, Yuecheng De La Torre, Fernando Sheikh, Yaser Facebook Real Labs Res Menlo Pk CA 94025 USA

ISBN: (纸本)9781665445092

Telecommunication with photorealistic avatars in virtual or augmented reality is a promising path for achieving authentic face-to-face communication in 3D over remote physical distances. In this work, we present the Pixel Codec Avatars (PiCA): a deep generative model of 3D human faces that achieves state of the art reconstruction performance while being computationally efficient and adaptive to the rendering conditions during execution. Our model combines two core ideas: (1) a fully convolutional architecture for decoding spatially varying features, and (2) a rendering-adaptive per-pixel decoder. Both techniques are integrated via a dense surface representation that is learned in a weakly-supervised manner from low-topology mesh tracking over training images. We demonstrate that PiCA improves reconstruction over existing techniques across testing expressions and views on persons of different gender and skin tone. Importantly, we show that the PiCA model is much smaller than the state-of-art baseline model, and makes multi-person telecommunicaiton possible: on a single Oculus Quest 2 mobile VR headset, 5 avatars are rendered in realtime in the same scene.

关键词： Training Adaptation models Three-dimensional displays Codecs Computational modeling Avatars Rendering (computer graphics)

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Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

Histopathological Image Classification with Cell Morphology ...

引用

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

作者： Andrey Ignatov Josephine Yates Valentina Boeva Computer Vision Lab ETH Zurich Computational Cancer Genomics Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Histopathological images are widely used for the analysis of diseased (tumor) tissues and patient treatment selection. While the majority of microscopy image processing was previously done manually by pathologists, recent advances in computer vision allow for accurate recognition of lesion regions with deep learning-based solutions. Such models, however, usually require extensive annotated datasets for training, which is often not the case in the considered task, where the number of available patient data samples is very limited. To deal with this problem, we propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types. The model consists of two modules: the first one performs cell nuclei segmentation and annotates each cell type, and is trained on a combination of 8 publicly available datasets to ensure its high generalizability and robustness. The second module combines the obtained segmentation map with the original microscopy image and is trained for the downstream task. We pre-trained this module on the Pan-Cancer TCGA dataset consisting of over 270K tissue patches extracted from 8736 diagnostic slides from 7175 patients. The proposed solution achieved a new state-of-the-art performance on the dataset under consideration, detecting 32 cancer types with over 82% accuracy and outperforming all previously proposed solutions by more than 4%. We demonstrate that the resulting pre-trained model can be easily fine-tuned on smaller microscopy datasets, yielding superior results compared to the current top solutions and models initialized with ImageNet weights. The codes and pre-trained models presented in this paper are available at: https://***/aiff22/DeepCMorph

关键词： Image segmentation computer vision Analytical models Accuracy Microscopy Microprocessors Computational modeling

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FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains

FaceInpainter: High Fidelity Face Adaptation to Heterogeneou...

引用

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

作者： Li, Jia Li, Zhaoyang Cao, Jie Song, Xingguang He, Ran Chinese Acad Sci Univ Chinese Acad Sci Sch Artificial IntelligenceNatl Lab Pattern Reco CASIACtr Excellence Brain Sci & Intelligence Tec Beijing Peoples R China

ISBN: (纸本)9781665445092

In this work, we propose a novel two-stage framework named Facelnpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains. Concretely, by explicitly disentangling foreground and background of the target face, the first stage focuses on adaptive face fitting to the fixed background via a Styled Face Inpainting Network (SFI-Net), with 3D priors and texture code of the target, as well as identity factor of the source face. It is challenging to deal with the inconsistency between the new identity of the source and the original background of the target, concerning the face shape and appearance on the fused boundary. The second stage consists of a Joint Refinement Network (JR-Net) to refine the swapped face. It leverages AdaIN considering identity and multi-scale texture codes, for feature transformation of the decoded face from SFI-Net with facial occlusions. We adopt the contextual loss to implicitly preserve the attributes, encouraging face deformation and fewer texture distortions. Experimental results demonstrate that our approach handles high-quality identity adaptation to heterogeneous domains, exhibiting the competitive performance compared with state-of-the-art methods concerning both attribute and identity fidelity.

关键词： computer vision Three-dimensional displays Codes Shape Face recognition Fitting Rendering (computer graphics)

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Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion

Sparse Auxiliary Networks for Unified Monocular Depth Predic...

引用

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

作者： Guizilini, Vitor Ambrus, Rares Burgard, Wolfram Gaidon, Adrien Toyota Res Inst TRI Los Altos CA 94022 USA

ISBN: (纸本)9781665445092

Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars. In this paper, we study the problem of predicting dense depth from a single RGB image (monodepth) with optional sparse measurements from low-cost active depth sensors. We introduce Sparse Auxiliary Networks (SANs), a new module enabling monodepth networks to perform both the tasks of depth prediction and completion, depending on whether only RGB images or also sparse point clouds are available at inference time. First, we decouple the image and depth map encoding stages using sparse convolutions to process only the valid depth map pixels. Second, we inject this information, when available, into the skip connections of the depth prediction network, augmenting its features. Through extensive experimental analysis on one indoor (NYUv2) and two outdoor (KITTI and DDAD) benchmarks, we demonstrate that our proposed SAN architecture is able to simultaneously learn both tasks, while achieving a new state of the art in depth prediction by a significant margin.

关键词： Training Image sensors Image coding computer architecture Benchmark testing Robot sensing systems Sensors

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Learning to Identify Correct 2D-2D Line Correspondences on Sphere

Learning to Identify Correct 2D-2D Line Correspondences on S...

引用

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

作者： Li, Haoang Chen, Kai Zhao, Ji Wang, Jiangliu Kim, Pyojin Liu, Zhe Liu, Yun-Hui Chinese Univ Hong Kong Hong Kong Peoples R China TuSimple Beijing Peoples R China Sookmyung Womens Univ Seoul South Korea Univ Cambridge Cambridge England

ISBN: (纸本)9781665445092

Given a set of putative 2D-2D line correspondences, we aim to identify correct matches. Existing methods exploit the geometric constraints. They are only applicable to structured scenes with orthogonality, parallelism and coplanarity. In contrast, we propose the first approach suitable for both structured and unstructured scenes. Instead of geometric constraint, we leverage the spatial regularity on sphere. Specifically, we propose to map line correspondences into vectors tangent to sphere. We use these vectors to encode both angular and positional variations of image lines, which is more reliable and concise than directly using inclinations, midpoints or endpoints of image lines. Neighboring vectors mapped from correct matches exhibit a spatial regularity called local trend consistency, regardless of the type of scenes. To encode this regularity, we design a neural network and also propose a novel loss function that enforces the smoothness constraint of vector field. In addition, we establish a large real-world dataset for image line matching. Experiments showed that our approach outperforms state-of-the-art ones in terms of accuracy, efficiency and robustness, and also leads to high generalization.

关键词： computer vision computer network reliability Neural networks Parallel processing Market research Robustness pattern recognition

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Label Propagation for Zero-shot Classification with vision-Language Models

Label Propagation for Zero-shot Classification with Vision-L...

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conference on computer vision and pattern recognition (CVPR)

作者： Vladan Stojnić Yannis Kalantidis Giorgos Tolias VRG FEE Czech Technical University in Prague NAVER LABS Europe

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

vision-Language Models (VLMs) have demonstrated im-pressive performance on zero-shot classification, i.e. classi-fication when provided merely with a list of class names. In this paper, we tackle the case of zero-shot classification in the presence of unlabeled data. We leverage the graph structure of the unlabeled data and introduce ZLaP, a method based on label propagation (LP) that utilizes geodesic distances for classification. We tailor LP to graphs containing both text and image features and further pro-pose an efficient method for performing inductive infer-ence based on a dual solution and a sparsification step. We perform extensive experiments to evaluate the effectiveness of our method on 14 common datasets and show that ZLaP outperforms the latest related works. Code: https://***/vladan-stojnic/ZLaP

关键词： computer vision Codes Computational modeling Closed box Encoding Data models pattern recognition

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