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共 3313 条记录，以下是921-930 订阅

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3D Scene Painting via Semantic Image Synthesis

3D Scene Painting via Semantic Image Synthesis

引用

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

作者： Jeong, Jaebong Jo, Janghun Cho, Sunghyun Park, Jaesik POSTECH GSAI & CSE Pohang South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a novel approach to 3D scene painting using a configurable 3D scene layout. Our approach takes a 3D scene with semantic class labels as input and trains a 3D scene painting network that synthesizes color values for the input 3D scene. We exploit an off-the-shelf 2D semantic image synthesis method to teach the 3D painting network without explicit color supervision. Experiments show that our approach produces images with geometrically correct structures and supports scene manipulation, such as the change of viewpoint, object poses, and painting style. Our approach provides rich controllability to synthesized images in the aspect of 3D geometry.

关键词： Training Solid modeling Three-dimensional displays Image color analysis Image synthesis Machine vision Semantics

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On-Orbit Inspection of an Unknown, Tumbling Target using NASA's Astrobee Robotic Free-Flyers

On-Orbit Inspection of an Unknown, Tumbling Target using NAS...

引用

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

作者： Oestreich, Charles Espinoza, Antonio Teran Todd, Jessica Albee, Keenan Linares, Richard MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665448994

Autonomous spacecraft critically depend on on-orbit inspection (i.e., relative navigation and inertial properties estimation) to intercept tumbling debris objects or defunct satellites. This work presents a practical method for on-orbit inspection and demonstrates its performance in simulation using NASA's Astrobee robotic free-flyers. The problem is formulated as a simultaneous localization and mapping task, utilizing IMU data from an observing "chaser" spacecraft and point clouds of the observed "target" spacecraft obtained via a 3D time-of-flight camera. The relative navigation between the chaser and target is solved via a factor graph-based approach. The target's principal axes of inertia are then estimated via a conic fit optimization procedure using a polhode analysis. Simulation results indicate the accuracy of the proposed method in preparation for hardware experiments on the International Space Station.

关键词： Space vehicles Three-dimensional displays Heuristic algorithms Robot vision systems Inspection Extraterrestrial measurements International Space Station

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Which Model to Transfer? Finding the Needle in the Growing Haystack

Which Model to Transfer? Finding the Needle in the Growing H...

引用

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

作者： Renggli, Cedric Pinto, Andre Susano Rimanic, Luka Puigcerver, Joan Riquelme, Carlos Zhang, Ce Lucic, Mario Swiss Fed Inst Technol Zurich Switzerland Google Res Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular for computer vision tasks where it provides a remarkably solid baseline. The emergence of rich model repositories, such as TensorFlow Hub, enables the practitioners and researchers to unleash the potential of these models across a wide range of downstream tasks. As these repositories keep growing exponentially, efficiently selecting a good model for the task at hand becomes paramount. We provide a formalization of this problem through a familiar notion of regret and introduce the predominant strategies, namely task-agnostic (e.g. ranking models by their ImageNet performance) and task-aware search strategies (such as linear or kNN evaluation). We conduct a largescale empirical study and show that both task-agnostic and task-aware methods can yield high regret. We then propose a simple and computationally efficient hybrid search strategy which outperforms the existing approaches. We highlight the practical benefits of the proposed solution on a set of 19 diverse vision tasks.

关键词： Training computer vision Solid modeling Computational modeling Transfer learning Search problems Solids

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SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep Convolutional Block

SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep...

引用

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

作者： Jo, Younghyun Yang, Sejong Kim, Seon Joo Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665448994

Multiple high-resolution (HR) images can be generated from a single low-resolution (LR) image, as super-resolution (SR) is an underdetermined problem. Recently, the conditional normalizing flow-based model, SRFlow, shows remarkable performance by learning an exact mapping from HR image manifold to a latent space. The flow-based SR model allows sampling multiple output images from a learned SR space with a given LR image. In this work, we propose SRFlow-DA which has a more suitable architecture for the SR task based on the original SRFlow model. Specifically, our approach enlarges the receptive field by stacking more convolutional layers in the affine couplings, and so our model can get more expressive power. At the same time, we reduce the total number of model parameters for efficiency. Compared to SRFlow, our SRFlow-DA achieves better or comparable PSNR and LPIPS for x4 and x8 SR tasks, while having a reduced number of parameters. In addition, our method generates visually clear results without excessive sharpness artifacts.

关键词： Couplings Training Manifolds computer vision conferences Superresolution Stacking

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Contrastive Learning for Depth Prediction

Contrastive Learning for Depth Prediction

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Rizhao Fan Matteo Poggi Stefano Mattoccia Department of Computer Science and Engineering University of Bologna Italy

Depth prediction is at the core of several computer vision applications, such as autonomous driving and robotics. It is often formulated as a regression task in which depth values are estimated through network layers. Unfortunately, the distribution of values on depth maps is seldom explored. Therefore, this paper proposes a novel framework combining contrastive learning and depth prediction, allowing us to pay more attention to depth distribution and consequently enabling improvements to the overall estimation process. Purposely, we propose a window-based contrastive learning module, which partitions the feature maps into non-overlapping windows and constructs contrastive loss within each one. Forming and sorting positive and negative pairs, then enlarging the gap between the two in the representation space, constraints depth distribution to fit the feature of the depth map. Experiments on KITTI and NYU datasets demonstrate the effectiveness of our framework.

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DANICE: Domain adaptation without forgetting in neural image compression

DANICE: Domain adaptation without forgetting in neural image...

引用

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

作者： Katakol, Sudeep Herranz, Luis Yang, Fei Mrak, Marta Univ Michigan Ann Arbor MI 48109 USA UAB Comp Vis Ctr Barcelona Spain BBC R&D London England

ISBN: (纸本)9781665448994

Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC.

关键词： Video coding computer vision Image coding Codecs conferences Transfer learning Interference

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Curriculum Learning for Data-Efficient vision-Language Alignment

Curriculum Learning for Data-Efficient Vision-Language Align...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tejas Srinivasan Xiang Ren Jesse Thomason University of Southern California

Aligning image and text encoders from scratch using contrastive learning requires large amounts of paired image-text data. We alleviate this need by aligning individually pre-trained language and vision representation models using a much smaller amount of paired data with a curriculum learning algorithm to learn fine-grained vision-language alignments. TOnICS (Training with Ontology-Informed Contrastive Sampling) initially samples minibatches whose image-text pairs contain a wide variety of objects to learn object-level vision-language alignment, and progressively samples minibatches where all image-text pairs contain the same object to learn finer-grained contextual alignment. Aligning pre-trained BERT and VinVL-OD models to each other using TOnICS outperforms CLIP on downstream zero-shot image retrieval using < 1% as much training data.

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Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay

Dual-Teacher Class-Incremental Learning With Data-Free Gener...

引用

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

作者： Choi, Yoojin El-Khamy, Mostafa Lee, Jungwon Samsung Semicond Inc SoC R&D San Diego CA 92121 USA Samsung Elect Syst LSI Suwon South Korea

ISBN: (纸本)9781665448994

This paper proposes two novel knowledge transfer techniques for class-incremental learning (CIL). First, we propose data-free generative replay (DF-GR) to mitigate catastrophic forgetting in CIL by using synthetic samples from a generative model. In the conventional generative replay, the generative model is pre-trained for old data and shared in extra memory for later incremental learning. In our proposed DF-GR, we train a generative model from scratch without using any training data, based on the pre-trained classification model from the past, so we curtail the cost of sharing pre-trained generative models. Second, we introduce dual-teacher information distillation (DT-ID) for knowledge distillation from two teachers to one student. In CIL, we use DT-ID to learn new classes incrementally based on the pre-trained model for old classes and another model (pre-)trained on the new data for new classes. We implemented the proposed schemes on top of one of the state-of-the-art CIL methods and showed the performance improvement on CIFAR-100 and ImageNet datasets.

关键词： Data privacy computer vision conferences Training data Data models pattern recognition Knowledge transfer

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Expression Transfer Using Flow-based Generative Models

Expression Transfer Using Flow-based Generative Models

引用

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

作者： Valenzuela, Andrea Segura, Carlos Diego, Ferran Gomez, Vicenc Univ Pompeu Fabra Barcelona Catalonia Spain Tel Res Barcelona Catalonia Spain

ISBN: (纸本)9781665448994

Among the different deepfake generation techniques, flow-based methods appear as natural candidates. Due to the property of invertibility, flow-based methods eliminate the necessity of person-specific training and are able to reconstruct any input image almost perfectly to human perception. We present a method for deepfake generation based on facial expression transfer using flow-based generative models. Our approach relies on simple latent vector operations akin to the ones used for attribute manipulation, but for transferring expressions between identity source-target pairs. We show the feasibility of this approach using a pre-trained Glow model and small sets of source and target images, not necessarily considered during prior training. We also provide an evaluation pipeline of the generated images in terms of similarities between identities and Action Units encoding the expression to be transferred. Our results show that an efficient expression transfer is feasible by using the proposed approach setting up a first precedent in deepfake content creation, and its evaluation, independently of the training identities.

关键词： Training computer vision Image coding conferences Computational modeling Pipelines pattern recognition

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Implications of Solution patterns on Adversarial Robustness

Implications of Solution Patterns on Adversarial Robustness

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hengyue Liang Buyun Liang Ju Sun Ying Cui Tim Mitchell Department of Electrical & Computer Engineering University of Minnesota Department of Computer Science & Engineering University of Minnesota Department of Industrial & Systems Engineering University of Minnesota Department of Computer Science Queens College City University of New York

Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations involves solving non-trivial constrained optimization problems. Recent work has shown that these RE problems can be reliably solved by a general-purpose constrained-optimization solver, PyGRANSO with Constraint-Folding (PWCF). In this paper, we take advantage of PWCF and other existing numerical RE algorithms to explore distinct solution patterns in solving RE problems with various combinations of losses, perturbation models, and optimization algorithms. We then provide extensive discussions on the implications of these patterns on current robustness evaluation and adversarial training. A comprehensive version of this work can be found in [19].

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