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

共 29687 条记录，以下是4821-4830 订阅

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GenesisTex: Adapting Image Denoising Diffusion to Texture Space

GenesisTex: Adapting Image Denoising Diffusion to Texture Sp...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Chenjian Gao Boyan Jiang Xinghui Li Yingpeng Zhang Qian Yu School of Software Beihang University R&D Efficiency and Capability Department Tencent IEG

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present GenesisTex, a novel method for synthesizing textures for 3D geometries from text descriptions. GenesisTex adapts the pretrained image diffusion model to texture space by texture space sampling. Specifically, we maintain a latent texture map for each viewpoint, which is updated with predicted noise on the rendering of the corresponding viewpoint. The sampled latent texture maps are then decoded into a final texture map. During the sampling process, we focus on both global and local consistency across multiple viewpoints: global consistency is achieved through the integration of style consistency mechanisms within the noise prediction network, and low-level consistency is achieved by dynamically aligning latent textures. Finally, we apply reference-based inpainting and img2img on denser views for texture refinement. Our approach overcomes the limitations of slow optimization in distillation-based methods and instability in inpainting-based methods. Experiments on meshes from various sources demonstrate that our method surpasses the baseline methods quantitatively and qualitatively.

关键词： Geometry computer vision Three-dimensional displays Noise Rendering (computer graphics) Diffusion models pattern recognition

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Supervised Contrastive Learning for Robust and Efficient Multi-modal Emotion and Sentiment Analysis 26

Supervised Contrastive Learning for Robust and Efficient Mul...

引用

26th International conference on pattern recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Gomaa, Ahmed Maier, Andreas Kosti, Ronak Friedrich Alexander Univ Erlangen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Expression of human emotion and sentiment are often multi-modal consisting use of spoken speech, vision, and text. Combining multiple modalities allows learning-based models to benefit with the complementary information present across modalities to produce more accurate predictions. One of the bigger challenges in multi-modal affective computing is performance consistency in non-ideal scenarios. Most benchmarks fail to generalize in non-ideal scenarios where one of the modalities is missing or highly corrupted due to occlusion, sensor errors, or change of orientation. Consequently, various modality fusion approaches were proposed. However, most of these fusion approaches assume that each modality is equally useful. To address the challenge of performance consistency, in this work we propose to use supervised contrastive learning (SCL). We demonstrate through various experiments and comparison with state-of-the-art (SOTA) methods that the model robustness against corrupted and missing modalities improves when trained with SCL. Next, we use the Perceiver architecture[1] in order to efficiently combine the representations of different modalities. Its iterative attention mechanism allows to create a reduced latent representation in an efficient manner. We observe that it can accommodate a wide range of modality combinations, allowing for robust information fusion. Our approach allows reduction of model complexity and efficient fusion of different modalities, while maintaining the performance consistency and model robustness. We conduct ablation experiments to study the effect of each contribution in different scenarios, and we show that the proposed methods outperform the state-of-art, while simultaneously being robust to corrupted modalities. Our method also outperforms its counterparts and SOTA while using less numerical complexity (inference times and compute operations).

关键词： Training Sentiment analysis Computational modeling computer architecture Predictive models Transformers Robustness

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H-ViT: A Hierarchical vision Transformer for Deformable Image Registration

H-ViT: A Hierarchical Vision Transformer for Deformable Imag...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Morteza Ghahremani Mohammad Khateri Bailiang Jian Benedikt Wiestler Ehsan Adeli Christian Wachinger Technical University of Munich Munich Center for Machine Learning University of Eastern Finland Stanford University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper introduces a novel top-down representation approach for deformable image registration, which estimates the deformation field by capturing various short-and long-range flow features at different scale levels. As a Hierarchical vision Transformer (H- ViT), we propose a dual self-attention and cross-attention mechanism that uses high-level features in the deformation field to represent low-level ones, enabling information streams in the deformation field across all voxel patch embeddings irrespective of their spatial proximity. Since high-level features contain abstract flow patterns, such patterns are expected to effectively contribute to the representation of the deformation field in lower scales. When the self-attention module utilizes within-scale short-range patterns for representation, the cross-attention modules dynamically look for the key tokens across different scales to further interact with the local query voxel patches. Our method shows superior accuracy and visual quality over the state-of-the-art registration methods in five publicly available datasets, highlighting a substantial enhancement in the performance of medical imaging registration. The project link is available at https://***/hvit.

关键词： computer vision Image registration Visualization Accuracy Deformation Transformers pattern recognition

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Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation

Differentiable SLAM-net: Learning Particle SLAM for Visual N...

引用

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

作者： Karkus, Peter Cai, Shaojun Hsu, David Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

Simultaneous localization and mapping (SLAM) remains challenging for a number of downstream applications, such as visual robot navigation, because of rapid turns, featureless walls, and poor camera quality. We introduce the Differentiable SLAM Network (SLAM-net) along with a navigation architecture to enable planar robot navigation in previously unseen indoor environments. SLAM-net encodes a particle filter based SLAM algorithm in a differentiable computation graph, and learns task-oriented neural network components by backpropagating through the SLAM algorithm. Because it can optimize all model components jointly for the end-objective, SLAM-net learns to be robust in challenging conditions. We run experiments in the Habitat platform with different real-world RGB and RGB-D datasets. SLAM-net significantly outperforms the widely adapted ORB-SLAM in noisy conditions. Our navigation architecture with SLAMnet improves the state-of-the-art for the Habitat Challenge 2020 PointNav task by a large margin (37% to 64% success).

关键词： Visualization Simultaneous localization and mapping Navigation Robot vision systems Neural networks computer architecture Particle filters

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Generalized Few-Shot Object Detection without Forgetting

Generalized Few-Shot Object Detection without Forgetting

引用

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

作者： Fan, Zhibo Ma, Yuchen Li, Zeming Sun, Jian Megvii Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Recently few-shot object detection is widely adopted to deal with data-limited situations. While most previous works merely focus on the performance on few-shot categories, we claim that detecting all classes is crucial as test samples may contain any instances in realistic applications, which requires the few-shot detector to learn new concepts without forgetting. Through analysis on transfer learning based methods, some neglected but beneficial properties are utilized to design a simple yet effective few-shot detector, Retentive R-CNN. It consists of Bias-Balanced RPN to debias the pretrained RPN and Re-detector to find few-shot class objects without forgetting previous knowledge. Extensive experiments on few-shot detection benchmarks show that Retentive R-CNN significantly outperforms state-of-the-art methods on overall performance among all settings as it can achieve competitive results on few-shot classes and does not degrade the base class performance at all. Our approach has demonstrated that the long desired never-forgetting learner is available in object detection.

关键词： Measurement computer vision Transfer learning Object detection Detectors Benchmark testing Reliability engineering

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Low Bandwidth Video-Chat Compression using Deep Generative Models

Low Bandwidth Video-Chat Compression using Deep Generative M...

引用

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

作者： Oquab, Maxime Stock, Pierre Gafni, Oran Haziza, Daniel Xu, Tao Zhang, Peizhao Celebi, Onur Hasson, Yana Labatut, Patrick Bose-Kolanu, Bobo Peyronel, Thibault Couprie, Camille Facebook Menlo Pk CA 94025 USA INRIA Le Chesnay France

ISBN: (纸本)9781665448994

To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's device using facial landmarks extracted at the sender's side and transmitted over the network. In this context, we discuss and evaluate the benefits and disadvantages of several deep adversarial approaches. In particular, we explore quality and bandwidth trade-offs for approaches based on static landmarks, dynamic landmarks or segmentation maps. We design a mobile-compatible architecture based on the first order animation model of Siarohin et al. In addition, we leverage SPADE blocks to refine results in important areas such as the eyes and lips. We compress the networks down to about 3 MB, allowing models to run in real time on iPhone 8 (CPU). This approach enables video calling at a few kbits per second, an order of magnitude lower than currently available alternatives.

关键词： Image segmentation Image coding Lips Bandwidth computer architecture Video compression Streaming media

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PatchMatch-Based Neighborhood Consensus for Semantic Correspondence

PatchMatch-Based Neighborhood Consensus for Semantic Corresp...

引用

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

作者： Lee, Jae Yong DeGol, Joseph Fragoso, Victor Sinha, Sudipta N. Univ Illinois Chicago IL 60680 USA Microsoft Washington DC USA

ISBN: (纸本)9781665445092

We address estimating dense correspondences between two images depicting different but semantically related scenes. End-to-end trainable deep neural networks incorporating neighborhood consensus cues are currently the best methods for this task. However, these architectures require exhaustive matching and 4D convolutions over matching costs for all pairs of feature map pixels. This makes them computationally expensive. We present a more efficient neighborhood consensus approach based on Patch-Match. For higher accuracy, we propose to use a learned local 4D scoring function for evaluating candidates during the PatchMatch iterations. We have devised an approach to jointly train the scoring function and the feature extraction modules by embedding them into a proxy model which is end-to-end differentiable. The modules are trained in a supervised setting using a cross-entropy loss to directly incorporate sparse keypoint supervision. Our evaluation on PF- PASCAL and SPAIR-71K shows that our method significantly outperforms the state-of-the-art on both datasets while also being faster and using less memory.

关键词： Deep learning computer vision Costs Computational modeling Semantics Memory management Feature extraction

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Learned Video Compression with Intra-Guided Enhancement and Implicit Motion Information

Learned Video Compression with Intra-Guided Enhancement and ...

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

作者： Zou, Nannan Zhang, Honglei Cricri, Francesco Tavakoli, Hamed R. Lainema, Jani Aksu, Emre Hannuksela, Miska Rahtu, Esa Nokia Technol Tampere Finland Tampere Univ Tampere Finland

ISBN: (纸本)9781665448994

Although learned approaches to video compression have been proposed with promising results, hand-engineered video codecs are still unbeaten. On the other hand, learned image compression has already surpassed traditional image codecs. In this paper, we propose a learned video compression system that mimics part of the pipeline of traditional codecs while leveraging learned image compression. It comprises two main modules: a learned intra-frame compression module, and a learned inter-frame compression module that is conditioned on intra-coded frames. These modules use separate learned probability models for entropy coding. The intra-frame codec uses a variant of nonlocal attention layers. Regarding the inter-frame codec, we propose an implicit motion information mechanism, and an enhancement of the inter-frame predictions by leveraging the high quality information of intra-coded frames. On the learned probability model side, we propose to use the reference frames as additional conditioning information. We used this system as our submitted entry for the 2021 Challenge on Learned Image Compression (CLIC). In our experiments, we show the effectiveness of our system and its components via a set of ablation studies.

关键词： computer vision Image coding conferences Computational modeling Pipelines MIMICs Video compression

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Stronger, Fewer, & Superior: Harnessing vision Foundation Models for Domain Generalized Semantic Segmentation

Stronger, Fewer, & Superior: Harnessing Vision Foundation Mo...

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conference on computer vision and pattern recognition (CVPR)

作者： Zhixiang Wei Lin Chen Yi Jin Xiaoxiao Ma Tianle Liu Pengyang Ling Ben Wang Huaian Chen Jinjin Zheng University of Science and Technology of China Shanghai AI Laboratory

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this paper, we first assess and harness various vision Foundation Models (VFMs) in the context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability, we introduce a robust fine-tuning approach, namely “Rein”, to parameter-efficiently harness VFMs for DGSS. Built upon a set of trainable tokens, each linked to distinct instances, Rein precisely refines and forwards the feature maps from each layer to the next layer within the backbone. This process produces diverse refinements for different categories within a single image. With fewer trainable parameters, Rein efficiently fine-tunes VFMs for DGSS tasks, surprisingly surpassing full parameter fine-tuning. Extensive experiments across various settings demonstrate that Rein significantly outperforms state-of-the-art methods. Remarkably, with just an extra 1% of trainable parameters within the frozen backbone, Rein achieves a mIoU of 78.4% on the Cityscapes, without accessing any real urban-scene datasets. Code is available at https://***/w1oves/***.

关键词： Training computer vision Codes Semantic segmentation pattern recognition

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Evaluating the Impact of Wide-Angle Lens Distortion on Learning-based Depth Estimation

Evaluating the Impact of Wide-Angle Lens Distortion on Learn...

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

作者： Buquet, Julie Zhang, Jinsong Roulet, Patrice Thibault, Simon Lalonde, Jean-Francois Univ Laval Quebec City PQ Canada Immervision Montreal PQ Canada

ISBN: (纸本)9781665448994

Most computer vision research focuses on narrow angle lenses and is not adapted to super-wide-angle (aka spherical) lenses. This is mainly because current neural networks are not designed or trained to interpret the significant barrel distortion that is introduced in the captured image by such wide angle *** these lenses capture a half-sphere or a section of sphere on the object space, barrel distortion appears when the image is projected on a 2D flat image sensor. By controlling this distortion at the lens design stage, camera designers can create some areas with augmented resolution [26]. In this work, we present an analysis of the impact of such augmented resolution on computer vision algorithm accuracy, using the problem of single image depth estimation as a case study. To this end, 360 degrees panorama datasets are warped to simulate different wideangle lens datasets, which are then used to train identical neural networks. Each lens presents specific areas of the image with augmented resolution using spatially-varying non-linear distortion. We show that this property leads to better local accuracy in depth estimation. We also demonstrate that considering lens manufacturing improves performance when tested on realistic lenses, especially in the area of augmented resolution. We further show that this property helps to locally come closer to performances obtained on perspective images without cropping the field of view.

关键词： Training computer vision Nonlinear distortion Neural networks Estimation Tools Cameras

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