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RoMa: Robust Dense Feature Matching

RoMa: Robust Dense Feature Matching

引用

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

作者： Edstedt, Johan Sun, Qiyu Bokman, Georg Wadenback, Marten Felsberg, Michael Linkoping Univ Linkoping Sweden East China Univ Sci & Technol Shanghai Peoples R China Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9798350353006

Feature matching is an important computer vision task that involves estimating correspondences between two images of a 3D scene, and dense methods estimate all such correspondences. The aim is to learn a robust model, i.e., a model able to match under challenging real-world changes. In this work, we propose such a model, leveraging frozen pretrained features from the foundation model DINOv2. Although these features are significantly more robust than local features trained from scratch, they are inherently coarse. We therefore combine them with specialized ConvNet fine features, creating a precisely localizable feature pyramid. To further improve robustness, we propose a tailored transformer match decoder that predicts anchor probabilities, which enables it to express multimodality. Finally, we propose an improved loss formulation through regression-by-classification with subsequent robust regression. We conduct a comprehensive set of experiments that show that our method, RoMa, achieves significant gains, setting a new state-of-the-art. In particular, we achieve a 36% improvement on the extremely challenging WxBS benchmark. Code is provided at ***/Parskatt/RoMa.

关键词： 3D vision dense feature matching dense matching feature matching geometry estimation image matching two-view geometry

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SaCo Loss: Sample-wise Affinity Consistency for vision-Language Pre-training

SaCo Loss: Sample-wise Affinity Consistency for Vision-Langu...

引用

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

作者： Wu, Sitong Tan, Haoru Tian, Zhuotao Chen, Yukang Qi, Xiaojuan Jia, Jiaya CUHK Hong Kong Peoples R China HKU Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9798350353006

vision-language pre-training (VLP) aims to learn joint representations of vision and language modalities. The contrastive paradigm is currently dominant in this field. However, we observe a notable misalignment phenomenon, that is, the affinity between samples has an obvious disparity across different modalities, namely "Affinity Inconsistency Problem". Our intuition is that, for a well-aligned model, two images that look similar to each other should have the same level of similarity as their corresponding texts that describe them. In this paper, we first investigate the reason of this inconsistency problem. We discover that the lack of consideration for sample-wise affinity consistency across modalities in existing training objectives is the central cause. To address this problem, we propose a novel loss function, named Sample-wise affinity Consistency (SaCo) loss, which is designed to enhance such consistency by minimizing the distance between image embedding similarity and text embedding similarity for any two samples. Our SaCo loss can be easily incorporated into existing vision-language models as an additional loss due to its complementarity for most training objectives. In addition, considering that pre-training from scratch is computationally expensive, we also provide a more efficient way to continuously pre-train on a converged model by integrating our loss. Experimentally, the model trained with our SaCo loss significantly outperforms the baseline on a variety of vision and language tasks.

关键词： Affinity Consistency vision-Language Pre-training

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Can domain adaptation make object recognition work for everyone?

Can domain adaptation make object recognition work for every...

引用

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

作者： Prabhu, Viraj Selvaraju, Ramprasaath R. Hoffman, Judy Naik, Nikhil Georgia Tech Atlanta GA 30332 USA Artera AI Berkeley CA USA Salesforce Res Washington DC USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Despite the rapid progress in deep visual recognition, modern computer vision datasets significantly overrepresent the developed world and models trained on such datasets underperform on images from unseen geographies. We investigate the effectiveness of unsupervised domain adaptation (UDA) of such models across geographies at closing this performance gap. To do so, we first curate two shifts from existing datasets to study the Geographical DA problem, and discover new challenges beyond data distribution shift: context shift, wherein object surroundings may change significantly across geographies, and subpopulation shift, wherein the intra-category distributions may shift. We demonstrate the inefficacy of standard DA methods at Geographical DA, highlighting the need for specialized geographical adaptation solutions to address the challenge of making object recognition work for everyone.

关键词： Geography computer vision Adaptation models Visualization Image recognition Computational modeling conferences

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Lift3D: Zero-Shot Lifting of Any 2D vision Model to 3D

Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D

引用

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

作者： Varma, Mukund T. Wang, Peihao Fan, Zhiwen Wang, Zhangyang Su, Hao Ramamoorthi, Ravi Univ Calif San Diego La Jolla CA 92093 USA Univ Texas Austin Austin TX USA

ISBN: (纸本)9798350353006

In recent years, there has been an explosion of 2D vision models for numerous tasks such as semantic segmentation, style transfer or scene editing, enabled by large-scale 2D image datasets. At the same time, there has been renewed interest in 3D scene representations such as neural radiance fields from multi-view images. However, the availability of 3D or multiview data is still substantially limited compared to 2D image datasets, making extending 2D vision models to 3D data highly desirable but also very challenging. Indeed, extending a single 2D vision operator like scene editing to 3D typically requires a highly creative method specialized to that task and often requires per-scene optimization. In this paper, we ask the question of whether any 2D vision model can be lifted to make 3D consistent predictions. We answer this question in the affirmative;our new Lift3D method trains to predict unseen views on feature spaces generated by a few visual models (i.e. DINO and CLIP), but then generalizes to novel vision operators and tasks, such as style transfer, super-resolution, open vocabulary segmentation and image colorization;for some of these tasks, there is no comparable previous 3D method. In many cases, we even outperform

关键词： Semantic Segmentation

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Compositional Mixture Representations for vision and Text

Compositional Mixture Representations for Vision and Text

引用

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

作者： Alaniz, Stephan Federici, Marco Akata, Zeynep Univ Tubingen Tubingen Germany Max Planck Inst Informat Saarbrucken Germany Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture representation imposing the compositionality of the text onto the visual domain without having explicit location supervision. By combining the spatial transformer with a representation learning approach we learn to split images into separately encoded patches to associate visual and textual representations in an interpretable manner. On variations of MNIST and CIFAR10, our model is able to perform weakly supervised object detection and demonstrates its ability to extrapolate to unseen combination of objects.

关键词： Representation learning Visualization Computational modeling Semantics Image retrieval Employment Object detection

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Towards Detailed Characteristic-Preserving Virtual Try-On

Towards Detailed Characteristic-Preserving Virtual Try-On

引用

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

作者： Lee, Sangho Lee, Seoyoung Lee, Joonseok Seoul Natl Univ Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

While virtual try-on has rapidly progressed recently, existing virtual try-on methods still struggle to faithfully represent various details of the clothes when worn. In this paper, we propose a simple yet effective method to better preserve details of the clothing and person by introducing an additional fitting step after geometric warping. This minimal modification enables disentangling representations of the clothing from the wearer, hence we are able to preserve the wearer-agnostic structure and details of the clothing, to fit a garment naturally to a variety of poses and body shapes. Moreover, we propose a novel evaluation framework applicable to any metric, to better reflect the semantics of clothes fitting. From extensive experiments, we empirically verify that the proposed method not only learns to disentangle clothing from the wearer, but also preserves details of the clothing on the try-on results.

关键词： Measurement computer vision Shape conferences Clothing Fitting Semantics

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Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity

Proposal-free Lidar Panoptic Segmentation with Pillar-level ...

引用

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

作者： Chen, Qi Vora, Sourabh Johns Hopkins Univ Baltimore MD 21218 USA Motional Boston MA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation. We jointly optimize both semantic segmentation and class-agnostic instance classification in a single network using a pilla-rbased bird's-eye view representation. The instance classification head learns pairwise affinity between pillars to determine whether the pillars belong to the same instance or not. We further propose a local clustering algorithm to propagate instance ids by merging semantic segmentation and affinity predictions. Our experiments on nuScenes dataset show that our approach outperforms previous proposal-free methods and is comparable to proposal-based methods which requires extra annotation from object detection.

关键词： computer vision Laser radar conferences Semantics Merging Clustering algorithms Object detection

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Anomaly Detection in Autonomous Driving: A Survey

Anomaly Detection in Autonomous Driving: A Survey

引用

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

作者： Bogdoll, Daniel Nitsche, Maximilian Zoellner, J. Marius FZI Res Ctr Informat Technol Karlsruhe Germany KIT Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781665487399

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.

关键词： computer vision Laser radar Roads conferences Radar detection Benchmark testing Cameras

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OVER-NAV: Elevating Iterative vision-and-Language Navigation with Open-Vocabulary Detection and StructurEd Representation

OVER-NAV: Elevating Iterative Vision-and-Language Navigation...

引用

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

作者： Zhao, Ganlong Li, Guanbin Chen, Weikai Yu, Yizhou Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Guangdong Peoples R China GuangDong Prov Key Lab Informat Secur Technol Guangzhou Guangdong Peoples R China Tencent Games Digital Content Technol Ctr Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350353006

Recent advances in Iterative vision-and-Language Navigation (IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes. Although the long-term memory aligns better with the persistent nature of the VLN task, it poses more challenges on how to utilize the highly unstructured navigation memory with extremely sparse supervision. Towards this end, we propose OVER-NAV, which aims to go over and beyond the current arts of IVLN techniques. In particular, we propose to incorporate LLMs and open-vocabulary detectors to distill key information and establish correspondence between multi-modal signals. Such a mechanism introduces reliable cross-modal supervision and enables on-the-fly generalization to unseen scenes without the need of extra annotation and re-training. To fully exploit the interpreted navigation data, we further introduce a structured representation, coded Omnigraph, to effectively integrate multi-modal information along the tour. Accompanied with a novel omnigraph fusion mechanism, OVER-NAV is able to extract the most relevant knowledge from omnigraph for a more accurate navigating action. In addition, OVER-NAV seamlessly supports both discrete and continuous environments under a unified framework. We demonstrate the superiority of OVER-NAV in extensive experiments.

关键词： Multi-Modal Learning Open-vocabulary vision-and-Language Navigation

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OutfitGAN: Learning Compatible Items for Generative Fashion Outfits

OutfitGAN: Learning Compatible Items for Generative Fashion ...

引用

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

作者： Moosaei, Maryam Lin, Yusan Akhazhanov, Ablaikhan Chen, Huiyuan Wang, Fei Yang, Hao Visa Res San Francisco CA 94158 USA Univ Calif Los Angeles Los Angeles CA 90024 USA Nazarbayev Univ Astana Kazakhstan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Fashion-on-demand is becoming an important concept for fashion industries. Many attempts have been made to leverage machine learning methods to generate fashion designs tailored to customers' tastes. However, how to assemble items together (e.g., compatibility) is crucial in designing high-quality outfits for synthesis images. Here we propose a fashion generation model, named OutfitGAN, which contains two core modules: a Generative Adversarial Network and a Compatibility Network. The generative module is able to generate new realistic high quality fashion items from a specific category, while the compatibility network ensures reasonable compatibility among all items. The experimental results show the superiority of our OutfitGAN.

关键词： Industries computer vision conferences Machine learning Generative adversarial networks pattern recognition

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