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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015"

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Blur Interpolation Transformer for Real-World Motion from Blur

Blur Interpolation Transformer for Real-World Motion from Bl...

引用

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

作者： Zhong, Zhihang Cao, Mingdeng Ji, Xiang Zheng, Yinqiang Sato, Imari Univ Tokyo Tokyo Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798350301298

This paper studies the challenging problem of recovering motion from blur, also known as joint deblurring and interpolation or blur temporal super-resolution. The challenges are twofold: 1) the current methods still leave considerable room for improvement in terms of visual quality even on the synthetic dataset, and 2) poor generalization to real-world data. To this end, we propose a blur interpolation transformer (BiT) to effectively unravel the underlying temporal correlation encoded in blur. Based on multi-scale residual Swin transformer blocks, we introduce dual-end temporal supervision and temporally symmetric ensembling strategies to generate effective features for time-varying motion rendering. In addition, we design a hybrid camera system to collect the first real-world dataset of one-to-many blur-sharp video pairs. Experimental results show that BiT has a significant gain over the state-of-the-art methods on the public dataset Adobe240. Besides, the proposed real-world dataset effectively helps the model generalize well to real blurry scenarios. Code and data are available at https://***/zzh-tech/BiT.

关键词： Low-level vision

来源：评论

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Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models

Open-Vocabulary Panoptic Segmentation with Text-to-Image Dif...

引用

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

作者： Xu, Jiarui Liu, Sifei Vahdat, Arash Byeon, Wonmin Wang, Xiaolong De Meo, Shalini Univ Calif San Diego La Jolla CA 92093 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9798350301298

We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained textimage diffusion and discriminative models to perform open-vocabulary panoptic segmentation. Text-to-image diffusion models have the remarkable ability to generate high-quality images with diverse open-vocabulary language descriptions. This demonstrates that their internal representation space is highly correlated with open concepts in the real world. Text-image discriminative models like CLIP, on the other hand, are good at classifying images into open-vocabulary labels. We leverage the frozen internal representations of both these models to perform panoptic segmentation of any category in the wild. Our approach outperforms the previous state of the art by significant margins on both open-vocabulary panoptic and semantic segmentation tasks. In particular, with COCO training only, our method achieves 23.4 PQ and 30.0 mIoU on the ADE20K dataset, with 8.3 PQ and 7.9 mIoU absolute improvement over the previous state of the art. We open-source our code and models at https://***/NVlabs/ODISE.

关键词： and reasoning language vision

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Asymmetric Feature Fusion for Image Retrieval

Asymmetric Feature Fusion for Image Retrieval

引用

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

作者： Wu, Hui Wang, Min Zhou, Wengang Lu, Zhenbo Li, Hougiang Univ Sci & Technol China CAS Key Lab Technol GIPAS Hefei Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

In asymmetric retrieval systems, models with different capacities are deployed on platforms with different computational and storage resources. Despite the great progress, existing approaches still suffer from a dilemma between retrieval efficiency and asymmetric accuracy due to the limited capacity of the lightweight query model. In this work, we propose an Asymmetric Feature Fusion (AFF) paradigm, which advances existing asymmetric retrieval systems by considering the complementarity among different features just at the gallery side. Specifically, it first embeds each gallery image into various features, e.g., local features and global features. Then, a dynamic mixer is introduced to aggregate these features into compact embedding for efficient search. On the query side, only a single lightweight model is deployed for feature extraction. The query model and dynamic mixer are jointly trained by sharing a momentum-updated classifier. Notably, the proposed paradigm boosts the accuracy of asymmetric retrieval without introducing any extra overhead to the query side. Exhaustive experiments on various landmark retrieval datasets demonstrate the superiority of our paradigm.

关键词： detection recognition: Categorization retrieval

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Transfer4D: A framework for frugal motion capture and deformation transfer

Transfer4D: A framework for frugal motion capture and deform...

引用

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

作者： Maheshwari, Shubh Narain, Rahul Hebbalaguppe, Ramya TCS Res Gurugram India Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9798350301298

Animating a virtual character based on a real performance of an actor is a challenging task that currently requires expensive motion capture setups and additional effort by expert animators, rendering it accessible only to large production houses. The goal of our work is to democratize this task by developing a frugal alternative termed "Transfer4D" that uses only commodity depth sensors and further reduces animators' effort by automating the rigging and animation transfer process. Our approach can transfer motion from an incomplete, single-view depth video to a semantically similar target mesh, unlike prior works that make a stricter assumption on the source to be noise-free and watertight. To handle sparse, incomplete videos from depth video inputs and variations between source and target objects, we propose to use skeletons as an intermediary representation between motion capture and transfer. We propose a novel unsupervised skeleton extraction pipeline from a single-view depth sequence that incorporates additional geometric information, resulting in superior performance in motion reconstruction and transfer in comparison to the contemporary methods and making our approach generic. We use non-rigid reconstruction to track motion from the depth sequence, and then we rig the source object using skinning decomposition. Finally, the rig is embedded into the target object for motion retargeting.

关键词： vision + graphics

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PlenVDB: Memory Efficient VDB-Based Radiance Fields for Fast Training and Rendering

PlenVDB: Memory Efficient VDB-Based Radiance Fields for Fast...

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

作者： Yan, Han Liu, Celong Ma, Chao Mei, Xing Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Shanghai Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

In this paper, we present a new representation for neural radiance fields that accelerates both the training and the inference processes with VDB, a hierarchical data structure for sparse volumes. VDB takes both the advantages of sparse and dense volumes for compact data representation and efficient data access, being a promising data structure for NeRF data interpolation and ray marching. Our method, Plenoptic VDB (PlenVDB), directly learns the VDB data structure from a set of posed images by means of a novel training strategy and then uses it for real-time rendering. Experimental results demonstrate the effectiveness and the efficiency of our method over previous arts: First, it converges faster in the training process. Second, it delivers a more compact data format for NeRF data presentation. Finally, it renders more efficiently on commodity graphics hardware. Our mobile PlenVDB demo achieves 30+ FPS, 1280x720 resolution on an iPhone12 mobile phone. Check *** for details.

关键词： vision + graphics

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DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering

DANI-Net: Uncalibrated Photometric Stereo by Differentiable ...

引用

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

作者： Li, Zongrui Zheng, Qian Shi, Boxin Pan, Gang Jiang, Xudong Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Zhejiang Univ State Key Lab Brain Machine Intelligence Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol I Iangzhou Peoples R China Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Peking Univ Sch Comp Sci Natl Engn Res Ctr Visual Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance.

关键词： Physics-based vision and shape-from-X

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Rethinking Few-Shot Medical Segmentation: A Vector Quantization View

Rethinking Few-Shot Medical Segmentation: A Vector Quantizat...

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

作者： Huang, Shiqi Xu, Tingfa Shen, Ning Mu, Feng Li, Jianan Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

The existing few-shot medical segmentation networks share the same practice that the more prototypes, the better performance. This phenomenon can be theoretically interpreted in Vector Quantization (VQ) view: the more prototypes, the more clusters are separated from pixel-wise feature points distributed over the full space. However, as we further think about few-shot segmentation with this perspective, it is found that the clusterization of feature points and the adaptation to unseen tasks have not received enough attention. Motivated by the observation, we propose a learning VQ mechanism consisting of grid-format VQ (GFVQ), self-organized VQ (SOVQ) and residual oriented VQ (ROVQ). To be specific, GFVQ generates the prototype matrix by averaging square grids over the spatial extent, which uniformly quantizes the local details;SOVQ adaptively assigns the feature points to different local classes and creates a new representation space where the learnable local prototypes are updated with a global view;ROVQ introduces residual information to fine-tune the aforementioned learned local prototypes without re-training, which benefits the generalization performance for the irrelevance to the training task. We empirically show that our VQ framework yields the state-of-the-art performance over abdomen, cardiac and prostate MRI datasets and expect this work will provoke a rethink of the current few-shot medical segmentation model design. Our code will soon be publicly available.

关键词： cell microscopy Medical and biological vision

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Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images

Sat2Cap: Mapping Fine-Grained Textual Descriptions from Sate...

引用

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

作者： Dhakal, Aayush Ahmad, Adeel Khanal, Subash Sastry, Srikumar Kerner, Hannah Jacobs, Nathan Washington Univ St Louis MO 63110 USA Taylor Geospatial Inst St Louis MO USA Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9798350365474

We propose a weakly supervised approach for creating maps using free-form textual descriptions. We refer to this work of creating textual maps as zero-shot mapping. Prior works have approached mapping tasks by developing models that predict a fixed set of attributes using overhead imagery. However, these models are very restrictive as they can only solve highly specific tasks for which they were trained. Mapping text, on the other hand, allows us to solve a large variety of mapping problems with minimal restrictions. To achieve this, we train a contrastive learning framework called Sat2Cap on a new large-scale dataset with 6.1M pairs of overhead and ground-level images. For a given location and overhead image, our model predicts the expected CLIP embeddings of the ground-level scenery. The predicted CLIP embeddings are then used to learn about the textual space associated with that location. Sat2Cap is also conditioned on date-time information, allowing it to model temporally varying concepts over a location. Our experimental results demonstrate that our models successfully capture ground-level concepts and allow large-scale mapping of fine-grained textual queries. Our approach does not require any text-labeled data, making the training easily scalable. The code, dataset, and models will be made publicly available.

关键词： Contrastive Learning Text-based Mapping vision-Language Model

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Meta-Personalizing vision-Language Models to Find Named Instances in Video

Meta-Personalizing Vision-Language Models to Find Named Inst...

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

作者： Yeh, Chun-Hsiao Russell, Bryan Sivic, Josef Heilbron, Fabian Caba Jenni, Simon Univ Calif Berkeley Berkeley CA 94720 USA Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Adobe Res San Francisco CA 94107 USA

ISBN: (纸本)9798350301298

Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. While these models allow category-level queries, they currently struggle with personalized searches for moments in a video where a specific object instance such as "My dog Biscuit" appears. We present the following three contributions to address this problem. First, we describe a method to meta-personalize a pre-trained VLM, i.e., learning how to learn to personalize a VLM at test time to search in video. Our method extends the VLM's token vocabulary by learning novel word embeddings specific to each instance. To capture only instance-specific features, we represent each instance embedding as a combination of shared and learned global category features. Second, we propose to learn such personalization without explicit human supervision. Our approach automatically identifies moments of named visual instances in video using transcripts and vision-language similarity in the VLM's embedding space. Finally, we introduce This-Is-My, a personal video instance retrieval benchmark. We evaluate our approach on This-Is-My and Deep-Fashion2 and show that we obtain a 15% relative improvement over the state of the art on the latter dataset.

关键词： language reasoning vision

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DeAR: Debiasing vision-Language Models with Additive Residuals

DeAR: Debiasing Vision-Language Models with Additive Residua...

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

作者： Seth, Ashish Hemani, Mayur Agarwal, Chirag IIT Madras Madras India Adobe Inc San Jose CA USA

ISBN: (纸本)9798350301298

Large pre-trained vision-language models (VLMs) reduce the time for developing predictive models for various vision-grounded language downstream tasks by providing rich, adaptable image and text representations. However, these models suffer from societal biases owing to the skewed distribution of various identity groups in the training data. These biases manifest as the skewed similarity between the representations for specific text concepts and images of people of different identity groups and, therefore, limit the usefulness of such models in real-world high-stakes applications. In this work, we present DEAR (Debiasing with Additive Residuals), a novel debiasing method that learns additive residual image representations to offset the original representations, ensuring fair output representations. In doing so, it reduces the ability of the representations to distinguish between the different identity groups. Further, we observe that the current fairness tests are performed on limited face image datasets that fail to indicate why a specific text concept should/should not apply to them. To bridge this gap and better evaluate DEAR, we introduce the PROTECTED ATTRIBUTE TAG ASSOCIATION (PATA) dataset - a new context-based bias benchmarking dataset for evaluating the fairness of large pre-trained VLMs. Additionally, PATA provides visual context for a diverse human population in different scenarios with both positive and negative connotations. Experimental results for fairness and zero-shot performance preservation using multiple datasets demonstrate the efficacy of our framework. The dataset is released here.

关键词： accountability ethics in vision fairness privacy Transparency

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