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Event-Based Frame Interpolation with Ad-hoc Deblurring

Event-Based Frame Interpolation with Ad-hoc Deblurring

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

作者： Sun, Lei Sakaridis, Christos Liang, Jingyun Sun, Peng Cao, Jiezhang Zhang, Kai Jiang, Qi Wang, Kaiwei Van Gool, Luc Zhejiang Univ Hangzhou Peoples R China Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9798350301298

The performance of video frame interpolation is inherently correlated with the ability to handle motion in the input scene. Even though previous works recognize the utility of asynchronous event information for this task, they ignore the fact that motion may or may not result in blur in the input video to be interpolated, depending on the length of the exposure time of the frames and the speed of the motion, and assume either that the input video is sharp, restricting themselves to frame interpolation, or that it is blurry, including an explicit, separate deblurring stage before interpolation in their pipeline. We instead propose a general method for event-based frame interpolation that performs deblurring ad-hoc and thus works both on sharp and blurry input videos. Our model consists in a bidirectional recurrent network that naturally incorporates the temporal dimension of interpolation and fuses information from the input frames and the events adaptively based on their temporal proximity. In addition, we introduce a novel real-world high-resolution dataset with events and color videos named HighREV, which provides a challenging evaluation setting for the examined task. Extensive experiments on the standard GoPro benchmark and on our dataset show that our network consistently outperforms previous state-of-the-art methods on frame interpolation, single image deblurring and the joint task of interpolation and deblurring. Our code and dataset are available at https://***/AHupuJR/REFID.

关键词： Low-level vision

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Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in vision-Language Navigation

Counterfactual Cycle-Consistent Learning for Instruction Fol...

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

作者： Wang, Hanqing Liang, Wei Shen, Jianbing Van Gool, Luc Wang, Wenguan Beijing Inst Technol Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland Univ Macau SKL IOTSC Taipa Macao Peoples R China Univ Technol Sydney AAII ReLER Sydney NSW Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Since the rise of vision-language navigation (VLN), great progress has been made in instruction following - building a follower to navigate environments under the guidance of instructions. However, far less attention has been paid to the inverse task: instruction generation - learning a speaker to generate grounded descriptions for navigation routes. Existing VLN methods train a speaker independently and often treat it as a data augmentation tool to strengthen the follower, while ignoring rich cross-task relations. Here we describe an approach that learns the two tasks simultaneously and exploits their intrinsic correlations to boost the training of each: the follower judges whether the speaker-created instruction explains the original navigation route correctly, and vice versa. Without the need of aligned instruction-path pairs, such cycle-consistent learning scheme is complementary to task-specific training targets defined on labeled data, and can also be applied over unlabeled paths (sampled without paired instructions). Another agent, called creator is added to generate counterfactual environments. It greatly changes current scenes yet leaves novel items - which are vital for the execution of original instructions - unchanged. Thus more informative training scenes are synthesized and the three agents compose a powerful VLN learning system. Extensive experiments on a standard benchmark show that our approach improves the performance of various follower models and produces accurate navigation instructions.

关键词： Training Learning systems computer vision Correlation Navigation Computational modeling Buildings

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Weakly Supervised High-Fidelity Clothing Model Generation

Weakly Supervised High-Fidelity Clothing Model Generation

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

作者： Feng, Ruili Ma, Cheng Shen, Chengji Gao, Xin Liu, Zhenjiang Li, Xiaobo Ou, Kairi Zhao, Deli Zha, Zheng-Jun Univ Sci & Technol China Hefei Anhui Peoples R China Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The development of online economics arouses the demand of generating images of models on product clothes, to display new clothes and promote sales. However, the expensive proprietary model images challenge the existing image virtual try-on methods in this scenario, as most of them need to be trained on considerable amounts of model images accompanied with paired clothes images. In this paper, we propose a cheap yet scalable weakly-supervised method called Deep Generative Projection (DGP) to address this specific scenario. Lying in the heart of the proposed method is to imitate the process of human predicting the wearing effect, which is an unsupervised imagination based on life experience rather than computation rules learned from supervisions. Here a pretrained StyleGAN is used to capture the practical experience of wearing. Experiments show that projecting the rough alignment of clothing and body onto the StyleGAN space can yield photo-realistic wearing results. Experiments on real scene proprietary model images demonstrate the superiority of DGP over several state-of-the-art supervised methods when generating clothing model images.

关键词： Heart Economics computer vision Biological system modeling Computational modeling Clothing Training data

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UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation

UniT: Unified Knowledge Transfer for Any-shot Object Detecti...

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

作者： Khandelwal, Siddhesh Goyal, Raghav Sigal, Leonid Univ British Columbia Dept Comp Sci Vancouver BC Canada Vector Inst AI Toronto ON Canada CIFAR AI Chair Montreal PQ Canada

ISBN: (纸本)9781665445092

Methods for object detection and segmentation rely on large scale instance-level annotations for training, which are difficult and time-consuming to collect. Efforts to alleviate this look at varying degrees and quality of supervision. Weakly-supervised approaches draw on image-level labels to build detectors/segmentors, while zero/few-shot methods assume abundant instance-level data for a set of base classes, and none to a few examples for novel classes. This taxonomy has largely saved algorithmic designs. In this work, we aim to bridge this divide by proposing an intuitive and unified semi-supervised model that is applicable to a range of supervision: from zero to a few instance-level samples per novel class. For base classes, our model learns a mapping from weakly-supervised to fully-supervised detectors/segmentors. By learning and leveraging visual and lingual similarities between the novel and base classes, we transfer those mappings to obtain detectors/segmentors for novel classes;refining them with a few novel class instance-level annotated samples, if available. The overall model is end-to-end trainable and highly flexible(1). Through extensive experiments on MS-COCO [32] and Pascal VOC [14] benchmark datasets we show improved performance in a variety of settings.

关键词： Training Visualization Image segmentation computer vision Computational modeling Taxonomy Refining

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Contrastive Regression for Domain Adaptation on Gaze Estimation

Contrastive Regression for Domain Adaptation on Gaze Estimat...

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

作者： Wang, Yaoming Jiang, Yangzhou Li, Jin Ni, Bingbing Dai, Wenrui Li, Chenglin Xiong, Hongkai Li, Teng Shanghai Jiao Tong Univ Shanghai Peoples R China Huawei Inc Shenzhen Peoples R China Anhui Univ Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance. However, its success depends on expensive and cumbersome annotation capture. When lacking precise annotation, the large domain gap hinders the performance of trained models on new domains. In this paper, we propose a novel gaze adaptation approach, namely Contrastive Regression Gaze Adaptation (CRGA), for generalizing gaze estimation on the target domain in an unsupervised manner. CRGA leverages the Contrastive Domain Generalization (CDG) module to learn the stable representation from the source domain and leverages the Contrastive Self-training Adaptation (CSA) module to learn from the pseudo labels on the target domain. The core of both CDG and CSA is the Contrastive Regression (CR) loss, a novel contrastive loss for regression by pulling features with closer gaze directions closer together while pushing features with farther gaze directions farther apart. Experimentally, we choose ETH-XGAZE and Gaze-360 as the source domain and test the domain generalization and adaptation performance on MPIIGAZE, RT-GENE, Gaze-Capture, EyeDiap respectively. The results demonstrate that our CRGA achieves remarkable performance improvement compared with the baseline models and also outperforms the state-of-the-art domain adaptation approaches on gaze adaptation tasks.

关键词： Representation learning Deep learning Adaptation models computer vision Annotations Face recognition Neural networks

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Rotation Averaging and Strong Duality 31

Rotation Averaging and Strong Duality

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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Eriksson, Anders Olsson, Carl Kahl, Fredrik Chin, Tat-Jun Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld Australia Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden Lund Univ Ctr Math Sci Lund Sweden Univ Adelaide Sch Comp Sci Adelaide SA Australia

ISBN: (纸本)9781538664209

In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally. We show how to circumvent this difficulty through the use of Lagrangian duality. While such an approach is well-known it is normally not guaranteed to provide a tight relaxation. Based on spectral graph theory, we analytically prove that in many cases there is no duality gap unless the noise levels are severe. This allows us to obtain certifiably global solutions to a class of important non-convex problems in polynomial time. We also propose an efficient, scalable algorithm that outperforms general purpose numerical solvers and is able to handle the large problem instances commonly occurring in structure from motion settings. The potential of this proposed method is demonstrated on a number of different problems, consisting of both synthetic and real-world data.

关键词： Cameras Symmetric matrices computer vision Noise level Structure from motion Eigenvalues and eigenfunctions Standards

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Efficient deformable shape correspondence via multiscale spectral manifold wavelets preservation

Efficient deformable shape correspondence via multiscale spe...

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

作者： Hu, Ling Li, Qinsong Liu, Shengjun Liu, Xinru Cent South Univ Inst Engn Modeling & Sci Comp Changsha Peoples R China Hunan First Normal Univ Sch Math & Computat Sci Changsha Peoples R China Cent South Univ State Key Lab High Performance Mfg Complex Changsha Peoples R China

ISBN: (纸本)9781665445092

The functional map framework has proven to be extremely effective for representing dense correspondences between deformable shapes. A key step in this framework is to formulate suitable preservation constraints to encode the geometric information that must be preserved by the unknown map. For this issue, we construct novel and powerful constraints to determine the functional map, where multiscale spectral manifold wavelets are required to be preserved at each scale correspondingly. Such constraints allow us to extract significantly more information than previous methods, especially those based on descriptor preservation constraints, and strongly ensure the isometric property of the map. In addition, we also propose a remarkable efficient iterative method to alternatively update the functional maps and pointwise maps. Moreover, when we use the tight wavelet frames in iterations, the computation of the functional maps boils down to a simple filtering procedure with low-pass and various band-pass filters, which avoids time-consuming solving large systems of linear equations commonly presented in functional maps. We demonstrate on a wide variety of experiments with different datasets that our approach achieves significant improvements both in the shape correspondence quality and the computing efficiency.

关键词： Manifolds Band-pass filters computer vision Shape Filtering Color Iterative methods

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Memory-augmented Deep Conditional Unfolding Network for Pan-sharpening

Memory-augmented Deep Conditional Unfolding Network for Pan-...

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

作者： Yang, Gang Zhou, Man Yan, Keyu Liu, Aiping Fu, Xueyang Wang, Fan Univ Sci & Technol China Hefei Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing systems and deep learning-based methods have achieved remarkable success. However, most existing methods are designed in a blackbox principle, lacking sufficient interpretability. Additionally, they ignore the different characteristics of each band of MS images and directly concatenate them with panchromatic (PAN) images, leading to severe copy artifacts [9]. To address the above issues, we propose an interpretable deep neural network, namely Memory-augmented Deep Conditional Unfolding Network with two specified core designs. Firstly, considering the degradation process, it formulates the Pan-sharpening problem as the minimization of a variational model with denoising-based prior and non-local auto-regression prior which is capable of searching the similarities between long-range patches, benefiting the texture enhancement. A novel iteration algorithm with builtin CNNs is exploited for transparent model design. Secondly, to fully explore the potentials of different bands of MS images, the PAN image is combined with each band of MS images, selectively providing the high frequency details and alleviating the copy artifacts. Extensive experimental results validate the superiority of the proposed algorithm against other state-of-the-art methods.

关键词： Learning systems Degradation Deep learning computer vision Neural networks Pansharpening Search problems

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MULTIFLOW: Shifting Towards Task-Agnostic vision-Language Pruning

MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pr...

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

作者： Farina, Matteo Mancini, Massimiliano Cunegatti, Elia Liu, Gaowen Iacca, Giovanni Ricci, Elisa Univ Trento Trento Italy Cisco Res Res Triangle Pk NC USA Fdn Bruno Kessler Povo Italy

ISBN: (纸本)9798350353006

While excellent in transfer learning, vision-Language models (VLMs) come with high computational costs due to their large number of parameters. To address this issue, removing parameters via model pruning is a viable solution. However, existing techniques for VLMs are task-specific, and thus require pruning the network from scratch for each new task of interest. In this work, we explore a new direction: Task-Agnostic vision-Language Pruning (TA-VLP). Given a pretrained VLM, the goal is to find a unique pruned counterpart transferable to multiple unknown downstream tasks. In this challenging setting, the transferable representations already encoded in the pretrained model are a key aspect to preserve. Thus, we propose Multimodal Flow Pruning (MULTIFLOW), a first, gradient-free, pruning framework for TA-VLP where: (i) the importance of a parameter is expressed in terms of its magnitude and its information flow, by incorporating the saliency of the neu-rons it connects;and (ii) pruning is driven by the emergent (multimodal) distribution of the VLM parameters after pretraining. We benchmark eight state-of-the-art pruning algorithms in the context of TA-VLP, experimenting with two VLMs, three vision-language tasks, and three pruning ratios. Our experimental results show that MULTIFLOW outperforms recent sophisticated, combinatorial competitors in the vast majority of the cases, paving the way towards addressing TA-VLP. The code is publicly available at https://***/FarinaMatteo/multiflow.

关键词： multimodal learning neural network pruning sparse neural networks transfer learning vision-language models

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Representing 3D Shapes with Probabilistic Directed Distance Fields

Representing 3D Shapes with Probabilistic Directed Distance ...

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

作者： Aumentado-Armstrong, Tristan Tsogkas, Stavros Dickinson, Sven Jepson, Allan Samsung AI Ctr Toronto Toronto ON Canada Univ Toronto Toronto ON Canada Vector Inst AI Toronto ON Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Differentiable rendering is an essential operation in modern vision, allowing inverse graphics approaches to 3D understanding to be utilized in modern machine learning frameworks. Explicit shape representations (voxels, point clouds, or meshes), while relatively easily rendered, often suffer from limited geometric fidelity or topological constraints. On the other hand, implicit representations (occupancy, distance, or radiance fields) preserve greater fidelity, but suffer from complex or inefficient rendering processes, limiting scalability. In this work, we endeavour to address both shortcomings with a novel shape representation that allows fast differentiable rendering within an implicit architecture. Building on implicit distance representations, we define Directed Distance Fields (DDFs), which map an oriented point (position and direction) to surface visibility and depth. Such a field can render a depth map with a single forward pass per pixel, enable differential surface geometry extraction (e.g., surface normals and curvatures) via network derivatives, be easily composed, and permit extraction of classical unsigned distance fields. Using probabilistic DDFs (PDDFs), we show how to model inherent discontinuities in the underlying field. Finally, we apply our method to fitting single shapes, unpaired 3D-aware generative image modelling, and single-image 3D reconstruction tasks, showcasing strong performance with simple architectural components via the versatility of our representation.

关键词： Surface reconstruction Solid modeling Three-dimensional displays Shape Scalability Rendering (computer graphics) Probabilistic logic

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