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Sliced Optimal Partial Transport

Sliced Optimal Partial Transport

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yikun Bai Bernhard Schmitzer Matthew Thorpe Soheil Kolouri Department of Computer Science Vanderbilt University Institute of Computer Science Göttingen University Department of Mathematics University of Manchester The Alan Turing Institute

Optimal transport (OT) has become exceedingly popular in machine learning, data science, and computer vision. The core assumption in the OT problem is the equal total amount of mass in source and target measures, which limits its application. Optimal Partial Transport (OPT) is a recently proposed solution to this limitation. Similar to the OT problem, the computation of OPT relies on solving a linear programming problem (often in high dimensions), which can become computationally prohibitive. In this paper, we propose an efficient algorithm for calculating the OPT problem between two non-negative measures in one dimension. Next, following the idea of sliced OT distances, we utilize slicing to define the Sliced OPT distance. Finally, we demonstrate the computational and accuracy benefits of the Sliced OPT-based method in various numerical experiments. In particular, we show an application of our proposed Sliced OPT problem in noisy point cloud registration and color adaptation. Our code is available at Github Link.

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TOPLight: Lightweight Neural Networks with Task-Oriented Pretraining for Visible-Infrared recognition

TOPLight: Lightweight Neural Networks with Task-Oriented Pre...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Hao Yu Xu Cheng Wei Peng School of Computer Science Nanjing University of Information Science and Technology China Department of Psychiatry and Behavioral Sciences Stanford University

Visible-infrared recognition (VI recognition) is a challenging task due to the enormous visual difference across heterogeneous images. Most existing works achieve promising results by transfer learning, such as pretraining on the ImageNet, based on advanced neural architectures like ResNet and ViT. However, such methods ignore the neg-ative influence of the pretrained colour prior knowledge, as well as their heavy computational burden makes them hard to deploy in actual scenarios with limited resources. In this paper, we propose a novel task-oriented pretrained lightweight neural network (TOPLight) for VI recognition. Specifically, the TOPLight method simulates the domain conflict and sample variations with the proposed fake do-main loss in the pretraining stage, which guides the network to learn how to handle those difficulties, such that a more general modality-shared feature representation is learned for the heterogeneous images. Moreover, an effective fine-grained dependency reconstruction module (FDR) is developed to discover substantial pattern dependencies shared in two modalities. Extensive experiments on VI person re-identification and VI face recognition datasets demonstrate the superiority of the proposed TOPLight, which signifi-cantly outperforms the current state of the arts while de-manding fewer computational resources.

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NICO++: Towards Better Benchmarking for Domain Generalization

NICO++: Towards Better Benchmarking for Domain Generalizatio...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Xingxuan Zhang Yue He Renzhe Xu Han Yu Zheyan Shen Peng Cui Department of Computer Science Tsinghua University

Despite the remarkable performance that modern deep neural networks have achieved on independent and identically distributed (I.I.D.) data, they can crash under distribution shifts. Most current evaluation methods for do-main generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains. We propose a large-scale benchmark with extensive labeled domains named $NICO^{++}$ along with more rational evaluation methods for comprehensively evaluating DC algorithms. To evaluate DG datasets, we propose two metrics to quantify covariate shift and concept shift, respectively. Two novel generalization bounds from the perspective of data construction are proposed to prove that limited concept shift and significant covariate shift favor the evaluation capability for generalization. Through extensive experiments, $NlCO^{++}$ shows its superior evaluation capability compared with current DG datasets and its contribution in alleviating unfairness caused by the leak of oracle knowledge in model selection. The data and code for the benchmark based on $NICO^{++}$ are available at https://***/xxgege/NICO-plus.

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Referring Multi-Object Tracking

Referring Multi-Object Tracking

引用

conference on computer vision and pattern recognition (CVPR)

作者： Dongming Wu Wencheng Han Tiancai Wang Xingping Dong Xiangyu Zhang Jianbing Shen Beijing Institute of Technology SKL-IOTSC CIS University of Macau MEGVII Technology School of Computer Science Wuhan University Beijing Academy of Artificial Intelligence

Existing referring understanding tasks tend to involve the detection of a single text-referred object. In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT). Its core idea is to employ a language expression as a semantic cue to guide the prediction of multi-object tracking. To the best of our knowledge, it is the first work to achieve an arbitrary number of referent object predictions in videos. To push forward RMOT, we construct one benchmark with scalable expressions based on KITTI, named Refer-KITTI. Specifically, it provides 18 videos with 818 expressions, and each expression in a video is annotated with an average of 10.7 objects. Further, we develop a transformer-based architecture TransRMOT to tackle the new task in an online manner, which achieves impressive detection performance and out-performs other counterparts. The Refer-KITTI dataset and the code are released at https://***.

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VoronoiNet General Functional Approximators with Local Support

VoronoiNet General Functional Approximators with Local Suppo...

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

作者： Williams, Francis Parent-Levesque, Jerome Nowrouzezahrai, Derek Panozzo, Daniele Yi, Kwang Moo Tagliasacchi, Andrea NYU New York NY 10003 USA McGill Univ Montreal PQ Canada Univ Victoria Victoria BC Canada Google Brain Mountain View CA USA

ISBN: (纸本)9781728193601

Voronoi diagrams are highly compact representations that are used in various Graphics applications. In this work, we show how to embed a differentiable version of it - via a novel deep architecture - into a generative deep network. By doing so, we achieve a highly compact latent embedding that is able to provide much more detailed reconstructions, both in 2D and 3D, for various shapes. In this tech report, we introduce our representation and present a set of preliminary results comparing it with recently proposed implicit occupancy networks.

关键词： computer vision

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LightedDepth: Video Depth Estimation in Light of Limited Inference View Angles

LightedDepth: Video Depth Estimation in Light of Limited Inf...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shengjie Zhu Xiaoming Liu Department of Computer Science and Engineering Michigan State University East Lansing MI

Video depth estimation infers the dense scene depth from immediate neighboring video frames. While recent works consider it a simplified structure-from-motion (SfM) problem, it still differs from the SfM in that significantly fewer view angels are available in inference. This setting, however, suits the mono-depth and optical flow estimation. This observation motivates us to decouple the video depth estimation into two components, a normalized pose estimation over af lowmap and a logged residual depth estimation over a mono-depth map. The two parts are unified with an efficient off-the-shelf scale alignment algorithm. Additionally, we stabilize the indoor two-view pose estimation by including additional projection constraints and ensuring sufficient camera translation. Though a two-view algorithm, we validate the benefit of the decoupling with the substantial performance improvement over multi-view iterative prior works on indoor and outdoor datasets. Codes and models are available at https://***/ShngJZ/LightedDepth.

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Structured Kernel Estimation for Photon-Limited Deconvolution

Structured Kernel Estimation for Photon-Limited Deconvolutio...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yash Sanghvi Zhiyuan Mao Stanley H. Chan School of Electrical and Computer Engineering Purdue University

Images taken in a low light condition with the presence of camera shake suffer from motion blur and photon shot noise. While state-of-the-art image restoration networks show promising results, they are largely limited to well-illuminated scenes and their performance drops significantly when photon shot noise is strong. In this paper, we propose a new blur estimation technique customized for photon-limited conditions. The proposed method employs a gradient-based backpropagation method to estimate the blur kernel. By modeling the blur kernel using a low-dimensional representation with the key points on the motion trajectory, we significantly reduce the search space and improve the regularity of the kernel estimation problem. When plugged into an iterative framework, our novel low-dimensional representation provides improved kernel estimates and hence significantly better deconvolution performance when compared to end-to-end trained neural networks. The source code and pretrained mdoels are available at https://***/sanghviyashiitb/structured-kernel-cvpr23

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Dionysus: Recovering Scene Structures by Dividing into Semantic Pieces

Dionysus: Recovering Scene Structures by Dividing into Seman...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Likang Wang Lei Chen Department of Computer Science and Engineering The Hong Kong University of Science and Technology Data Science and Analytics Thrust The Hong Kong University of Science and Technology (Guangzhou)

Most existing 3D reconstruction methods result in either detail loss or unsatisfying efficiency. However, effectiveness and efficiency are equally crucial in real-world applications, e.g., autonomous driving and augmented reality. We argue that this dilemma comes from wasted resources on valueless depth samples. This paper tackles the problem by proposing a novel learning-based 3D reconstruction framework named Dionysus. Our main contribution is to find out the most promising depth candidates from estimated semantic maps. This strategy simultaneously enables high effectiveness and efficiency by attending to the most reliable nominators. Specifically, we distinguish unreliable depth candidates by checking the cross-view semantic consistency and allow adaptive sampling by redistributing depth nominators among pixels. Experiments on the most popular datasets confirm our proposed framework's effectiveness.

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Neumann Network with Recursive Kernels for Single Image Defocus Deblurring

Neumann Network with Recursive Kernels for Single Image Defo...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yuhui Quan Zicong Wu Hui Ji School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Department of Mathematics National University of Singapore Singapore

Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus blurring effects with significant size variation. Motivated by the strong correlation among defocus kernels of different sizes and the blob-type structure of defocus kernels, we propose a learnable recursive kernel representation (RKR) for defocus kernels that expresses a defocus kernel by a linear combination of recursive, separable and positive atom kernels, leading to a compact yet effective and physics-encoded parametrization of the spatially-varying defocus blurring process. Afterwards, a physics-driven and efficient deep model with a cross-scale fusion structure is presented for SIDD, with inspirations from the truncated Neumann series for approximating the matrix inversion of the RKR-based blurring operator. In addition, a reblurring loss is proposed to regularize the RKR learning. Extensive experiments show that, our proposed approach significantly outperforms existing ones, with a model size comparable to that of the top methods.

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DyNCA: Real-Time Dynamic Texture Synthesis Using Neural Cellular Automata

DyNCA: Real-Time Dynamic Texture Synthesis Using Neural Cell...

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conference on computer vision and pattern recognition (CVPR)

作者： Ehsan Pajouheshgar Yitao Xu Tong Zhang Sabine Süsstrunk School of Computer and Communication Sciences EPFL Switzerland

Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos. However, they require a slow iterative optimization process to synthesize a single fixed-size short video, and they do not offer any post-training control over the synthesis process. We propose Dynamic Neural Cellular Automata (DyNCA), a framework for real-time and controllable dynamic texture synthesis. Our method is built upon the recently introduced NCA models and can synthesize infinitely long and arbitrary-sized realistic video textures in real time. We quantitatively and qualitatively evaluate our model and show that our synthesized videos appear more realistic than the existing results. We improve the SOTA DyTS performance by 2 ~ 4 orders of magnitude. Moreover, our model offers several real-time video controls including motion speed, motion direction, and an editing brush tool. We exhibit our trained models in an online interactive demo that runs on local hardware and is accessible on personal computers and smartphones.

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