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IMODAL: creating learnable user-defined deformation models

IMODAL: creating learnable user-defined deformation models

引用

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

作者： Lacroix, Leander Charlier, Benjamin Trouve, Alain Gris, Barbara Univ Paris Saclay INSERM U1299 Gif Sur Yvette France Univ Montpellier IMAG Montpellier France ENS Paris Saclay Ctr Borelli Cachan France Sorbonne Univ CNRS LJLL Paris France

ISBN: (纸本)9781665445092

A natural way to model the evolution of an object (growth of a leaf for instance) is to estimate a plausible deforming path between two observations. This interpolation process can generate deceiving results when the set of considered deformations is not relevant to the observed data. To overcome this issue, the framework of deformation modules allows to incorporate in the model structured deformation patterns coming from prior knowledge on the data. The goal of this article is twofold. First defining new deformation modules incorporating structures coming from the elastic properties of the objects. Second, presenting the IMODAL library allowing to perform registration through structured deformations. This library is modular: adapted priors can be easily defined by the user, several priors can be combined into a global one and various types of data can be considered such as curves, meshes or images. It can be downloaded at https://***/imodal.

关键词： Deformable models Adaptation models Interpolation computer vision Computational modeling Libraries Data models

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Reinforced Attention for Few-Shot Learning and Beyond

Reinforced Attention for Few-Shot Learning and Beyond

引用

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

作者： Hong, Jie Fang, Pengfei Li, Weihao Zhang, Tong Simon, Christian Harandi, Mehrtash Petersson, Lars Australian Natl Univ Canberra ACT Australia Data61 CSIRO Canberra ACT Australia Ecole Polytech Fed Lausanne Lausanne Switzerland Monash Univ Clayton Vic Australia

ISBN: (纸本)9781665445092

Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images. In this paper, we propose to equip the backbone network with an attention agent, which is trained by reinforcement learning. The policy gradient algorithm is employed to train the agent towards adaptively localizing the representative regions on feature maps over time. We further design a reward function based on the prediction of the held-out data, thus helping the attention mechanism to generalize better across the unseen classes. The extensive experiments show, with the help of the reinforced attention, that our embedding network has the capability to progressively generate a more discriminative representation in few-shot learning. Moreover, experiments on the task of image classification also show the effectiveness of the proposed design.

关键词： computer vision Image recognition Computational modeling Reinforcement learning Prediction algorithms Data models pattern recognition

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Automatic Correction of Internal Units in Generative Neural Networks

Automatic Correction of Internal Units in Generative Neural ...

引用

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

作者： Tousi, Ali Jeong, Haedong Han, Jiyeon Choi, Hwanil Choi, Jaesik Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Ulsan Natl Inst Sci & Technol UNIST Ulsan South Korea INEEJI Seoul South Korea

ISBN: (纸本)9781665445092

Generative Adversarial Networks (GANs) have shown satisfactory performance in synthetic image generation by devising complex network structure and adversarial training scheme. Even though GANs are able to synthesize realistic images, there exists a number of generated images with defective visual patterns which are known as artifacts. While most of the recent work tries to fix artifact generations by perturbing latent code, few investigate internal units of a generator to fix them. In this work, we devise a method that automatically identifies the internal units generating various types of artifact images. We further propose the sequential correction algorithm which adjusts the generation flow by modifying the detected artifact units to improve the quality of generation while preserving the original outline. Our method outperforms the baseline method in terms of FID-score and shows satisfactory results with human evaluation.

关键词： Training Visualization computer vision Codes Image synthesis Neural networks Complex networks

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Partial Person Re-identification with Part-Part Correspondence Learning

Partial Person Re-identification with Part-Part Corresponden...

引用

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

作者： He, Tianyu Shen, Xu Huang, Jianqiang Chen, Zhibo Hua, Xian-Sheng Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781665445092

Driven by the success of deep learning, the last decade has seen rapid advances in person re-identification (re-ID). Nonetheless, most of approaches assume that the input is given with the fulfillment of expectations, while imperfect input remains rarely explored to date, which is a non-trivial problem since directly apply existing methods without adjustment can cause significant performance degradation. In this paper, we focus on recognizing partial (flawed) input with the assistance of proposed Part-Part Correspondence Learning (PPCL), a self-supervised learning framework that learns correspondence between image patches without any additional part-level supervision. Accordingly, we propose Part-Part Cycle (PP-Cycle) constraint and Part-Part Triplet (PP-Triplet) constraint that exploit the duality and uniqueness between corresponding image patches respectively. We verify our proposed PPCL on several partial person re-ID benchmarks. Experimental results demonstrate that our approach can surpass previous methods in terms of the standard evaluation metric.

关键词： Measurement Degradation Deep learning computer vision Image recognition Benchmark testing pattern recognition

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SSAN: Separable Self-Attention Network for Video Representation Learning

SSAN: Separable Self-Attention Network for Video Representat...

引用

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

作者： Guo, Xudong Guo, Xun Lu, Yan Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China MSRA Beijing Peoples R China

ISBN: (纸本)9781665445092

Self-attention has been successfully applied to video representation learning due to the effectiveness of modeling long range dependencies. Existing approaches build the dependencies merely by computing the pairwise correlations along spatial and temporal dimensions simultaneously. However;spatial correlations and temporal correlations represent different contextual information of scenes and temporal reasoning. Intuitively, learning spatial contextual information first will benefit temporal modeling. In this paper;we propose a separable self-attention (SSA) module, which models spatial and temporal correlations sequentially so that spatial contexts can be efficiently used in temporal modeling. By adding SSA module into 21) CNN, we build a SSA network (SSAN) for video representation learning. On the task of video action recognition, our approach outperforms state-of:the-art methods on Something Something and Kinetics-400 datasets. Our models often outperform counterparts with shallower network and fewer modalities. We further verify the semantic learning ability of our method in visual-language task of video retrieval, which showcases the homogeneity of video representations and text embeddings. On MSR-VIT and Youcook2 dowsers, video representations learnt by SSA significantly improve the state-of-the-art performance.

关键词： computer vision Correlation Pairwise error probability Computational modeling Semantics Cognition pattern recognition

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Confluent Vessel Trees with Accurate Bifurcations

Confluent Vessel Trees with Accurate Bifurcations

引用

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

作者： Zhang, Zhongwen Marin, Dmitrii Drangova, Maria Boykov, Yuri Univ Waterloo Waterloo ON Canada Vector Res Inst Toronto ON Canada Robarts Res London ON Canada

ISBN: (纸本)9781665445092

We are interested in unsupervised reconstruction of complex near-capillary vasculature with thousands of bifurcations where supervision and learning are infeasible. Unsupervised methods can use many structural constraints, e.g. topology, geometry, physics. Common techniques use variants of MST on geodesic tubular graphs minimizing symmetric pairwise costs, i.e. distances. We show limitations of such standard undirected tubular graphs producing typical errors at bifurcations where flow\directedness" is critical. We introduce a new general concept of confluence for continuous oriented curves forming vessel trees and show how to enforce it on discrete tubular graphs. While confluence is a high-order property, we present an efficient practical algorithm for reconstructing confluent vessel trees using minimum arborescence on a directed graph enforcing confluence via simple flow-extrapolating arc construction. Empirical tests on large near-capillary sub-voxel vasculature volumes demonstrate significantly improved reconstruction accuracy at bifurcations. Our code has also been made publicly available(1).

关键词： Geometry computer vision Costs Codes Directed graphs Bifurcation Topology

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3D Human Action Representation Learning via Cross-View Consistency Pursuit

3D Human Action Representation Learning via Cross-View Consi...

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

作者： Li, Linguo Wang, Minsi Ni, Bingbing Wang, Hang Yang, Jiancheng Zhang, Wenjun Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multiview complementary supervision signal. CrosSCLR consists of both single-view contrastive learning (Skeleton-CLR) and cross-view consistent knowledge mining (CVC-KM) modules, integrated in a collaborative learning manner. It is noted that CVC-KM works in such a way that high-confidence positive/negative samples and their distributions are exchanged among views according to their embedding similarity, ensuring cross-view consistency in terms of contrastive context, i.e., similar distributions. Extensive experiments show that CrosSCLR achieves remarkable action recognition results on NTU-60 and NTU-120 dmasets under unsupervised settings, with observed higherquality action representations. Our code is available at https://***/Linguoli/CrosSCLR.

关键词： computer vision Three-dimensional displays Codes Collaborative work pattern recognition

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Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach

Hybrid Rotation Averaging: A Fast and Robust Rotation Averag...

引用

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

作者： Chen, Yu Zhao, Ji Kneip, Laurent Peking Univ Beijing Peoples R China TuSimple San Diego CA USA ShanghaiTech Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

We address rotation averaging (RA) and its application to real-world 3D reconstruction. Local optimisation based approaches are the de facto choice, though they only guarantee a local optimum. Global optimisers ensure global optimality in low noise conditions, but they are inefficient and may easily deviate under the influence of outliers or elevated noise levels. We push the envelope of rotation averaging by leveraging the advantages of a global RA method and a local RA method. Combined with a fast view graph filtering as preprocessing, the proposed hybrid approach is robust to outliers. We further apply the proposed hybrid rotation averaging approach to incremental Structure from Motion (SfM), the accuracy and robustness of SfM are both improved by adding the resulting global rotations as regularisers to bundle adjustment. Overall, we demonstrate high practicality of the proposed method as bad camera poses are effectively corrected and drift is reduced.

关键词： Bundle adjustment computer vision Structure from motion Filtering Pipelines Optimization methods Cameras

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Line Segment Detection Using Transformers without Edges

Line Segment Detection Using Transformers without Edges

引用

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

作者： Xu, Yifan Xu, Weijian Cheung, David Tu, Zhuowen Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781665445092

In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free. Our method, named LinE segment TRansformers (LEM), takes advantages of having integrated tokenized queries, a self-attention mechanism, and encoding-decoding strategy within Transformers by skipping standard heuristic designs for the edge element detection and perceptual grouping processes. We equip Transformers with a multi-scale encoder/decoder strategy to perform fine-grained line segment detection under a direct endpoint distance loss. This loss term is particularly suitable for detecting geometric structures such as line segments that are not conveniently represented by the standard bounding box representations. The Transformers learn to gradually refine line segments through layers of self-attention. In our experiments, we show state-of-the-art results on Wireframe and YorkUrban benchmarks.

关键词： computer vision Casting Image edge detection Computational modeling Detectors Predictive models Transformers

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Shape and Material Capture at Home

Shape and Material Capture at Home

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

作者： Lichy, Daniel Wu, Jiaye Sengupta, Soumyadip Jacobs, David W. Univ Maryland College Pk MD 20742 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781665445092

In this paper, we present a technique for estimating the geometry and reflectance of objects using only a camera, flashlight, and optionally a tripod. We propose a simple data capture technique in which the user goes around the object, illuminating it with a flashlight and capturing only a few *** main technical contribution is the introduction of a recursive neural architecture, which can predict geometry and reflectance at 2(k) x 2(k) resolution given an input image at 2(k) x 2(k) and estimated geometry and reflectance from the previous step at 2(k-1) x 2(k -1). This recursive architecture, termed RecNet, is trained with 256 x256 resolution but can easily operate on 1024 x 1024 images during inference. We show that our method produces more accurate surface normal and albedo, especially in regions of specular highlights and cast shadows, compared to previous approaches, given three or fewer input images.

关键词： Reflectivity Geometry Training computer vision Image resolution Shape Neural networks

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