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Domain Consensus Clustering for Universal Domain Adaptation

Domain Consensus Clustering for Universal Domain Adaptation

引用

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

作者： Li, Guangrui Kang, Guoliang Zhu, Yi Wei, Yunchao Yang, Yi Univ Technol Sydney ReLER Lab AAII Sydney NSW Australia Carnegie Mellon Univ Pittsburgh PA 15213 USA Amazon Web Serv Seattle WA USA

ISBN: (纸本)9781665445092

In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to transfer the knowledge from source to target under unaligned label space. The main challenge of UniDA lies in how to separate common classes (i.e., classes shared across domains), from private classes (i.e., classes only exist in one domain). Previous works treat the private samples in the target as one generic class but ignore their intrinsic structure. Consequently, the resulting representations are not compact enough in the latent space and can be easily confused with common samples. To better exploit the intrinsic structure of the target domain, we propose Domain Consensus Clustering (DCC), which exploits the domain consensus knowledge to discover discriminative clusters on both common samples and private ones. Specifically, we draw the domain consensus knowledge from two aspects to facilitate the clustering and the private class discovery, i.e., the semantic-level consensus, which identifies the cycle-consistent clusters as the common classes, and the sample-level consensus, which utilizes the cross-domain classification agreement to determine the number of clusters and discover the private classes. Based on DCC, we are able to separate the private classes from the common ones, and differentiate the private classes themselves. Finally, we apply a class-aware alignment technique on identified common samples to minimize the distribution shift, and a prototypical regularizer to inspire discriminative target clusters. Experiments on four benchmarks demonstrate DCC significantly outperforms previous state-of-the-arts.

关键词： computer vision Benchmark testing pattern recognition

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Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization

Learning to Refactor Action and Co-occurrence Features for T...

引用

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

作者： Xia, Kun Wang, Le Zhou, Sanping Zheng, Nanning Tang, Wei Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Univ Illinois Chicago IL USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The main challenge of Temporal Action Localization is to retrieve subtle human actions from various co-occurring ingredients, e.g., context and background, in an untrimmed video. While prior approaches have achieved substantial progress through devising advanced action detectors, they still suffer from these co-occurring ingredients which often dominate the actual action content in videos. In this paper, we explore two orthogonal but complementary aspects of a video snippet, i.e., the action features and the co-occurrence features. Especially, we develop a novel auxiliary task by decoupling these two types of features within a video snippet and recombining them to generate a new feature representation with more salient action information for accurate action localization. We term our method RefactorNet, which first explicitly factorizes the action content and regularizes its co-occurrence features, and then synthesizes a new actiondominated video representation. Extensive experimental results and ablation studies on THUMOS14 and ActivityNet v1.3 demonstrate that our new representation, combined with a simple action detector, can significantly improve the action localization performance.

关键词： Location awareness computer vision Tracking Detectors Feature extraction pattern recognition Task analysis

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Deep Stable Learning for Out-Of-Distribution Generalization

Deep Stable Learning for Out-Of-Distribution Generalization

引用

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

作者： Zhang, Xingxuan Cui, Peng Xu, Renzhe Zhou, Linjun He, Yue Shen, Zheyan Tsinghua Univ Dept Comp Sci Beijing Peoples R China Beijing Key Lab Networked Multimedia Beijing Peoples R China

ISBN: (纸本)9781665445092

Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise. Therefore, eliminating the impact of distribution shifts between training and testing data is crucial for building performance-promising deep models. Conventional methods assume either the known heterogeneity of training data (e.g. domain labels) or the approximately equal capacities of different domains. In this paper, we consider a more challenging case where neither of the above assumptions holds. We propose to address this problem by removing the dependencies between features via learning weights for training samples, which helps deep models get rid of spurious correlations and, in turn, concentrate more on the true connection between discriminative features and labels. Extensive experiments clearly demonstrate the effectiveness of our method on multiple distribution generalization benchmarks compared with state-of-the-art counterparts. Through extensive experiments on distribution generalization benchmarks including PACS, VLCS, MNIST-M, and NICO, we show the effectiveness of our method compared with state-of-the-art counterparts.

关键词： Training Deep learning computer vision Correlation Computational modeling Training data Benchmark testing

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Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections

Iterative Next Boundary Detection for Instance Segmentation ...

引用

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

作者： Gillert, Alexander Resente, Giulia Anadon-Rosell, Alba Wilmking, Martin von Lukas, Uwe Freiherr Fraunhofer Inst Comp Graph Res IGD Rostock Germany Ernst Moritz Arndt Univ Inst Bot & Landscape Ecol Greifswald Germany Ctr Res Ecol & Forestry Applicat CREAF Barcelona Spain Univ Rostock Inst Visual & Analyt Comp Rostock Germany

ISBN: (纸本)9798350301298

We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate performance of existing methods. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://***/alexander-g/INBD.

关键词： cell microscopy Medical and biological vision

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An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM 32

An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Geneva, Patrick Maley, James Huang, Guoquan Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9781728132938

It holds great implications for practical applications to enable centimeter-accuracy positioning for mobile and wearable sensor systems. In this paper, we propose a novel, high-precision, efficient visual-inertial (VI)-SLAM algorithm, termed Schmidt-EKF VI-SLAM (SEVIS), which optimally fuses IMU measurements and monocular images in a tightly-coupled manner to provide 3D motion tracking with bounded error. In particular, we adapt the Schmidt Kalman filter formulation to selectively include informative features in the state vector while treating them as nuisance parameters (or Schmidt states) once they become matured. This change in modeling allows for significant computational savings by no longer needing to constantly update the Schmidt states (or their covariance), while still allowing the EKF to correctly account for their cross-correlations with the active states. As a result, we achieve linear computational complexity in terms of map size, instead of quadratic as in the standard SLAM systems. In order to fully exploit the map information to bound navigation drifts, we advocate efficient keyframe-aided 2D-to-2D feature matching to find reliable correspondences between current 2D visual measurements and 3D map features. The proposed SEVIS is extensively validated in both simulations and experiments.

关键词： 3D from Multiview and Sensors Motion and Tracking Optimization Methods Robotics + Driving vision Applications and Sys

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NFormer: Robust Person Re-identification with Neighbor Transformer

NFormer: Robust Person Re-identification with Neighbor Trans...

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

作者： Wang, Haochen Shen, Jiayi Liu, Yongtuo Gao, Yan Gavves, Efstratios Univ Amsterdam Amsterdam Netherlands Xiaohongshu Inc Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Person re-identification aims to retrieve persons in highly varying settings across different cameras and scenarios, in which robust and discriminative representation learning is crucial. Most research considers learning representations from single images, ignoring any potential interactions between them. However, due to the high intra-identity variations, ignoring such interactions typically leads to outlier features. To tackle this issue, we propose a Neighbor Transformer Network, or NFormer, which explicitly models interactions across all input images, thus suppressing outlier features and leading to more robust representations overall. As modelling interactions between enormous amount of images is a massive task with lots of distractors, NFormer introduces two novel modules, the Landmark Agent Attention, and the Reciprocal Neighbor Softmax. Specifically, the Landmark Agent Attention efficiently models the relation map between images by a low-rank factorization with a few landmarks in feature space. Moreover, the Reciprocal Neighbor Softmax achieves sparse attention to relevant-rather than all-neighbors only, which alleviates interference of irrelevant representations and further relieves the computational burden. In experiments on four large-scale datasets, NFormer achieves a new state-of-the-art. The code is released at https:// ***/haochenheheda/NFormer.

关键词： Representation learning Training computer vision Codes Computational modeling Focusing Interference

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RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation 30

RefineNet: Multi-Path Refinement Networks for High-Resolutio...

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lin, Guosheng Milan, Anton Shen, Chunhua Reid, Ian Nanyang Technol Univ Singapore Singapore Univ Adelaide Adelaide SA Australia Australian Ctr Robot Vis Brisbane Qld Australia

ISBN: (纸本)9781538604571

Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new stateof- the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.

关键词： multi-path pooling Semantics image resolution object recognition Refining segmentation Segmentation Identity mapping RESIDUAL

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RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization

RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust ...

引用

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

作者： Xu, Yan Lin, Kwan-Yee Zhang, Guofeng Wang, Xiaogang Li, Hongsheng Chinese Univ Hong Kong Hong Kong Peoples R China Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

6-DoF object pose estimation from a monocular image is challenging, and a post-refinement procedure is generally needed for high-precision estimation. In this paper, we propose a framework based on a recurrent neural network (RNN) for object pose refinement, which is robust to erroneous initial poses and occlusions. During the recurrent iterations, object pose refinement is formulated as a nonlinear least squares problem based on the estimated correspondence field (between a rendered image and the observed image). The problem is then solved by a differentiable Levenberg-Marquardt (LM) algorithm enabling end-to-end training. The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover the object poses. Furthermore, to improve the robustness to occlusion, we introduce a consistency-check mechanism based on the learned descriptors of the 3D model and observed 2D images, which downweights the unreliable correspondences during pose optimization. Extensive experiments on LINEMOD, Occlusion-LINEMOD, and YCB-Video datasets validate the effectiveness of our method and demonstrate state-of-the-art performance.

关键词： Training Solid modeling computer vision Recurrent neural networks Three-dimensional displays Computational modeling Pose estimation

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

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

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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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Polygonal Point Set Tracking

Polygonal Point Set Tracking

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

作者： Nam, Gunhee Heo, Miran Oh, Seoung Wug Lee, Joon-Young Kim, Seon Joo Lunit Inc Seoul South Korea Yonsei Univ Seoul South Korea Adobe Res Redmond WA USA

ISBN: (纸本)9781665445092

In this paper, we propose a novel learning-based polygonal point set tracking method. Compared to existing video object segmentation (VOS) methods that propagate pixel-wise object mask information, we propagate a polygonal point set over frames. Specifically, the set is defined as a subset of points in the target contour, and our goal is to track corresponding points on the target contour. Those outputs enable us to apply various visual effects such as motion tracking, part deformation, and texture mapping. To this end, we propose a new method to track the corresponding points between frames by the global-local alignment with delicately designed losses and regularization terms. We also introduce a novel learning strategy using synthetic and VOS datasets that makes it possible to tackle the problem without developing the point correspondence dataset. Since the existing datasets are not suitable to validate our method, we build a new polygonal point set tracking dataset and demonstrate the superior performance of our method over the baselines and existing contour-based VOS methods. In addition, we present visual-effects applications of our method on part distortion and text mapping.

关键词： Learning systems computer vision Target tracking Image analysis Object segmentation Visual effects Distortion

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