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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

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A sampling-based approach for efficient clustering in large datasets

A sampling-based approach for efficient clustering in large ...

引用

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

作者： Exarchakis, Georgios Oubari, Omar Lenz, Gregor IHU Strasbourg Strasbourg France Univ Strasbourg ICube CNRS Strasbourg France Sorbonne Univ CNRS INSERM Paris France Inst Vis 17 Rue Moreau F-75012 Paris France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters. Our algorithm achieves high-performance by evaluating distances of datapoints with a subset of the cluster centres. Our contribution is substantially more efficient than k-means as it does not require an all to all comparison of data points and clusters. We show that the optimal solutions of our approximation are the same as in the exact solution. However, our approach is considerably more efficient at extracting these clusters compared to the state-of-the-art. We compare our approximation with the exact k-means and alternative approximation approaches on a series of standardised clustering tasks. For the evaluation, we consider the algorithmic complexity, including number of operations to convergence, and the stability of the results. An efficient implementation of the algorithm is available online.

关键词： computer vision Clustering methods Clustering algorithms Approximation algorithms Complexity theory Task analysis Convergence

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The Two Dimensions of Worst-case Training and Their Integrated Effect for Out-of-domain Generalization

The Two Dimensions of Worst-case Training and Their Integrat...

引用

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

作者： Huang, Zeyi Wang, Haohan Huang, Dong Lee, Yong Jae Xing, Eric P. Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Wisconsin Madison WI 53706 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e.g., generalization across distributions) is valued. Existing literature discussing this "hard-to-learn" concept are mainly expanded either along the dimension of the samples or the dimension of the features. In this paper, we aim to introduce a simple view merging these two dimensions, leading to a new, simple yet effective, heuristic to train machine learning models by emphasizing the worst-cases on both the sample and the feature dimensions. We name our method W2D following the concept of "Worst-case along Two Dimensions". We validate the idea and demonstrate its empirical strength over standard benchmarks.

关键词： Training Representation learning computer vision Correlation Merging Force Robustness

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Robust Contrastive Learning against Noisy Views

Robust Contrastive Learning against Noisy Views

引用

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

作者： Chuang, Ching-Yao Hjelm, R. Devon Wang, Xin Vineet, Vibhav Joshi, Neel Torralba, Antonio Jegelka, Stefanie Song, Yale MIT CSAIL Cambridge MA 02139 USA Microsoft Res Redmond WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Contrastive learning relies on an assumption that positive pairs contain related views that share certain underlying information about an instance, e.g., patches of an image or co-occurring multimodal signals of a video. What if this assumption is violated? The literature suggests that contrastive learning produces suboptimal representations in the presence of noisy views, e.g., false positive pairs with no apparent shared information. In this work, we propose a new contrastive loss function that is robust against noisy views. We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure. The proposed loss is completely modality-agnostic and a simple drop-in replacement for the InfoNCE loss, which makes it easy to apply to existing contrastive frameworks. We show that our approach provides consistent improvements over the state-of-the-art on image, video, and graph contrastive learning benchmarks that exhibit a variety of real-world noise patterns.

关键词： Representation learning computer vision Loss measurement Robustness pattern recognition Noise measurement Mutual information

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Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering

Unsupervised Action Segmentation by Joint Representation Lea...

引用

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

作者： Kumar, Sateesh Haresh, Sanjay Ahmed, Awais Konin, Andrey Zia, M. Zeeshan Tran, Quoc-Huy Retrocausal Inc Seattle WA 98052 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a novel approach for unsupervised activity segmentation which uses video frame clustering as a pretext task and simultaneously performs representation learning and online clustering. This is in contrast with prior works where representation learning and clustering are often performed sequentially. We leverage temporal information in videos by employing temporal optimal transport. In particular;we incorporate a temporal regularization term which preserves the temporal order of the activity into the standard optimal transport module for computing pseudo-label cluster assignments. The temporal optimal transport module enables our approach to learn effective representations for unsupervised activity segmentation. Furthermore, previous methods require storing learned features for the entire dataset before clustering them in an offline manner;whereas our approach processes one mini-batch at a time in an online manner. Extensive evaluations on three public datasets, i.e. 50-Salads, Younibe Instructions, and Breakfast, and our dataset, i.e., Desktop Assembly, show that our approach performs on par with or better than previous methods, despite having significantly less memory constraints.

关键词： Representation learning Visualization computer vision Video on demand Memory management pattern recognition Task analysis

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Investigating Top-k White-Box and Transferable Black-box Attack

Investigating Top-k White-Box and Transferable Black-box Att...

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

作者： Zhang, Chaoning Benz, Philipp Karjauv, Adil Cho, Jae Won Zhang, Kang Kweon, In So Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing works have identified the limitation of top-1 attack success rate (ASR) as a metric to evaluate the attack strength but exclusively investigated it in the white-box setting, while our work extends it to a more practical black-box setting: transferable attack. It is widely reported that stronger I-FGSM transfers worse than simple FGSM, leading to a popular belief that transferability is at odds with the white-box attack strength. Our work challenges this belief with empirical finding that stronger attack actually transfers better for the general top-k ASR indicated by the interest class rank (ICR) after attack. For increasing the attack strength, with an intuitive analysis on the logit gradient from the geometric perspective, we identify that the weakness of the commonly used losses lie in prioritizing the speed to fool the network instead of maximizing its strength. To this end, we propose a new normalized CE loss that guides the logit to be updated in the direction of implicitly maximizing its rank distance from the ground-truth class. Extensive results in various settings have verified that our proposed new loss is simple yet effective for top-k attack. Code is available at: https://***/3uCiomP

关键词： Measurement computer vision Codes Semantics pattern recognition

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RCL: Recurrent Continuous Localization for Temporal Action Detection

RCL: Recurrent Continuous Localization for Temporal Action D...

引用

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

作者： Wang, Qiang Zhang, Yanhao Zheng, Yun Pan, Pan Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Temporal representation is the cornerstone of modern action detection techniques. State-of-the-art methods mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the temporal domain with a discretized grid, and then regress the accurate boundaries. In this paper, we revisit this foundational stage and introduce Recurrent Continuous Localization (RCL), which learns a fully continuous anchoring representation. Specifically, the proposed representation builds upon an explicit model conditioned with video embeddings and temporal coordinates, which ensure the capability of detecting segments with arbitrary length. To optimize the continuous representation, we develop an effective scale-invariant sampling strategy and recurrently refine the prediction in subsequent iterations. Our continuous anchoring scheme is fully differentiable, allowing to be seamlessly integrated into existing detectors, e.g., BMN [20] and G-TAD [41]. Extensive experiments on two benchmarks demonstrate that our continuous representation steadily surpasses other discretized counterparts by similar to 2% mAP. As a result, RCL achieves 52.92% mAP@0.5 on THUMOS14 and 37.65% mAP on ActivtiyNet v1.3, outperforming all existing single-model detectors.

关键词： Location awareness computer vision Detectors Performance gain Benchmark testing pattern recognition

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Large-scale Video Panoptic Segmentation in the Wild: A Benchmark

Large-scale Video Panoptic Segmentation in the Wild: A Bench...

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

作者： Miao, Jiaxu Wang, Xiaohan Wu, Yu Li, Wei Zhang, Xu Wei, Yunchao Yang, Yi Zhejiang Univ CCAI Hangzhou Peoples R China Baidu Res Beijing Peoples R China Beijing Jiaotong Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we present a new large-scale dataset for the video panoptic segmentation task, which aims to assign semantic classes and track identities to all pixels in a video. As the ground truth for this task is difficult to annotate, previous datasets for video panoptic segmentation are limited by either small scales or the number of scenes. In contrast, our large-scale Video Panoptic Segmentation in the Wild (VIPSeg) dataset provides 3,536 videos and 84,750 frames with pixel-level panoptic annotations, covering a wide range of real-world scenarios and categories. To the best of our knowledge, our VIPSeg is the first attempt to tackle the challenging video panoptic segmentation task in the wild by considering diverse scenarios. Based on VIPSeg, we evaluate existing video panoptic segmentation approaches and propose an efficient and effective clip-based baseline method to analyze our VIPSeg dataset. Our dataset is available at https://***/VIPSeg-Dataset/VIPSeg-Dataset/.

关键词： computer vision Annotations Shape Semantics Benchmark testing pattern recognition Task analysis

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MonoScene: Monocular 3D Semantic Scene Completion

MonoScene: Monocular 3D Semantic Scene Completion

引用

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

作者： Anh-Quan Cao de Charette, Raoul INRIA Paris France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense geometry and semantics of a scene are inferred from a single monocular RGB image. Different from the SSC literature, relying on 2.5 or 3D input, we solve the complex problem of 2D to 3D scene reconstruction while jointly inferring its semantics. Our framework relies on successive 2D and 3D UNets, bridged by a novel 2D-3D features projection inspired by optics, and introduces a 3D context relation prior to enforce spatio-semantic consistency. Along with architectural contributions, we introduce novel global scene and local frustums losses. Experiments show we outperform the literature on all metries and datasets while hallucinating plausible scenery even beyond the camera field of view. Our code and trained models are available at https://***/cv-rits/MonoScene.

关键词： Optical losses Geometry computer vision Three-dimensional displays Codes Computational modeling Semantics

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Class-Incremental Learning with Strong Pre-trained Models

Class-Incremental Learning with Strong Pre-trained Models

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

作者： Wu, Tz-Ying Swaminathan, Gurumurthy Li, Zhizhong Ravichandran, Avinash Vasconcelos, Nuno Bhotika, Rahul Soatto, Stefano AWS AI Labs Seattle WA 98101 USA Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world setting of CIL that starts with a strong model pre-trained on a large number of base classes. We hypothesize that a strong base model can provide a good representation for novel classes and incremental learning can be done with small adaptations. We propose a 2-stage training scheme, i) feature augmentation - cloning part of the backbone and fine-tuning it on the novel data, and ii) fusion - combining the base and novel classifiers into a unified classifier. Experiments show that the proposed method significantly outperforms state-of-the-art CIL methods on the large-scale ImageNet dataset (e.g. +10% overall accuracy than the best). We also propose and analyze understudied practical CIL scenarios, such as base-novel overlap with distribution shift. Our proposed method is robust and generalizes to all analyzed CIL settings.

关键词： Training Adaptation models computer vision Computational modeling Cloning pattern recognition

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LiT : Zero-Shot Transfer with Locked-image text Tuning

LiT : Zero-Shot Transfer with Locked-image text Tuning

引用

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

作者： Zhai, Xiaohua Wang, Xiao Mustafa, Basil Steiner, Andreas Keysers, Daniel Kolesnikov, Alexander Beyer, Lucas Google Res Brain Team Zurich Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training. In our empirical study we find that locked pre-trained image models with unlocked text models work best. We call this instance of contrastive-tuning "Locked-image Tuning" (LiT), which just teaches a text model to read out good representations from a pre-trained image model for new tasks. A LiT model gains the capability of zero-shot transfer to new vision tasks, such as image classification or retrieval. The proposed LiT is widely applicable;it works reliably with multiple pre-training methods (supervised and unsupervised) and across diverse architectures (ResNet, vision Transformers and MLP-Mixer) using three different imagetext datasets. With the transformer-based pre-trained ViTg/14 model, the LiT model achieves 84.5% zero-shot transfer accuracy on the ImageNet test set, and 81.1% on the challenging out-of-distribution ObjectNet test set.

关键词： Training Representation learning Adaptation models Computational modeling Transformers Data models pattern recognition

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