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检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021"

共 11423 条记录，以下是161-170 订阅

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Towards Robust Classification Model by Counterfactual and Invariant Data Generation

Towards Robust Classification Model by Counterfactual and In...

引用

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

作者： Chang, Chun-Hao Adam, George Alexandru Goldenberg, Anna Univ Toronto Hosp Sick Children Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlations to make predictions. Spuriousness occurs when some features correlate with labels but are not causal;relying on such features prevents models from generalizing to unseen environments where such correlations break. In this work, we focus on image classification and propose two data generation processes to reduce spuriousness. Given human annotations of the subset of the features responsible (causal) for the labels (e.g. bounding boxes), we modify this causal set to generate a surrogate image that no longer has the same label (i.e. a counterfactual image). We also alter non-causal features to generate images still recognized as the original labels, which helps to learn a model invariant to these features. In several challenging datasets, our data generations outperform state-of-the-art methods in accuracy when spurious correlations break, and increase the saliency focus on causal features providing better explanations.

关键词： Industries computer vision Correlation Image recognition Annotations Computational modeling Machine learning

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Learning Asynchronous and Sparse Human-Object Interaction in Videos

Learning Asynchronous and Sparse Human-Object Interaction in...

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

作者： Morais, Romero Vuong Le Venkatesh, Svetha Truyen Tran Deakin Univ Appl Artificial Intelligence Inst Geelong Vic Australia

ISBN: (纸本)9781665445092

Human activities can be learned from video. With effective modeling it is possible to discover not only the action labels but also the temporal structure of the activities, such as the progression of the sub-activities. Automatically recognizing such structure from raw video signal is a new capability that promises authentic modeling and successful recognition of human-object interactions. Toward this goal, we introduce Asynchronous-Sparse Interaction Graph Networks (ASSIGN), a recurrent graph network that is able to automatically detect the structure of interaction events associated with entities in a video scene. ASSIGN pioneers learning of autonomous behavior of video entities including their dynamic structure and their interaction with the coexisting neighbors. Entities' lives in our model are asynchronous to those of others therefore more flexible in adapting to complex scenarios. Their interactions are sparse in time hence more faithful to the true underlying nature and more robust in inference and learning. ASSIGN is tested on humanobject interaction recognition and shows superior performance in segmenting and labeling of human sub-activities and object affordances from raw videos. The native ability of ASSIGN in discovering temporal structure also eliminates the dependence on external segmentation that was previously mandatory for this task.

关键词： computer vision Analytical models Adaptation models Computational modeling Affordances Time series analysis pattern recognition

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Dynamic Head: Unifying Object Detection Heads with Attentions

Dynamic Head: Unifying Object Detection Heads with Attention...

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

作者： Dai, Xiyang Chen, Yinpeng Xiao, Bin Chen, Dongdong Liu, Mengchen Yuan, Lu Zhang, Lei Microsoft Redmond WA 98052 USA

ISBN: (纸本)9781665445092

The complex nature of combining localization and classification in object detection has resulted in the flourished development of methods. Previous works tried to improve the performance in various object detection heads but failed to present a unified view. In this paper, we present a novel dynamic head framework to unify object detection heads with attentions. By coherently combining multiple self-attention mechanisms between feature levels for scale-awareness, among spatial locations for spatial-awareness, and within output channels for task-awareness, the proposed approach significantly improves the representation ability of object detection heads without any computational overhead. Further experiments demonstrate that the effectiveness and efficiency of the proposed dynamic head on the COCO benchmark. With a standard ResNeXt-101-DCN backbone, we largely improve the performance over popular object detectors and achieve a new state-of-the-art at 54.0 AP. The code will be released at http://***/microsoft/DynamicHead.

关键词： Location awareness computer vision Codes Computational modeling Object detection Detectors Feature extraction

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Over-the-Air Adversarial Flickering Attacks against Video recognition Networks

Over-the-Air Adversarial Flickering Attacks against Video Re...

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

作者： Pony, Roi Naeh, Itay Mannor, Shie Technion Israel Inst Technol Dept Elect Engn Haifa Israel Rafael Adv Def Syst Ltd Haifa Israel Nvidia Res Shanghai Peoples R China

ISBN: (纸本)9781665445092

Deep neural networks for video classification, just like image classification networks, may be subjected to adversarial manipulation. The main difference between image classifiers and video classifiers is that the latter usually use temporal information contained within the video. In this work we present a manipulation scheme for fooling video classifiers by introducing a flickering temporal perturbation that in some cases may be unnoticeable by human observers and is implementable in the real world. After demonstrating the manipulation of action classification of single videos, we generalize the procedure to make universal adversarial perturbation, achieving high fooling ratio. In addition, we generalize the universal perturbation and produce a temporal-invariant perturbation, which can be applied to the video without synchronizing the perturbation to the input. The attack was implemented on several target models and the transferability of the attack was demonstrated. These properties allow us to bridge the gap between simulated environment and real-world application, as will be demonstrated in this paper for the first time for an over-the-air flickering attack.

关键词： Deep learning computer vision Perturbation methods Observers pattern recognition Image classification

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StruMonoNet: Structure-Aware Monocular 3D Prediction

StruMonoNet: Structure-Aware Monocular 3D Prediction

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

作者： Yang, Zhenpei Li, Li Erran Huang, Qixing Univ Texas Austin Austin TX 78712 USA Columbia Univ New York NY 10027 USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Monocular 3D prediction is one of the fundamental problems in 3D vision. Recent deep learning-based approaches have brought us exciting progress on this problem. However, existing approaches have predominantly focused on end-to-end depth and normal predictions, which do not filly utilize the underlying 3D environment's geometric structures. This paper introduces StruMonoNet, which detects and enforces a planar structure to enhance pixel-wise predictions. StruMonoNet innovates in leveraging a hybrid representation that combines visual feature and a surfel representation for plane prediction. This formulation allows us to combine the power of visual feature learning and the flexibility of geometric representations in incorporating geometric relations. As a result, StruMonoNet can detect relations between planes such as adjacent planes, perpendicular planes, and parallel planes, all of which are beneficial for dense 3D prediction. Experimental results show that StruMonoNet considerably outperforms state-of-the-art approaches on NYUv2 and ScanNet.

关键词： Visualization computer vision Three-dimensional displays Boosting pattern recognition

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Understanding Failures of Deep Networks via Robust Feature Extraction

Understanding Failures of Deep Networks via Robust Feature E...

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

作者： Singla, Sahil Nushi, Besmira Shah, Shital Kamar, Ece Horvitz, Eric Univ Maryland College Pk MD 20742 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665445092

Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over features and instances. We introduce and study a method aimed at characterizing and explaining failures by identifying visual attributes whose presence or absence results in poor performance. In distinction to previous work that relies upon crowdsourced labels for visual attributes, we leverage the representation of a separate robust model to extract interpretable features and then harness these features to identify failure modes. We further propose a visualization method aimed at enabling humans to understand the meaning encoded in such features and we test the comprehensibility of the features. An evaluation of the methods on the ImageNet dataset demonstrates that: (i) the proposed workflow is effective for discovering important failure modes, (ii) the visualization techniques help humans to understand the extracted features, and (iii) the extracted insights can assist engineers with error analysis and debugging.

关键词： Measurement Visualization computer vision Error analysis Aggregates Debugging Feature extraction

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IMAGINE: Image Synthesis by Image-Guided Model Inversion

IMAGINE: Image Synthesis by Image-Guided Model Inversion

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

作者： Wang, Pei Li, Yijun Singh, Krishna Kumar Lu, Jingwan Vasconcelos, Nuno UC San Diego CA 92093 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample. We leverage the knowledge of image semantics from a pre-trained classifier to achieve plausible generations via matching multi-level feature representations in the classifier, associated with adversarial training with an external discriminator. IMAGINE enables the synthesis procedure to simultaneously 1) enforce semantic specificity constraints during the synthesis, 2) produce realistic images without generator training, and 3) give users intuitive control over the generation process. With extensive experimental results, we demonstrate qualitatively and quantitatively that IMAGINE performs favorably against state-of-the-art GAN-based and inversion-based methods, across three different image domains (i.e., objects, scenes, and textures).

关键词： Training computer vision Image synthesis Computational modeling Semantics Process control Generators

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Repetitive Activity Counting by Sight and Sound

Repetitive Activity Counting by Sight and Sound

引用

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

作者： Zhang, Yunhua Shao, Ling Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

This paper strives for repetitive activity counting in videos. Different from existing works, which all analyze the visual video content only, we incorporate for the first time the corresponding sound into the repetition counting process. This benefits accuracy in challenging vision conditions such as occlusion, dramatic camera view changes, low resolution, etc. We propose a model that starts with analyzing the sight and sound streams separately. Then an audiovisual temporal stride decision module and a reliability estimation module are introduced to exploit cross-modal temporal interaction. For learning and evaluation, an existing dataset is repurposed and reorganized to allow for repetition counting with sight and sound. We also introduce a variant of this dataset for repetition counting under challenging vision conditions. Experiments demonstrate the benefit of sound, as well as the other introduced modules, for repetition counting. Our sight-only model already outperforms the state-of-the-art by itself, when we add sound, results improve notably, especially under harsh vision conditions. The code and datasets are available at https://***/xiaobai1217/RepetitionCounting.

关键词： Visualization computer vision Analytical models Codes Estimation Cameras pattern recognition

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Shelf-Supervised Mesh Prediction in the Wild

Shelf-Supervised Mesh Prediction in the Wild

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

作者： Ye, Yufei Tulsiani, Shubham Gupta, Abhinav Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook AI Res Pittsburgh PA USA

ISBN: (纸本)9781665445092

We aim to infer 3D shape and pose of object from a single image and propose a learning-based approach that can train from unstructured image collections, supervised by only segmentation outputs from off-the-shelf recognition systems (i.e. `shelf-supervised'). We first infer a volumetric representation in a canonical frame, along with the camera pose. We enforce the representation geometrically consistent with both appearance and masks, and also that the synthesized novel views are indistinguishable from image collections. The coarse volumetric prediction is then converted to a mesh-based representation, which is further refined in the predicted camera frame. These two steps allow both shape-pose factorization from image collections and per-instance reconstruction in finer details. We examine the method on both synthetic and the real-world datasets and demonstrate its scalability on 50 categories in the wild, an order of magnitude more classes than existing works.

关键词： Visualization Three-dimensional displays Shape Scalability Lighting Predictive models Rendering (computer graphics)

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Audio-Visual Instance Discrimination with Cross-Modal Agreement

Audio-Visual Instance Discrimination with Cross-Modal Agreem...

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

作者： Morgado, Pedro Vasconcelos, Nuno Misra, Ishan Univ Calif San Diego La Jolla CA 92093 USA Facebook AI Res New York NY USA

ISBN: (纸本)9781665445092

We present a self-supervised learning approach to learn audio-visual representations from video and audio. Our method uses contrastive learning for cross-modal discrimination of video from audio and vice-versa. We show that optimizing for cross-modal discrimination, rather than withinmodal discrimination, is important to learn good representations from video and audio. With this simple but powerful insight, our method achieves highly competitive performance when finetuned on action recognition tasks. Furthermore, while recent work in contrastive learning defines positive and negative samples as individual instances, we generalize this definition by exploring cross-modal agreement. We group together multiple instances as positives by measuring their similarity in both the video and audio feature spaces. Cross-modal agreement creates better positive and negative sets, which allows us to calibrate visual similarities by seeking within-modal discrimination of positive instances, and achieve significant gains on downstream tasks.

关键词： Visualization computer vision Extraterrestrial measurements pattern recognition Task analysis

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