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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22905 条记录，以下是4921-4930 订阅

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SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in vision-Language Models with Counterfactual Examples

SocialCounterfactuals: Probing and Mitigating Intersectional...

引用

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

作者： Howard, Phillip Madasu, Avinash Le, Tiep Moreno, Gustavo Lujan Bhiwandiwalla, Anahita Lal, Vasudev Intel Labs Santa Clara CA 95052 USA

ISBN: (纸本)9798350353006

While vision-language models (VLMs) have achieved remarkable performance improvements recently, there is growing evidence that these models also posses harmful biases with respect to social attributes such as gender and race. Prior studies have primarily focused on probing such bias attributes individually while ignoring biases associated with intersections between social attributes. This could be due to the difficulty of collecting an exhaustive set of image-text pairs for various combinations of social attributes. To address this challenge, we employ text-to-image diffusion models to produce counterfactual examples for probing intersectional social biases at scale. Our approach utilizes Stable Diffusion with cross attention control to produce sets of counterfactual image-text pairs that are highly similar in their depiction of a subject (e.g., a given occupation) while differing only in their depiction of intersectional social attributes (e.g., race & gender). Through our over-generate-then-filter methodology, we produce SocialCounterfactuals, a high-quality dataset containing 171k image-text pairs for probing intersectional biases related to gender, race, and physical characteristics. We conduct extensive experiments to demonstrate the usefulness of our generated dataset for probing and mitigating intersectional social biases in state-of-the-art VLMs.

关键词： counterfactuals Fairness intersectionality social bias vision-language models

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METAL: Minimum Effort Temporal Activity Localization in Untrimmed Videos

METAL: Minimum Effort Temporal Activity Localization in Untr...

引用

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

作者： Zhang, Da Dai, Xiyang Wang, Yuan-Fang Univ Calif Santa Barbara Santa Barbara CA 93106 USA Microsoft Redmond WA 98052 USA

ISBN: (纸本)9781728171685

Existing Temporal Activity Localization (TAL) methods largely adopt strong supervision for model training which requires (1) vast amounts of untrimmed videos per each activity category and (2) accurate segment-level boundary annotations (start time and end time) for every instance. This poses a critical restriction to the current methods in practical scenarios where not only segment-level annotations are expensive to obtain but many activity categories are also rare and unobserved during training. Therefore, Can we learn a TAL model under weak supervision that can localize unseen activity classes? To address this scenario, we define a novel example-based TAL problem called Minimum Effort Temporal Activity Localization (METAL): Given only a few examples, the goal is to find the occurrences of semantically-related segments in an untrimmed video sequence while model training is only supervised by the video-level annotation. Towards this objective, we propose a novel Similarity Pyramid Network (SPN) that adopts the few-shot learning technique of Relation Network and directly encodes hierarchical multi-scale correlations, which we learn by optimizing two complimentary loss functions in an end-to-end manner. We evaluate the SPN on the THUMOS' 14 and ActivityNet datasets, of which we rearrange the videos to fit the METAL setup. Results show that our SPN achieves performance superior or competitive to state-of-the-art approaches with stronger supervision.

关键词： computer vision

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Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation

Tree Energy Loss: Towards Sparsely Annotated Semantic Segmen...

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

作者： Liang, Zhiyuan Wang, Tiancai Zhang, Xiangyu Sun, Jian Shen, Jianbing Beijing Inst Technol Beijing Peoples R China MEGVII Technol Beijing Peoples R China Univ Macau SKL IOTSC Zhuhai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Sparsely annotated semantic segmentation (SASS) aims to train a segmentation network with coarse-grained (i.e., point-, scribble-, and block-wise) supervisions, where only a small proportion of pixels are labeled in each image. In this paper, we propose a novel tree energy loss for SASS by providing semantic guidance for unlabeled pixels. The tree energy loss represents images as minimum spanning trees to model both low-level and high-level pair-wise affinities. By sequentially applying these affinities to the net-work prediction, soft pseudo labels for unlabeled pixels are generated in a coarse-to-fine manner, achieving dynamic online self-training. The tree energy loss is effective and easy to be incorporated into existing frameworks by combining it with a traditional segmentation loss. Compared with previous SASS methods, our method requires no multi-stage training strategies, alternating optimization procedures, additional supervised data, or time-consuming post-processing while outperforming them in all SASS settings. Code is available at https://***/megvii-research/TreeEnergyLoss.

关键词： Training Energy loss Image segmentation computer vision Shape Computational modeling Semantics

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A Structured Dictionary Perspective on Implicit Neural Representations

A Structured Dictionary Perspective on Implicit Neural Repre...

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

作者： Yuce, Gizem Ortiz-Jimenez, Guillermo Besbinar, Beril Frossard, Pascal Swiss Fed Inst Technol Zurich Switzerland Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a novel unified perspective to theoretically analyse INRs. Leveraging results from harmonic analysis and deep learning theory, we show that most INR families are analogous to structured signal dictionaries whose atoms are integer harmonics of the set of initial mapping frequencies. This structure allows INRs to express signals with an exponentially increasing frequency support using a number of parameters that only grows linearly with depth. We also explore the inductive bias of INRs exploiting recent results about the empirical neural tangent kernel (NTK). Specifically, we show that the eigenfunctions of the NTK can be seen as dictionary atoms whose inner product with the target signal determines the final performance of their reconstruction. In this regard, we reveal that meta-learning has a reshaping effect on the NTK analogous to dictionary learning, building dictionary atoms as a combination of the examples seen during meta-training. Our results permit to design and tune novel INR architectures, but can also be of interest for the wider deep learning theory community.

关键词： Deep learning computer vision Dictionaries Data visualization Power system harmonics Harmonic analysis Eigenvalues and eigenfunctions

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Single Image Reflection Removal with Absorption Effect

Single Image Reflection Removal with Absorption Effect

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

作者： Zheng, Qian Shi, Boxin Chen, Jinnan Jiang, Xudong Duan, Ling-Yu Kot, Alex C. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Peking Univ Dept Comp Sci & Technol NELVT Beijing Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

In this paper, we consider the absorption effect for the problem of single image reflection removal. We show that the absorption effect can be numerically approximated by the average of refractive amplitude coefficient map. We then reformulate the image formation model and propose a two-step solution that explicitly takes the absorption effect into account. The first step estimates the absorption effect from a reflection-contaminated image, while the second step recovers the transmission image by taking a reflection-contaminated image and the estimated absorption effect as the input. Experimental results on four public datasets show that our two-step solution not only successfully removes reflection artifact, but also faithfully restores the intensity distortion caused by the absorption effect. Our ablation studies further demonstrate that our method achieves superior performance on the recovery of overall intensity and has good model generalization capacity. The code is available at https://***/q-zh/absorption.

关键词： computer vision Absorption Lighting Estimation Distortion Reflection Data models

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Spatial weighting for bag-of-features

Spatial weighting for bag-of-features

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Marszalek, Marcin Schmid, Cordelia INRIA Rhône-Alpes LEAR - GRAVIR 665 av de l'Europe 38330 Montbonnot France

ISBN: (纸本)0769525970

This paper presents an extension to category classification with bag-of-features, which represents an image as an orderless distribution of features. We propose a method to exploit spatial relations between features by utilizing object boundaries provided during supervised training. We boost the weights of features that agree on the position and shape of the object and suppress the weights of background features, hence the name of our method - "spatial weighting". The proposed representation is thus richer and more robust to background clutter. Experimental results show that our approach improves the results of one of the best current image classification techniques. Furthermore, we propose to apply the spatial model to object localization. Initial results are promising. © 2006 ieee.

关键词： pattern recognition systems

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Category-Level Articulated Object Pose Estimation

Category-Level Articulated Object Pose Estimation

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

作者： Li, Xiaolong Wang, He Yi, Li Guibas, Leonidas Abbott, A. Lynn Song, Shuran Virginia Tech Blacksburg VA 24061 USA Stanford Univ Stanford CA 94305 USA Google Res Cambridge MA USA Columbia Univ New York NY 10027 USA

ISBN: (纸本)9781728171685

This paper addresses the task of category-level pose estimation for articulated objects from a single depth image. We present a novel category-level approach that correctly accommodates object instances previously unseen during training. We introduce Articulation-aware Normalized Coordinate Space Hierarchy (ANCSH) - a canonical representation for different articulated objects in a given category. As the key to achieve intra-category generalization, the representation constructs a canonical object space as well as a set of canonical part spaces. The canonical object space normalizes the object orientation, scales and articulations (e.g. joint parameters and states) while each canonical part space further normalizes its part pose and scale. We develop a deep network based on PointNet++ that predicts ANCSH from a single depth point cloud, including part segmentation, normalized coordinates, and joint parameters in the canonical object space. By leveraging the canonicalized joints, we demonstrate: 1) improved performance in part pose and scale estimations using the induced kinematic constraints from joints;2) high accuracy for joint parameter estimation in camera space.

关键词： computer vision

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Hands by hand: Crowd-sourced motion tracking for gesture annotation

Hands by hand: Crowd-sourced motion tracking for gesture ann...

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2010 ieee computer society conference on computer vision and pattern recognition - Workshops, CVPRW 2010

作者： Spiro, Ian Taylor, Graham Williams, George Bregler, Christoph Department of Computer Science Courant Institute New York University United States

ISBN: (纸本)9781424470297

We describe a method for using crowd-sourced labor to track motion and ultimately annotate gestures of humans in video. Our chosen platform for deployment, Amazon Mechanical Turk, divides labor into HITs (Human Intelligence Tasks). Given the informational density of video, our task is potentially larger than a traditional HIT that involves processing a block of text or a single image. We exploit redundancies in video data in such a way that workers' efforts can be multiplied in effect. In the end, a fraction of frames need to be annotated by hand, but we can still achieve complete coverage of all video frames. This is achieved with a combination of HITs using a novel user interface, combined with automatic techniques such as template tracking and affinity propagation clustering. We show in a case study how we can annotate a video database of political speeches with 2D positions and 3D hand pose configurations. This data is then used for some preliminary analytical tasks. © 2010 ieee.

关键词： User interfaces

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Learning to Track Instances without Video Annotations

Learning to Track Instances without Video Annotations

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

作者： Fu, Yang Liu, Sifei Iqbal, Umar De Mello, Shalini Shi, Humphrey Kautz, Jan Univ Illinois Urbana IL 61801 USA NVIDIA Santa Clara CA USA Univ Oregon Eugene OR 97403 USA

ISBN: (纸本)9781665445092

Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation, and 2) the complexity of two-stage approaches. To resolve these challenges, we introduce a novel semi-supervised framework by learning instance tracking networks with only a labeled image dataset and unlabeled video sequences. With an instance contrastive objective, we learn an embedding to discriminate each instance from the others. We show that even when only trained with images, the learned feature representation is robust to instance appearance variations, and is thus able to track objects steadily across frames. We further enhance the tracking capability of the embedding by learning correspondence from unlabeled videos in a self-supervised manner. In addition, we integrate this module into single-stage instance segmentation and pose estimation frameworks, which significantly reduce the computational complexity of tracking compared to two-stage networks. We conduct experiments on the YouTube-VIS and PoseTrack datasets. Without any video annotation efforts, our proposed method can achieve comparable or even better performance than most fully-supervised methods(1).

关键词： Image segmentation computer vision Image resolution Video sequences Pose estimation Image annotation Entropy

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Learning to Detect Scene Landmarks for Camera Localization

Learning to Detect Scene Landmarks for Camera Localization

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

作者： Do, Tien Miksik, Ondrej DeGol, Joseph Park, Hyun Soo Sinha, Sudipta N. Univ Minnesota Minneapolis MN 55455 USA Microsoft Redmond WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Modern camera localization methods that use image retrieval, feature matching, and 3D structure-based pose estimation require long-term storage of numerous scene images or a vast amount of image features. This can make them unsuitable for resource constrained VR/AR devices and also raises serious privacy concerns. We present a new learned camera localization technique that eliminates the need to store features or a detailed 3D point cloud. Our key idea is to implicitly encode the appearance of a sparse yet salient set of 3D scene points into a convolutional neural network (CNN) that can detect these scene points in query images whenever they are visible. We refer to these points as scene landmarks. We also show that a CNN can be trained to regress bearing vectors for such landmarks even when they are not within the camera's field-of-view. We demonstrate that the predicted landmarks yield accurate pose estimates and that our method outperforms DSAC*, the state-of-the-art in learned localization. Furthermore, extending HLoc (an accurate method) by combining its correspondences with our predictions boosts its accuracy even further.

关键词： Location awareness Point cloud compression Privacy computer vision Three-dimensional displays Pose estimation Image retrieval

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