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Enabling Equivariance for Arbitrary Lie Groups

Enabling Equivariance for Arbitrary Lie Groups

引用

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

作者： MacDonald, Lachlan E. Ramasinghe, Sameera Lucey, Simon Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Although provably robust to translational perturbations, convolutional neural networks (CNNs) are known to suffer from extreme performance degradation when presented at test time with more general geometric transformations of inputs. Recently, this limitation has motivated a shift in focus from CNNs to Capsule Networks (CapsNets). However, CapsNets suffer from admitting relatively few theoretical guarantees of invariance. We introduce a rigourous mathematical framework to permit invariance to any Lie group of warps, exclusively using convolutions (over Lie groups), without the need for capsules. Previous work on group convolutions has been hampered by strong assumptions about the group, which precludes the application of such techniques to common warps in computer vision such as affine and homographic. Our framework enables the implementation of group convolutions over any finite-dimensional Lie group. We empirically validate our approach on the benchmark affine-invariant classification task, where we achieve similar to 30% improvement in accuracy against conventional CNNs while outperforming most CapsNets. As further illustration of the generality of our framework, we train a homography-convolutional model which achieves superior robustness on a homography-perturbed dataset, where CapsNet results degrade.

关键词： Degradation computer vision Perturbation methods Benchmark testing Mathematical models Robustness pattern recognition

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Generalizable Human Pose Triangulation

Generalizable Human Pose Triangulation

引用

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

作者： Bartol, Kristijan Bojanic, David Petkovic, Tomislav Pribanic, Tomislav Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We address the problem of generalizability for multi-view 3D human pose estimation. The standard approach is to first detect 2D keypoints in images and then apply triangulation from multiple views. Even though the existing methods achieve remarkably accurate 3D pose estimation on public benchmarks, most of them are limited to a single spatial camera arrangement and their number. Several methods address this limitation but demonstrate significantly degraded performance on novel views. We propose a stochastic framework for human pose triangulation and demonstrate a superior generalization across different camera arrangements on two public datasets. In addition, we apply the same approach to the fundamental matrix estimation problem, showing that the proposed method can successfully apply to other computer vision problems. The stochastic framework achieves more than 8.8% improvement on the 3D pose estimation task, compared to the state-of-the-art, and more than 30% improvement for fundamnental matrix estimation, compared to a standard algorithm.

关键词： Training computer vision Three-dimensional displays Computational modeling Pose estimation Stochastic processes Training data

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SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems

SplitNets: Designing Neural Architectures for Efficient Dist...

引用

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

作者： Dong, Xin De Salvo, Barbara Li, Meng Liu, Chiao Qu, Zhongnan Kung, H. T. Li, Ziyun Harvard Univ Cambridge MA 02138 USA Meta Real Labs Menlo Pk CA USA Swiss Fed Inst Technol Zurich Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to meet system performance targets in inference accuracy and latency under the given hardware resource constraints. To achieve an optimal balance among computation, communication, and performance, a split-aware neural architecture search framework, SplitNets, is introduced to conduct model designing, splitting, and communication reduction simultaneously. We further extend the framework to multi-view systems for learning to fuse inputs from multiple camera sensors with optimal performance and systemic efficiency. We validate SplitNets for single-view system on ImageNet as well as multi-view system on 3D classification, and show that the SplitNets framework achieves state-of-the-art (SOTA) performance and system latency compared with existing approaches.

关键词： Performance evaluation Three-dimensional displays System performance Neural networks computer architecture Sensor fusion Cameras

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Video Swin Transformer

Video Swin Transformer

引用

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

作者： Liu, Ze Ning, Jia Cao, Yue Wei, Yixuan Zhang, Zheng Lin, Stephen Hu, Han Microsoft Res Asia Beijing Peoples R China Univ Sci & Technol China Hefei Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks. These video models are all built on Transformer layers that globally connect patches across the spatial and temporal dimensions. In this paper, we instead advocate an inductive bias of locality in video Transformers, which leads to a better speed-accuracy trade-off compared to previous approaches which compute self-attention globally even with spatial-temporal factorization. The locality of the proposed video architecture is realized by adapting the Swin Transformer designed for the image domain, while continuing to leverage the power of pre-trained image models. Our approach achieves state-of-the-art accuracy on a broad range of video recognition benchmarks, including on action recognition (84.9 top-1 accuracy on Kinetics-400 and 85.9 top-1 accuracy on Kinetics-600 with similar to 20x less pre-training data and similar to 3x smaller model size) and temporal modeling (69.6 top-1 accuracy on Something-Something v2).

关键词： Adaptation models Image recognition Computational modeling computer architecture Benchmark testing Transformers Solids

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Face Relighting with Geometrically Consistent Shadows

Face Relighting with Geometrically Consistent Shadows

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

作者： Hou, Andrew Sarkis, Michel Bi, Ning Tong, Yiying Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA Qualcomm Technol Inc San Diego CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Most face relighting methods are able to handle diffuse shadows, but struggle to handle hard shadows, such as those cast by the nose. Methods that propose techniques for handling hard shadows often do not produce geometrically consistent shadows since they do not directly leverage the estimated face geometry while synthesizing them. We propose a novel differentiable algorithm for synthesizing hard shadows based on ray tracing, which we incorporate into training our face relighting model. Our proposed algorithm directly utilizes the estimated face geometry to synthesize geometrically consistent hard shadows. We demonstrate through quantitative and qualitative experiments on Multi-PIE and FFHQ that our method produces more geometrically consistent shadows than previous face relighting methods while also achieving state-of-the-art face relighting performance under directional lighting. In addition, we demonstrate that our differentiable hard shadow modeling improves the quality of the estimated face geometry over diffuse shading models.

关键词： Geometry Training Shape Computational modeling Face recognition Surveillance Lighting

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Open-Set Text recognition via Character-Context Decoupling

Open-Set Text Recognition via Character-Context Decoupling

引用

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

作者： Liu, Chang Yang, Chun Yin, Xu-Cheng Univ Sci & Technol Sch Comp & Commun Engn Beijing Peoples R China Univ Sci & Technol Inst Artificial Intelligence Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The open-set text recognition task is an emerging challenge that requires an extra capability to cognize novel characters during evaluation. We argue that a major cause of the limited performance for current methods is the confounding effect of contextual information over the visual information of individual characters. Under open-set scenarios, the intractable bias in contextual information can be passed down to visual information, consequently impairing the classification performance. In this paper, a Character-Context Decoupling framework is proposed to alleviate this problem by separating contextual information and character-visual information. Contextual information can be decomposed into temporal information and linguistic information. Here, temporal information that models character order and word length is isolated with a detached temporal attention module. Linguistic information that models n-gram and other linguistic statistics is separated with a decoupled context anchor mechanism. A variety of quantitative and qualitative experiments show that our method achieves promising performance on open-set, zero-shot, and close-set text recognition datasets.

关键词： Visualization computer vision Text analysis Text recognition Linguistics Task analysis Context modeling

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Make It Move: Controllable Image-to-Video Generation with Text Descriptions

Make It Move: Controllable Image-to-Video Generation with Te...

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

作者： Hu, Yaosi Luo, Chong Chen, Zhenzhong Wuhan Univ Wuhan Peoples R China Microsoft Res Asia Beijing Peoples R China MSRA Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Generating controllable videos conforming to user intentions is an appealing yet challenging topic in computer vision. To enable maneuverable control in line with user intentions, a novel video generation task, named Text-Imageto-Video generation (TI2V), is proposed. With both controllable appearance and motion, TI2V aims at generating videos from a static image and a text description. The key challenges of TI2V task lie both in aligning appearance and motion from different modalities, and in handling uncertainty in text descriptions. To address these challenges, we propose a Motion Anchor-based video GEnerator (MAGE) with an innovative motion anchor (MA) structure to store appearance-motion aligned representation. To model the uncertainty and increase the diversity, it further allows the injection of explicit condition and implicit randomness. Through three-dimensional axial transformers, MA is interacted with given image to generate next frames recursively with satisfying controllability and diversity. Accompanying the new task, we build two new video-text paired datasets based on MNIST and CATER for evaluation. Experiments conducted on these datasets verify the effectiveness of MAGE and show appealing potentials of TI2V task.

关键词： computer vision Uncertainty Transformers Controllability Generators pattern recognition Task analysis

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NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models

NICGSlowDown: Evaluating the Efficiency Robustness of Neural...

引用

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

作者： Chen, Simin Song, Zihe Haque, Mirazul Liu, Cong Yang, Wei Univ Texas Dallas 800 W Campbell Rd Richardson TX 75080 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Neural image caption generation (NICG) models have received massive attention from the research community due to their excellent performance in visual understanding. Existing work focuses on improving NICG model accuracy while efficiency is less explored. However, many real-world applications require real-time feedback, which highly relies on the efficiency of NICG models. Recent research observed that the efficiency of NICG models could vary for different inputs. This observation brings in a new attack surface of NICG models, i.e., An adversary might be able to slightly change inputs to cause the NICG models to consume more computational resources. To further understand such efficiency-oriented threats, we propose a new attack approach, NICGSlowDown, to evaluate the efficiency robustness of NICG models. Our experimental results show that NICGSlowDown can generate images with human-unnoticeable perturbations that will increase the NICG model latency up to 483.86%. We hope this research could raise the community's concern about the efficiency robustness of NICG models.

关键词： Visualization computer vision Computational modeling Perturbation methods Robustness Real-time systems pattern recognition

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Online Learning of Reusable Abstract Models for Object Goal Navigation

Online Learning of Reusable Abstract Models for Object Goal ...

引用

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

作者： Campari, Tommaso Lamanna, Leonardo Traverso, Paolo Serafini, Luciano Ballan, Lamberto Univ Padua Padua Italy Fdn Bruno Kessler FBK Trento Italy Univ Brescia Brescia Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we present a novel approach to incrementally learn an Abstract Model of an unknown environment, and show how an agent can reuse the learned model for tackling the Object Goal Navigation task. The Abstract Model is a finite state machine in which each state is an abstraction of a state of the environment, as perceived by the agent in a certain position and orientation. The perceptions are high-dimensional sensory data (e.g., RGB-D images), and the abstraction is reached by exploiting image segmentation and the Taskonomy model bank. The learning of the Abstract Model is accomplished by executing actions, observing the reached state, and updating the Abstract Model with the acquired information. The learned models are memorized by the agent, and they are reused whenever it recognizes to be in an environment that corresponds to the stored model. We investigate the effectiveness of the proposed approach for the Object Goal Navigation task, relying on public benchmarks. Our results show that the reuse of learned Abstract Models can boost performance on Object Goal Navigation.

关键词： Image segmentation computer vision Navigation Computational modeling Machine vision Robot vision systems Benchmark testing

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Cross-modal Representation Learning for Zero-shot Action recognition

Cross-modal Representation Learning for Zero-shot Action Rec...

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

作者： Lin, Chung-Ching Lin, Kevin Wang, Lijuan Liu, Zicheng Li, Linjie Microsoft Redmond WA 98052 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a cross-modal Transformer-based framework, which jointly encodes video data and text labels for zero-shot action recognition (ZSAR). Our model employs a conceptually new pipeline by which visual representations are learned in conjunction with visual-semantic associations in an end-to-end manner. The model design provides a natural mechanism for visual and semantic representations to be learned in a shared knowledge space, whereby it encourages the learned visual embedding to be discriminative and more semantically consistent. In zero-shot inference, we devise a simple semantic transfer scheme that embeds semantic relatedness information between seen and unseen classes to composite unseen visual prototypes. Accordingly, the discriminative features in the visual structure could be preserved and exploited to alleviate the typical zero-shot issues of information loss, semantic gap, and the hubness problem. Under a rigorous zero-shot setting of not pretraining on additional datasets, the experiment results show our model considerably improves upon the state of the arts in ZSAR, reaching encouraging top-I accuracy on UCF101, HMDB51, and ActivityNet benchmark datasets. Code will be made available.(1)

关键词： Training Representation learning Visualization Text recognition Computational modeling Semantics Benchmark testing

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