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S2F2: Single-Stage Flow Forecasting for Future Multiple Trajectories Prediction

S2F2: Single-Stage Flow Forecasting for Future Multiple Traj...

引用

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

作者： Chen, Yu-Wen Yang, Hsuan-Kung Chiu, Chu-Chi Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this work, we present a single-stage framework, named S2F2, for forecasting multiple human trajectories from raw video images by predicting future optical flows. S2F2 differs from the previous two-stage approaches in that it performs detection, Re-ID, and forecasting of multiple pedestrians at the same time. Unlike the prior approaches, the computational burden of S2F2 remains consistent even as the number of pedestrians grows. The experimental results demonstrate that S2F2 is able to outperform two conventional forecasting algorithms and a recent learning-based two-stage model [1], while maintaining its tracking performance on par with the contemporary MOT models.

关键词： computer vision Computational modeling conferences Predictive models Prediction algorithms Trajectory pattern recognition

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Take the Scenic Route: Improving Generalization in vision-and-Language Navigation

Take the Scenic Route: Improving Generalization in Vision-an...

引用

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

作者： Yu, Felix Deng, Zhiwei Narasimhan, Karthik Russakovsky, Olga Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781728193601

In the vision-and-Language Navigation (VLN) task, an agent with egocentric vision navigates to a destination given natural language instructions. The act of manually annotating these instructions is timely and expensive, such that many existing approaches automatically generate additional samples to improve agent performance. However, these approaches still have difficulty generalizing their performance to new environments. In this work, we investigate the popular Room-to-Room (R2R) VLN benchmark and discover that what is important is not only the amount of data you synthesize, but also how you do it. We find that shortest path sampling, which is used by both the R2R benchmark and existing augmentation methods, encode biases in the action space of the agent which we dub as action priors. We then show that these action priors offer one explanation toward the poor generalization of existing works. To mitigate such priors, we propose a path sampling method based on random walks to augment the data. By training with this augmentation strategy, our agent is able to generalize better to unknown environments compared to the baseline, significantly improving model performance in the process.

关键词： computer vision

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Contrastive Learning for Sports Video: Unsupervised Player Classification

Contrastive Learning for Sports Video: Unsupervised Player C...

引用

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

作者： Koshkina, Maria Pidaparthy, Hemanth Elder, James H. York Univ Toronto ON Canada

ISBN: (纸本)9781665448994

We address the problem of unsupervised classification of players in a team sport according to their team affiliation, when jersey colours and design are not known a priori. We adopt a contrastive learning approach in which an embedding network learns to maximize the distance between representations of players on different teams relative to players on the same team, in a purely unsupervised fashion, without any labelled data. We evaluate the approach using a new hockey dataset and find that it outperforms prior unsupervised approaches by a substantial margin, particularly for real-time application when only a small number of frames are available for unsupervised learning before team assignments must be made. Remarkably, we show that our contrastive method achieves 94% accuracy after unsupervised training on only a single frame, with accuracy rising to 97% within 500 frames (17 seconds of game time). We further demonstrate how accurate team classification allows accurate team-conditional heat maps of player positioning to be computed.

关键词： Training Heating systems computer vision Image color analysis conferences Games Real-time systems

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Zero-shot Learning Using Multimodal Descriptions

Zero-shot Learning Using Multimodal Descriptions

引用

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

作者： Mall, Utkarsh Hariharan, Bharath Bala, Kavita Cornell Univ Ithaca NY 14853 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Zero-shot learning (ZSL) tackles the problem of recognizing unseen classes using only semantic descriptions, e.g., attributes. Current zero-shot learning techniques all assume that a single vector of attributes suffices to describe each category. We show that this assumption is incorrect. Many classes in real world problems have multiple modes of appearance: male and female birds vary in appearance, for instance. Domain experts know this and can provide attribute descriptions of the chief modes of appearance for each class. Motivated by this, we propose the task of multi-modal zero-shot learning, where the learner must learn from these multimodal attribute descriptions. We present a technique for addressing this problem of multimodal ZSL that outperforms the unimodal counterpart significantly. We posit that multimodal ZSL is more practical for real-world problems where complex intra-class variation is common.

关键词： computer vision conferences Computational modeling Semantics Manuals Benchmark testing Birds

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recognition of Freely Selected Keypoints on Human Limbs

Recognition of Freely Selected Keypoints on Human Limbs

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

作者： Ludwig, Katja Kienzle, Daniel Lienhart, Rainer Univ Augsburg Machine Learning & Comp Vis Lab Augsburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Nearly all Human Pose Estimation (HPE) datasets consist of a fixed set of keypoints. Standard HPE models trained on such datasets can only detect these keypoints. If more points are desired, they have to be manually annotated and the model needs to be retrained. Our approach leverages the vision Transformer architecture to extend the capability of the model to detect arbitrary keypoints on the limbs of persons. We propose two different approaches to encode the desired keypoints. (1) Each keypoint is defined by its position along the line between the two enclosing keypoints from the fixed set and its relative distance between this line and the edge of the limb. (2) Keypoints are defined as coordinates on a norm pose. Both approaches are based on the TokenPose [12] architecture, while the keypoint tokens that correspond to the fixed keypoints are replaced with our novel module. Experiments show that our approaches achieve similar results to TokenPose on the fixed keypoints and are capable of detecting arbitrary keypoints on the limbs.

关键词： Measurement computer vision Image edge detection conferences Computational modeling Biological system modeling Pose estimation

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Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation

Deep Spherical Manifold Gaussian Kernel for Unsupervised Dom...

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

作者： Zhang, Youshan Davison, Brian D. Lehigh Univ Comp Sci & Engn Bethlehem PA 18015 USA

ISBN: (纸本)9781665448994

Unsupervised Domain adaptation is an effective method in addressing the domain shift issue when transferring knowledge from an existing richly labeled domain to a new domain. Existing manifold-based methods either are based on traditional models or largely rely on Grassmannian manifold via minimizing differences of single covariance matrices of two domains. In addition, existing pseudo-labeling algorithms inadequately consider the quality of pseudo labels in aligning the conditional distribution between two domains. In this work, a deep spherical manifold Gaussian kernel (DSGK) framework is proposed to map the source and target subspaces into a spherical manifold and reduce the discrepancy between them by embedding both extracted features and a Gaussian kernel. To align the conditional distributions, we further develop an easy-to-hard pseudo label refinement process to improve the quality of the pseudo labels and then reduce categorical spherical manifold Gaussian kernel geodesic loss. Extensive experimental results show that DSGK outperforms state-of-the-art methods, especially on challenging cross-domain learning tasks.

关键词： Manifolds Adaptation models computer vision conferences Feature extraction pattern recognition Noise measurement

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Hot-started NAS for Task-specific Embedded Applications

Hot-started NAS for Task-specific Embedded Applications

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

作者： Hendrickx, Lotte Van Ranst, Wiebe Goedeme, Toon Katholieke Univ Leuven EAVISE PSI ESAT Jan Pieter De Nayerlaan 5 B-2860 St Katelijne Waver Belgium

ISBN: (纸本)9781665487399

Neural architecture search (NAS) has proven its worth in discovering new neural networks. Combining the possibility to satisfy multiple objectives in one search, it is especially useful for getting the most out of embedded devices with limited resources. However, research into small and efficient neural networks precedes NAS. We investigate the influence of combining this pre-existing knowledge with NAS techniques, for which we propose to hot-start the NAS search with a human-designed optimal network. Our experiments show that doing so speeds up the NAS process significantly, but the resulting optimal model at the end is only marginally better. Since embedded devices are often used for a specific task, we also explore the impact of using a task-specific dataset in the NAS process. Our experiments demonstrate that for a constrained problem, a smaller network can be found as compared to a general problem.

关键词： Knowledge engineering computer vision conferences Neural networks Size measurement Search problems pattern recognition

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Two-stage Network For Single Image Super-Resolution

Two-stage Network For Single Image Super-Resolution

引用

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

作者： Han, Yuzhuo Du, Xiaobiao Yang, Zhi Dalian Univ Technol Dalian Peoples R China Jilin Univ Zhuhai Coll Zhuhai Peoples R China Dibaocheng Shanghai Med Imaging Technol Co Ltd Shanghai Peoples R China

ISBN: (纸本)9781665448994

The task of single-image super-resolution (SISR) is a highly inverse problem because it is very challenging to reconstruct rich details from blurred images. Most previous super-resolution (SR) methods based on the convolutional neural networks (CNN) tend to design more complex network structures to directly learn the mapping between low-resolution images and high-resolution images. However, this is not the best choice to blindly increase the network depth, because the performance improvement may not increase, but it will increase the computational cost. To solve this problem, we propose an effective method that learns high frequency information in high-resolution images to enhance the image reconstruction. In this work, we propose a two-stage network (TSN) to recover clear SR images. The proposed TSN firstly learns the high frequency information in high-resolution images, then learns how to transform to high-resolution images. A large number of experiments show that our TSN achieves satisfactory performance.

关键词： computer vision Inverse problems conferences Computational modeling Superresolution Transforms pattern recognition

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A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement

A Deeper Look into Aleatoric and Epistemic Uncertainty Disen...

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

作者： Valdenegro-Toro, Matias Mori, Daniel Saromo Univ Groningen Dept AI Bernoulli Inst Groningen Netherlands Pontifical Catholic Univ Peru Artificial Intelligence Res Grp San Miguel Peru

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural networks are ubiquitous in many tasks, but trusting their predictions is an open issue. Uncertainty quantification is required for many applications, and disentangled aleatoric and epistemic uncertainties are best. In this paper, we generalize methods to produce disentangled uncertainties to work with different uncertainty quantification methods, and evaluate their capability to produce disentangled uncertainties. Our results show that: there is an interaction between learning aleatoric and epistemic uncertainty, which is unexpected and violates assumptions on aleatoric uncertainty, some methods like Flipout produce zero epistemic uncertainty, aleatoric uncertainty is unreliable in the out-of-distribution setting, and Ensembles provide overall the best disentangling quality. We also explore the error produced by the number of samples hyper-parameter in the sampling softmax function, recommending N > 100 samples. We expect that our formulation and results help practitioners and researchers choose uncertainty methods and expand the use of disentangled uncertainties, as well as motivate additional research into this topic.

关键词： computer vision Uncertainty conferences Neural networks pattern recognition Task analysis

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CompConv: A Compact Convolution Module for Efficient Feature Learning

CompConv: A Compact Convolution Module for Efficient Feature...

引用

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

作者： Zhang, Chen Xu, Yinghao Shen, Yujun Zhejiang Univ Hangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665448994

Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks but rely on tremendous computational cost. To solve this problem, existing approaches either compress well-trained large-scale models or learn lightweight models with carefully designed network structures. In this work, we make a close study of the convolution operator, which is the basic unit used in CNNs, to reduce its computing load. In particular, we propose a compact convolution module, called CompConv, to facilitate efficient feature learning. With the divide-and-conquer strategy, CompConv is able to save a great many computations as well as parameters to produce a certain dimensional feature map. Furthermore, CompConv discreetly integrates the input features into the outputs to efficiently inherit the input information. More importantly, the novel CompConv is a plug-and-play module that can be directly applied to modern CNN structures to replace the vanilla convolution layers without further effort. Extensive experimental results suggest that CompConv can adequately compress the benchmark CNN structures yet barely sacrifice the performance, surpassing other competitors.

关键词： computer vision Convolution Computational modeling conferences Benchmark testing pattern recognition Computational efficiency

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