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DFM: A Performance Baseline for Deep Feature Matching

DFM: A Performance Baseline for Deep Feature Matching

引用

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

作者： Efe, Ufuk Ince, Kutalmis Gokalp Alatan, A. Aydin Middle East Tech Univ Dept Elect & Elect Engn Ctr Image Anal OGAM Ankara Turkey

ISBN: (纸本)9781665448994

A novel image matching method is proposed that utilizes learned features extracted by an off-the-shelf deep neural network to obtain a promising performance. The proposed method uses pre-trained VGG architecture as a feature extractor and does not require any additional training specific to improve matching. Inspired by well-established concepts in the psychology area, such as the Mental Rotation paradigm, an initial warping is performed as a result of a preliminary geometric transformation estimate. These estimates are simply based on dense matching of nearest neighbors at the terminal layer of VGG network outputs of the images to be matched. After this initial alignment, the same approach is repeated again between reference and aligned images in a hierarchical manner to reach a good localization and matching performance. Our algorithm achieves 0.57 and 0.80 overall scores in terms of Mean Matching Accuracy (MMA) for 1 pixel and 2 pixels thresholds respectively on Hpatches dataset [4], which indicates a better performance than the state-of-the-art.

关键词： Deep learning Training Location awareness computer vision Image matching conferences Psychology

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Evaluating Scalable Bayesian Deep Learning Methods for Robust computer vision

Evaluating Scalable Bayesian Deep Learning Methods for Robus...

引用

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

作者： Gustafsson, Fredrik K. Danelljan, Martin Schoen, Thomas B. Uppsala Univ Dept Informat Technol Uppsala Sweden Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781728193601

While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, for example in automotive applications. In Bayesian deep learning, predictive uncertainty is commonly decomposed into the distinct types of aleatoric and epistemic uncertainty. The former can be estimated by letting a neural network output the parameters of a certain probability distribution. Epistemic uncertainty estimation is a more challenging problem, and while different scalable methods recently have emerged, no extensive comparison has been performed in a real-world setting. We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. Our proposed framework is specifically designed to test the robustness required in real-world computer vision applications. We also apply this framework to provide the first properly extensive and conclusive comparison of the two current state-of-the-art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates. Code is available at https://***/fregu856/evaluating_bdl.

关键词： computer vision

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Video Demo: An Egocentric vision based Assistive Co-robot

Video Demo: An Egocentric Vision based Assistive Co-robot

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Jingzhe Zhuang, Lishuo Wang, Yang Zhou, Yameng Meng, Yan Hua, Gang Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA Stevens Inst Technol Dept Comp Sci Hoboken NJ 07030 USA

ISBN: (纸本)9780769549903

We present the video demo of the prototype of an egocentric vision based assistive co-robot system. In this co-robot system, the user is wearing a pair of glasses with a forward looking camera, and is actively engaged in the control loop of the robot in navigational tasks. The egocentric vision glasses serve for two purposes. First, it serves as a source of visual input to request the robot to find a certain object in the environment. Second, the motion patterns computed from the egocentric video associated with a specific set of head movements are exploited to guide the robot to find the object. These are especially helpful for quadriplegic individuals who do not have the needed hand functionality for control with other modalities (e.g., joystick). In our co-robot system, when the robot does not fulfill the object finding task in a pre-specified time window, it would actively solicit user controls for guidance. Then the users can use the egocentric vision based gesture interface to orient the robot towards the direction of the object. After that the robot will automatically navigate towards the object until it finds it. Our experiments validated the efficacy of the closed-loop design to engage the human in the loop.

关键词： closed loop systems gesture recognition handicapped aids human-robot interaction image motion analysis medical robotics mobile robots path planning robot vision video signal processing

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DAMSL: Domain Agnostic Meta Score-based Learning

DAMSL: Domain Agnostic Meta Score-based Learning

引用

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

作者： Cai, John Cai, Bill Mei, Shen Sheng Princeton Univ Princeton NJ 08544 USA MIT Cambridge MA 02139 USA Pensees Pte Ltd Singapore Singapore

ISBN: (纸本)9781665448994

In this paper, we propose Domain Agnostic Meta Score-based Learning (DAMSL), a novel, versatile and highly effective solution that delivers significant out-performance over state-of-the-art methods for cross-domain few-shot learning. We identify key problems in previous meta-learning methods over-fitting to the source domain, and previous transfer-learning methods under-utilizing the structure of the support set. The core idea behind our method is that instead of directly using the scores from a fine-tuned feature encoder, we use these scores to create input coordinates for a domain agnostic metric space. A graph neural network is applied to learn an embedding and relation function over these coordinates to process all information contained in the score distribution of the support set. We test our model on both established CD-FSL benchmarks and new domains and show that our method overcomes the limitations of previous meta-learning and transfer-learning methods to deliver substantial improvements in accuracy across both smaller and larger domain shifts.

关键词： computer vision conferences Computational modeling Performance gain Benchmark testing Extraterrestrial measurements Boosting

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Adversarial Fooling Beyond "Flipping the Label"

Adversarial Fooling Beyond "Flipping the Label"

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

作者： Mopuri, Konda Reddy Shaj, Vaisakh Babu, R. Venkatesh Indian Inst Sci Video Analyt Lab Benguluru India

ISBN: (纸本)9781728193601

Recent advancements in CNNs have shown remarkable achievements in various CV/AI applications. Though CNNs show near human or better than human performance in many critical tasks, they are quite vulnerable to adversarial attacks. These attacks are potentially dangerous in real-life deployments. Though there have been many adversarial attacks proposed in recent years, there is no proper way of quantifying the effectiveness of these attacks. As of today, mere fooling rate is used for measuring the susceptibility of the models, or the effectiveness of adversarial attacks. Fooling rate just considers label flipping and does not consider the cost of such flipping, for instance, in some deployments, flipping between two species of dogs may not be as severe as confusing a dog category with that of a vehicle. Therefore, the metric to quantify the vulnerability of the models should capture the severity of the flipping as well. In this work we first bring out the drawbacks of the existing evaluation and propose novel metrics to capture various aspects of the fooling. Further, for the first time, we present a comprehensive analysis of several important adversarial attacks over a set of distinct CNN architectures. We believe that the presented analysis brings valuable insights about the current adversarial attacks and the CNN models.

关键词： computer vision

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AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data

AuxMix: Semi-Supervised Learning with Unconstrained Unlabele...

引用

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

作者： Banitalebi-Dehkordi, Amin Gujjar, Pratik Zhang, Yong Huawei Technol Canada Co Ltd Vancouver BC Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised learning (SSL) has seen great strides when labeled data is scarce but unlabeled data is abundant. Critically, most recent work assume that such unlabeled data is drawn from the same distribution as the labeled data. In this work, we show that state-of-the-art SSL algorithms suffer a degradation in performance in the presence of unlabeled auxiliary data that does not necessarily possess the same class distribution as the labeled set. We term this problem as Auxiliary-SSL and propose AuxMix, an algorithm that leverages self-supervised learning tasks to learn generic features in order to mask auxiliary data that are not semantically similar to the labeled set. We also propose to regularize learning by maximizing the predicted entropy for dissimilar auxiliary samples. We show an improvement of 5% over existing baselines on a ResNet-50 model when trained on CIFAR10 dataset with 4k labeled samples and all unlabeled data is drawn from the Tiny-Imagenet dataset. We report competitive results on several datasets and conduct ablation studies.

关键词： Degradation computer vision conferences Self-supervised learning Semisupervised learning Prediction algorithms Entropy

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Federated Learning with a Single Shared Image

Federated Learning with a Single Shared Image

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

作者： Soni, Sunny Saeed, Aaqib Asano, Yuki M. Univ Amsterdam Amsterdam Netherlands TU Eindhoven Eindhoven Netherlands

ISBN: (纸本)9798350365474

Federated Learning (FL) enables multiple machines to collaboratively train a machine learning model without sharing of private training data. Yet, especially for heterogeneous models, a key bottleneck remains the transfer of knowledge gained from each client model with the server. One popular method, FedDF, uses distillation to tackle this task with the use of a common, shared dataset on which predictions are exchanged. However, in many contexts such a dataset might be difficult to acquire due to privacy and the clients might not allow for storage of a large shared dataset. To this end, in this paper, we introduce a new method that improves this knowledge distillation method to only rely on a single shared image between clients and server. In particular, we propose a novel adaptive dataset pruning algorithm that selects the most informative crops generated from only a single image. With this, we show that federated learning with distillation under a limited shared dataset budget works better by using a single image compared to multiple individual ones. Finally, we extend our approach to allow for training heterogeneous client architectures by incorporating a non-uniform distillation schedule and client-model mirroring on the server side.

关键词： computer vision Federated Learning Limited Data Representation Learning

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Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis

Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint...

引用

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

作者： Von Braun, Marie-Sophie Frenzel, Patrick Kaeding, Christian Fuchs, Mirco Leipzig Univ Appl Sci Lab Biosignal Proc Leipzig Germany Inst Appl Training Sci Leipzig Leipzig Germany

ISBN: (纸本)9781728193601

Determining a waterline in images recorded in canoe sprint training is an important component for the kinematic parameter analysis to assess an athlete's performance. Here, we propose an approach for the automated waterline detection. First, we utilized a pre-trained Mask R-CNN by means of transfer learning for canoe segmentation. Second, we developed a multi-stage approach to estimate a waterline from the outline of the segments. It consists of two linear regression stages and the systematic selection of canoe parts. We then introduced a parameterization of the waterline as a basis for further evaluations. Next, we conducted a study among several experts to estimate the ground truth waterlines. This not only included an average waterline drawn from the individual experts annotations but, more importantly, a measure for the uncertainty between individual results. Finally, we assessed our method with respect to the question whether the predicted waterlines are in accordance with the experts annotations. Our method demonstrated a high performance and provides opportunities for new applications in the field of automated video analysis in canoe sprint.

关键词： computer vision

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Dynamic Attention-based Visual Odometry

Dynamic Attention-based Visual Odometry

引用

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

作者： Kuo, Xin-Yu Liu, Chien Lin, Kai-Chen Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan

ISBN: (纸本)9781728193601

This paper proposes a dynamic attention-based visual odometry framework (DAVO), a learning-based VO method, for estimating the ego-motion of a monocular camera. DAVO dynamically adjusts the attention weights on different semantic categories for different motion scenarios based on optical flow maps. These weighted semantic categories can then be used to generate attention maps that highlight the relative importance of different semantic regions in input frames for pose estimation. In order to examine the proposed DAVO, we perform a number of experiments on the KITTI Visual Odometry and SLAM benchmark suite to quantitatively and qualitatively inspect the impacts of the dynamically adjusted weights on the accuracy of the evaluated trajectories. Moreover, we design a set of ablation analyses to justify each of our design choices, and validate the effectiveness as well as the advantages of DAVO. Our experiments on the KITTI dataset shows that the proposed DAVO framework does provide satisfactory performance in ego-motion estimation, and is able deliver competitive performance when compared to the contemporary VO methods.

关键词： computer vision

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Multi-Modal Hit Detection and Positional Analysis in Padel Competitions

Multi-Modal Hit Detection and Positional Analysis in Padel C...

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

作者： Decorte, Robbe Pare, Martin Vanhaeverbeke, Jelle Taelman, Joachim Slembrouck, Maarten Verstockt, Steven Univ Ghent imec IDLab Ghent Belgium

ISBN: (纸本)9798350365474

Padel is a rapidly growing racquet sport and has gained popularity globally due to its accessibility and exciting gameplay dynamics. Effective coordination between teammates hinges on maintaining an appropriate distance, allowing for seamless transitions between offensive and defensive maneuvers. A balanced inter-player distance and distance to the net not only facilitates efficient communication but also enhances the team's ability to exploit openings in the opponent's defense while minimizing vulnerabilities. We introduce a new open dataset of padel rallies with annotations for hits and player-ball interactions, a predictive model for detecting hits based on audio signals, a reidentification algorithm for pose tracking, and a framework for calculating inter-player and player-net distances during rallies. Our predictive model achieves an average F1-score of 92% for hit detection, demonstrating robust performance across different match conditions. Furthermore, we develop a system for accurately assigning hits to individual players, achieving an overall accuracy of 83.70% for player-specific assignment and 86.83% for team-based assignment.

关键词： computer vision Deep Learning Player Tracking Sound Event Detection Sports Data Science

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