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A stroke of genius: Predicting the next move in badminton

A stroke of genius: Predicting the next move in badminton

引用

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

作者： Ibh, Magnus Grasshof, Stella Hansen, Dan Witzner IT Univ Copenhagen Machine Learning Grp Copenhagen Denmark

ISBN: (纸本)9798350365474

This paper presents, RallyTemPose, a transformer encoder-decoder model for predicting future badminton strokes based on previous rally actions. The model uses court position, skeleton poses, and player-specific embeddings to learn stroke and player-specific latent representations in a spatiotemporal encoder module. The representations are then used to condition the subsequent strokes in a decoder module through rally-aware fusion blocks, which provide additional relevant strategic and technical considerations to make more informed predictions. RallyTemPose shows improved forecasting accuracy compared to traditional sequential methods on two real-world badminton datasets. The performance boost can also be attributed to the inclusion of improved stroke embeddings extracted from the latent representation of a pre-trained large-language model subjected to detailed text descriptions of stroke descriptions. In the discussion, the latent representations learned by the encoder module show useful properties regarding player analysis and comparisons. The code can be found at: This https url.

关键词： Action Forecasting computer vision Encoder-Decoder Skeleton-data Sports Application

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How to Calibrate Your Event Camera

How to Calibrate Your Event Camera

引用

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

作者： Muglikar, Manasi Gehrig, Mathias Gehrig, Daniel Scaramuzza, Davide Univ Zurich Dept Informat Zurich Switzerland Univ Zurich Dept Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

We propose a generic event camera calibration framework using image reconstruction. Instead of relying on blinking LED patterns or external screens, we show that neural-network-based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras. The advantage of our proposed approach is that we can use standard calibration patterns that do not rely on active illumination. Furthermore, our approach enables the possibility to perform extrinsic calibration between frame-based and event-based sensors without additional complexity. Both simulation and real-world experiments indicate that calibration through image reconstruction is accurate under common distortion models and a wide variety of distortion parameters.

关键词： Computational modeling Robot vision systems Cameras Distortion Calibration Sensors pattern recognition

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Learning to Detect Phone-related Pedestrian Distracted Behaviors with Synthetic Data

Learning to Detect Phone-related Pedestrian Distracted Behav...

引用

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

作者： Hatay, Emre Ma, Jin Sun, Huiming Fang, Jianwu Gao, Zhiqiang Yu, Hongkai Cleveland State Univ Cleveland OH 44115 USA Changan Univ Xian Peoples R China

ISBN: (纸本)9781665448994

Due to the popularity and mobility of smart phones, phone-related pedestrian distracted behaviors, e.g., Texting, Game Playing, and Phone calls, have caused many traffic fatalities and accidents. As an advanced driver-assistance or autonomous-driving system, computer vision could be used to automatically detect distractions from cameras installed on the vehicle for useful safety intervention. The state-of-the-art method models this problem as a standard supervised learning method with a two-branch Convolutional Neural Network (CNN) followed by a voting on all image frames. In contrast, this paper proposes a new synthetic dataset named SYN-PPDB (448 synchronized video pairs of 53,760 computer game images) for this research problem and models it as a transfer learning problem from synthetic data to real data. A new deep learning model embedded with spatial-temporal feature learning and pose-aware transfer learning is proposed. Experimental results show that we could improve the state-of-the-art overall recognition accuracy from 84.27% to 96.67%.

关键词： computer vision Computational modeling Transfer learning Supervised learning Games pattern recognition Synchronization

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Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis

Lacunarity Pooling Layers for Plant Image Classification usi...

引用

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

作者： Mohan, Akshatha Peeples, Joshua Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77840 USA

ISBN: (纸本)9798350365474

Pooling layers (e.g., max and average) may overlook important information encoded in the spatial arrangement of pixel intensity and/or feature values. We propose a novel lacunarity pooling layer that aims to capture the spatial heterogeneity of the feature maps by evaluating the variability within local windows. The layer operates at multiple scales, allowing the network to adaptively learn hierarchical features. The lacunarity pooling layer can be seamlessly integrated into any artificial neural network architecture. Experimental results demonstrate the layer's effectiveness in capturing intricate spatial patterns, leading to improved feature extraction capabilities. The proposed approach holds promise in various domains, especially in agricultural image analysis tasks. This work contributes to the evolving landscape of artificial neural network architectures by introducing a novel pooling layer that enriches the representation of spatial features. Our code is publicly available. (1)

关键词： computer vision Image Classification Machine Learning Texture Analysis

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Interaction Classification with Key Actor Detection in Multi-Person Sports Videos

Interaction Classification with Key Actor Detection in Multi...

引用

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

作者： Askari, Farzaneh Ramaprasad, Rohit Clark, James J. Levine, Martin D. McGill Univ Montreal PQ Canada Birla Inst Technol & Sci Pilani Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Interaction recognition from multi-person videos is a challenging yet essential task in computer vision. Often the videos depict actions with multiple actors involved, some of whom participate in the main event, and the rest are present in the scene without being part of the actual event. This paper proposes a model to tackle the problem of interaction recognition from multi-person videos. Our model consists of a Recurrent Neural Network (RNN) equipped with a time-varying attention mechanism. It receives scene features and localized actors features to predict the interaction class. Additionally, the attention model identifies the people responsible for the main event. We chose penalty classification from ice hockey broadcast videos as our application. These videos are multi-persons and depict complex interactions between players in a non-laboratory recording setup. We evaluate our model on a new dataset of ice hockey penalty videos and report 93.93% classification accuracy. We include a qualitative analysis of the attention mechanism by visualizing the attention weights. Our code is publicly available (1).

关键词： Visualization computer vision Recurrent neural networks Annotations Computational modeling Ice Recording

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Essentials for Class Incremental Learning

Essentials for Class Incremental Learning

引用

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

作者： Mittal, Sudhanshu Galesso, Silvio Brox, Thomas Univ Freiburg Freiburg Germany

ISBN: (纸本)9781665448994

Contemporary neural networks are limited in their ability to learn from evolving streams of training data. When trained sequentially on new or evolving tasks, their accuracy drops sharply, making them unsuitable for many real-world applications. In this work, we shed light on the causes of this well known yet unsolved phenomenon often referred to as catastrophic forgetting - in a class-incremental setup. We show that a combination of simple components and a loss that balances intra-task and inter-task learning can already resolve forgetting to the same extent as more complex measures proposed in literature. Moreover, we identify poor quality of the learned representation as another reason for catastrophic forgetting in class-IL. We show that performance is correlated with secondary class information (dark knowledge) learned by the model and it can be improved by an appropriate regularizer. With these lessons learned, class-incremental learning results on CIFAR-100 and ImageNet improve over the state-of-the-art by a large margin, while keeping the approach simple.

关键词： Learning systems computer vision Art conferences Neural networks Training data Boosting

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Uncertainty Gated Network for Land Cover Segmentation 31

Uncertainty Gated Network for Land Cover Segmentation

引用

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

作者： Pascual, Guillem Segui, Santi Vioia, Joidi Univ Barcelona Barcelona Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The production of thematic maps depicting land cover is one of the most common applications of remote sensing. To this end, several semantic segmentation approaches, based on deep learning, have been proposed in the literature, but land cover segmentation is still considered an open problem due to some specific problems related to remote sensing imaging. In this paper we propose a novel approach to deal with the problem of modelling multiscale contexts surrounding pixels of different land cover categories. The approach leverages the computation of a heteroscedastic measure of uncertainty when classifying individual pixels in an image. This classification uncertainty measure is used to define a set of memory gates between layers that allow a principled method to select the optimal decision for each pixel.

关键词： Uncertainty Image segmentation Logic gates Semantics computer vision Measurement uncertainty Image resolution

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Discovering Style Trends through Deep Visually Aware Latent Item Embeddings 31

Discovering Style Trends through Deep Visually Aware Latent ...

引用

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

作者： Iqbal, Murium Kovac, Adair Aryafar, Kamelia Overstock Com Midvale UT 84047 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we explore Latent Dirichlet Allocation (LDA) [1] and Polylingual Latent Dirichlet Allocation (PolyLDA) [6], as a means to discover trending styles in Overstock 1 from deep visual semantic features transferred from a pretrained convolutional neural network and text-based item attributes. To utilize deep visual semantic features in conjunction with LDA, we develop a method for creating a bag of words representation of unrolled image vectors. By viewing the channels within the convolutional layers of a Resnet-50 [2] as being representative of a word, we can index these activations to create visual documents. We then train LDA over these documents to discover the latent style in the images. We also incorporate text-based data with PolyLDA, where each representation is viewed as an independent language attempting to describe the same style. The resulting topics are shown to be excellent indicators of visual style across our platform.

关键词： Visualization Semantics Image color analysis Shape Vocabulary Data models computer vision

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SAM: The Sensitivity of Attribution Methods to Hyperparameters

SAM: The Sensitivity of Attribution Methods to Hyperparamete...

引用

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

作者： Bansal, Naman Agarwal, Chirag Anh Nguyen Auburn Univ Auburn AL 36849 USA Univ Illinois Chicago IL 60680 USA

ISBN: (纸本)9781728193601

Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation method is its robustness to input hyperparameters which are often randomly set or empirically tuned. High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust of end-users. In this paper, we provide a thorough empirical study on the sensitivity of existing attribution methods. We found an alarming trend that many methods are highly sensitive to changes in their common hyperparameters e.g. even changing a random seed can yield a different explanation! Interestingly, such sensitivity is not reflected in the average explanation accuracy scores over the dataset as commonly reported in the literature. In addition, explanations generated for robust classifiers (i.e. which are trained to be invariant to pixel-wise perturbations) are surprisingly more robust than those generated for regular classifiers.

关键词： computer vision

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Differentiable Rendering-based Pose-Conditioned Human Image Generation

Differentiable Rendering-based Pose-Conditioned Human Image ...

引用

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

作者： Horiuchi, Yusuke Simo-Serra, Edgar Iizuka, Satoshi Ishikawa, Hiroshi Waseda Univ Tokyo Japan Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781665448994

Conditional human image generation, or generation of human images with specified pose based on one or more reference images, is an inherently ill-defined problem, as there can be multiple plausible appearance for parts that are occluded in the reference. Using multiple images can mitigate this problem while boosting the performance. In this work, we introduce a differentiable vertex and edge renderer for incorporating the pose information to realize human image generation conditioned on multiple reference images. The differentiable renderer has parameters that can be jointly optimized with other parts of the system to obtain better results by learning more meaningful shape representation of human pose. We evaluate our method on the Market-1501 and DeepFashion datasets and comparison with existing approaches validates the effectiveness of our approach.

关键词： Heating systems computer vision Image synthesis Shape Image edge detection conferences Training data

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