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IntegralAction: Pose-driven Feature Integration for Robust Human Action recognition in Videos

IntegralAction: Pose-driven Feature Integration for Robust H...

引用

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

作者： Moon, Gyeongsik Kwon, Heeseung Lee, Kyoung Mu Cho, Minsu SNU ECE & ASRI Seoul South Korea ASRI Seoul South Korea POSTECH CSE Pohang South Korea AIGS Seoul South Korea

ISBN: (纸本)9781665448994

Most current action recognition methods heavily rely on appearance information by taking an RGB sequence of entire image regions as input. While being effective in exploiting contextual information around humans, e.g., human appearance and scene category, they are easily fooled by out-of-context action videos where the contexts do not exactly match with target actions. In contrast, pose-based methods, which take a sequence of human skeletons only as input, suffer from inaccurate pose estimation or ambiguity of human pose per se. Integrating these two approaches has turned out to be non-trivial;training a model with both appearance and pose ends up with a strong bias towards appearance and does not generalize well to unseen videos. To address this problem, we propose to learn pose-driven feature integration that dynamically combines appearance and pose streams by observing pose features on the fly. The main idea is to let the pose stream decide how much and which appearance information is used in integration based on whether the given pose information is reliable or not. We show that the proposed IntegralAction achieves highly robust performance across in-context and out-of-context action video datasets. The codes are available in here.

关键词： Training computer vision Image recognition conferences Pose estimation Information filters Skeleton

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LOFI: LOng-tailed FIne-Grained Network for Food recognition

LOFI: LOng-tailed FIne-Grained Network for Food Recognition

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jesús M. Rodríguez-de-Vera Imanol G. Estepa Marc Bolaños Bhalaji Nagarajan Petia Radeva Universitat de Barcelona Barcelona Spain AIGecko Technologies SL Barcelona Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Food recognition plays a crucial role in several healthcare applications. Nevertheless, it presents significant computer vision challenges such as long-tailed and fine-grained distributions that hinder its progress. In this work, we propose LOFI, a Long-tailed Fine-grained Network aimed specifically at tackling these food recognition challenges by improving the feature learning capabilities of food recognition models. Specifically, we improve vanilla R-CNN architecture by tailoring it for food recognition. We design an efficient multi-task framework for fine-grained food recognition, which exploits the lexical similarity of dishes during training to improve the discriminative ability of the network. Secondly, we include a Graph Confidence Propagation module based on graph neural networks to aggregate the information of overlapping detections and refine the final prediction of the network. Extensive analysis and ablations of different components of LOFI highlight that it successfully addresses the targeted problems and leads to noticeable gains in performance. Remarkably, the proposed method achieves competitive results and outperforms the current state-of-the-art methods in three public food benchmarks: UECFood-256, AiCrowd Food Challenge 2022, and UECFood-100 segmented.

关键词： Training Representation learning computer vision conferences Computational modeling Medical services computer architecture

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Generative Dataset Distillation: Balancing Global Structure and Local Details

Generative Dataset Distillation: Balancing Global Structure ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Longzhen Li Guang Li Ren Togo Keisuke Maeda Takahiro Ogawa Miki Haseyama Hokkaido University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a generative model. Dataset distillation has been proposed to reduce the size of the required dataset when training models. The conventional dataset distillation methods face the problem of long redeployment time and poor cross-architecture performance. Moreover, previous methods focused too much on the high-level semantic attributes between the synthetic dataset and the original dataset while ignoring the local features such as texture and shape. Based on the above understanding, we propose a new method for distilling the original image dataset into a generative model. Our method involves using a conditional generative adversarial network to generate the distilled dataset. Subsequently, we ensure balancing global structure and local details in the distillation process, continuously optimizing the generator for more information-dense dataset generation.

关键词： Training computer vision Shape Face recognition conferences Computational modeling Semantics

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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 computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Akshatha Mohan Joshua Peeples Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

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 Adaptation models Accuracy Computational modeling computer architecture Feature extraction Convolutional neural networks

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Using Language-Aligned Gesture Embeddings for Understanding Gestures Accompanying Math Terms

Using Language-Aligned Gesture Embeddings for Understanding ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tristan Maidment Purav J Patel Erin Walker Adriana Kovashka Intelligent Systems Program University of Pittsburgh Pittsburgh PA USA University of Maryland College Park MD USA Computer Science University of Pittsburgh Pittsburgh PA USA Learning Research and Development Center University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we introduce an approach for recognizing and classifying gestures that accompany mathematical terms, in a new collection we name the "GAMT" dataset. Our method uses language as a means of providing context to classify gestures. Specifically, we use a CLIP-style framework to construct a shared embedding space for gestures and language, experimenting with various methods for encoding gestures within this space. We evaluate our method on our new dataset containing a wide array of gestures associated with mathematical terms. The shared embedding space leads to a substantial improvement in gesture classification. Furthermore, we identify an efficient model that excelled at classifying gestures from our unique dataset, thus contributing to the further development of gesture recognition in diverse interaction scenarios.

关键词： computer vision conferences Computational modeling Gesture recognition Mathematical models Encoding

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Purposeful Regularization with Reinforcement Learning for Facial Expression recognition In-the-Wild

Purposeful Regularization with Reinforcement Learning for Fa...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： SangHwa Hong Department of Industrial Engineering Seoul National University of Science and Technology Seoul South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Facial Expression recognition (FER), an essential aspect of emotion analysis through artificial intelligence, is a crucial research area. Although traditional approaches utilizing Convolutional Neural Networks (CNNs) for analyzing human emotions from facial expressions achieve superior accuracy over conventional machine learning methods, overfitting—especially arising severely from data collected in uncontrolled, In-the-wild settings—significantly impedes CNNs performance. This is due to the data scarcity and inherent noise inside In-the-wild data. To address this challenge, this paper introduces a novel regularization method that employs Reinforcement Learning (RL) to adaptively apply regularization hyperparameters appropriate for the evolving state of trained CNNs. Through experiments on various datasets such as CIFAR100, FER2013, and AffectNet datasets including diverse perspective analysis such as graphical, Grad-CAM and numerical analysis, it is demonstrated that the suggested method can alleviate memorization of noise in training data and promote learning of essential features. The significance of the suggested method lies in its demonstrated remarkable effectiveness in enhancing CNNs’ generalization and reducing overfitting.

关键词： Training Emotion recognition computer vision Numerical analysis Face recognition conferences Noise

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A Cross-Dataset Study for Text-based 3D Human Motion Retrieval

A Cross-Dataset Study for Text-based 3D Human Motion Retriev...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Léore Bensabath Mathis Petrovich Gül Varol LIGM École des Ponts Univ Gustave Eiffel CNRS France Max Planck Institute for Intelligent Systems Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We provide results of our study on text-based 3D human motion retrieval and particularly focus on cross-dataset gen-eralization. Due to practical reasons such as dataset-specific human body representations, existing works typically benchmark by training and testing on partitions from the same dataset. Here, we employ a unified SMPL body format for all datasets, which allows us to perform training on one dataset, testing on the other, as well as training on a combination of datasets. Our results suggest that there exist dataset biases in standard text-motion benchmarks such as HumanML3D, KIT Motion-Language, and BABEL. We show that text augmentations help close the domain gap to some extent, but the gap remains. We further provide the first zero-shot action recognition results on BABEL, without using categorical action labels during training, opening up a new avenue for future research.

关键词： Training computer vision Three-dimensional displays conferences Benchmark testing pattern recognition Standards

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A Survey on 3D Egocentric Human Pose Estimation

A Survey on 3D Egocentric Human Pose Estimation

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Md Mushfiqur Azam Kevin Desai The University of Texas at San Antonio

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Egocentric human pose estimation aims to estimate human body poses and develop body representations from a first-person camera perspective. It has gained vast popularity in recent years because of its wide range of applications in sectors like XR-technologies, human-computer interaction, and fitness tracking. However, to the best of our knowledge, there is no systematic literature review based on the proposed solutions regarding egocentric 3D human pose estimation. To that end, the aim of this survey paper is to provide an extensive overview of the current state of egocentric pose estimation research. In this paper, we categorize and discuss the popular datasets and the different pose estimation models, highlighting the strengths and weaknesses of different methods by comparative analysis. This survey can be a valuable resource for both researchers and practitioners in the field, offering insights into key concepts and cutting-edge solutions in egocentric pose estimation, its wide-ranging applications, as well as the open problems with future scope.

关键词： Surveys Human computer interaction computer vision Analytical models Three-dimensional displays conferences Pose estimation

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Rethinking the Domain Gap in Near-infrared Face recognition

Rethinking the Domain Gap in Near-infrared Face Recognition

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Michail Tarasiou Jiankang Deng Stefanos Zafeiriou Imperial College London

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Heterogeneous face recognition (HFR) involves the intricate task of matching face images across the visual domains of visible (VIS) and near-infrared (NIR). While much of the existing literature on HFR identifies the domain gap as a primary challenge and directs efforts towards bridging it at either the input or feature level, our work deviates from this trend. We observe that large neural networks, unlike their smaller counterparts, when pretrained on large scale homogeneous VIS data, demonstrate exceptional zero-shot performance in HFR, suggesting that the domain gap might be less pronounced than previously believed. By approaching the HFR problem as one of low-data fine-tuning, we introduce a straightforward framework: comprehensive pre-training, succeeded by a regularized fine-tuning strategy, that matches or surpasses the current state-of-the-art on four publicly available benchmarks. Given its simplicity and demonstrably strong performance, our method could be used as a practical solution for adjusting face recognition models to HFR as well as a new baseline for future HFR research. Corresponding training and evaluation codes can be found at https://***/michaeltrs/RethinkNIRVIS.

关键词： Training Visualization computer vision Codes Face recognition conferences Refining

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MarkerPose: Robust Real-time Planar Target Tracking for Accurate Stereo Pose Estimation

MarkerPose: Robust Real-time Planar Target Tracking for Accu...

引用

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

作者： Meza, Jhacson Romero, Lenny A. Marrugo, Andres G. Univ Tecnol Bolivar Cartagena Colombia

ISBN: (纸本)9781665448994

Despite the attention marker-less pose estimation has attracted in recent years, marker-based approaches still provide unbeatable accuracy under controlled environmental conditions. Thus, they are used in many fields such as robotics or biomedical applications but are primarily implemented through classical approaches, which require lots of heuristics and parameter tuning for reliable performance under different environments. In this work, we propose MarkerPose, a robust, real-time pose estimation system based on a planar target of three circles and a stereo vision system. MarkerPose is meant for high-accuracy pose estimation applications. Our method consists of two deep neural networks for marker point detection. A SuperPoint-like network for pixel-level accuracy keypoint localization and classification, and we introduce EllipSegNet, a lightweight ellipse segmentation network for sub-pixel-level accuracy keypoint detection. The marker's pose is estimated through stereo triangulation. The target point detection is robust to low lighting and motion blur conditions. We compared MarkerPose with a detection method based on classical computer vision techniques using a robotic arm for validation. The results show our method provides better accuracy than the classical technique. Finally, we demonstrate the suitability of MarkerPose in a 3D freehand ultrasound system, which is an application where highly accurate pose estimation is required. Code is available in Python and C++ at https://***/jhacsonmeza/MarkerPose.

关键词： Deep learning computer vision Ultrasonic imaging Target tracking Three-dimensional displays Pose estimation Lighting

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