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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...

引用

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

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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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"BNN - BN = ?": Training Binary Neural Networks without Batch Normalization

"BNN - BN = ?": Training Binary Neural Networks without Batc...

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

作者： Chen, Tianlong Zhang, Zhenyu Ouyang, Xu Liu, Zechun Shen, Zhiqiang Wang, Zhangyang Univ Texas Austin Austin TX 78712 USA Univ Sci & Technol China Hefei Anhui Peoples R China Cornell Univ Ithaca NY 14853 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665448994

Batch normalization (BN) is a key facilitator and considered essential for state-of-the-art binary neural networks (BNN). However, the BN layer is costly to calculate and is typically implemented with non-binary parameters, leaving a hurdle for the efficient implementation of BNN training. It also introduces undesirable dependence between samples within each batch. Inspired by the latest advance on Batch Normalization Free (BN-Free) training [7], we extend their framework to training BNNs, and for the first time demonstrate that BNs can be completely removed from BNN training and inference regimes. By plugging in and customizing techniques including adaptive gradient clipping, scale weight standardization, and specialized bottleneck block, a BN-free BNN is capable of maintaining competitive accuracy compared to its BN-based counterpart. Extensive experiments validate the effectiveness of our proposal across diverse BNN backbones and datasets. For example, after removing BNs from the state-of-the-art ReActNets [38], it can still be trained with our proposed methodology to achieve 92.08%, 68.34%, and 68.0% accuracy on CIFAR-10, CIFAR-100, and ImageNet respectively, with marginal performance drop (0.23% similar to 0.44% on CIFAR and 1.40% on ImageNet). Codes and pre-trained models are available at: https://***/VITA-Group/BNN_NoBN.

关键词： Training computer vision conferences Neural networks Standardization Hardware pattern recognition

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IB-DRR - Incremental Learning with Information-Back Discrete Representation Replay

IB-DRR - Incremental Learning with Information-Back Discrete...

引用

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

作者： Jiang, Jian Cetin, Edoardo Celiktutan, Oya Kings Coll London Ctr Robot Res Dept Engn London England

ISBN: (纸本)9781665448994

Incremental learning aims to enable machine learning models to continuously acquire new knowledge given new classes, while maintaining the knowledge already learned for old classes. Saving a subset of training samples of previously seen classes in the memory and replaying them during new training phases is proven to be an efficient and effective way to fulfil this aim. It is evident that the larger number of exemplars the model inherits the better performance it can achieve. However, finding a trade-off between the model performance and the number of samples to save for each class is still an open problem for replay-based incremental learning and is increasingly desirable for real-life applications. In this paper, we approach this open problem by tapping into a two-step compression approach. The first step is a lossy compression, we propose to encode input images and save their discrete latent representations in the form of 'codes' that are learned using a hierarchical Vector Quantised Variational Autoencoder (VQ-VAE). In the second step, we further compress 'codes' losslessly by learning a hierarchical latent variable model with bits-back asymmetric numeral systems (BB-ANS). To compensate for the information lost in the first step compression, we introduce an Information Back (IB) mechanism that utilizes raw exemplars for a contrastive learning loss to regularise the training of a classifier. By maintaining all seen exemplars' representations in the format of 'codes', Discrete Representation Replay (DRR) outperforms the state-of-art method on CIFAR-100 by a margin of 4% average accuracy with a much less memory cost required for saving samples. Incorporated with IB and saving a small set of old raw exemplars as well, the average accuracy of DRR can be further improved by 2%.

关键词： Training computer vision Image coding conferences Memory management Machine learning pattern recognition

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CroSpace6D: Leveraging Geometric and Motion Cues for High-Precision Cross-Domain 6DoF Pose Estimation for Non-Cooperative Spacecrafts

CroSpace6D: Leveraging Geometric and Motion Cues for High-Pr...

引用

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

作者： Jianhong Zuo Shengyang Zhang Qianyu Zhang Yutao Zhao Baichuan Liu Aodi Wu Xue Wan Leizheng Shu Guohua Kang Nanjing University of Aeronautics and Astronautics Chinese Academy of Sciences Technology and Engineering Center for Space Utilization University of Chinese Academy of Sciences

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The utilization of monocular vision for non-cooperative spacecraft pose estimation has been significantly researched in space target monitoring, on-orbit servicing, and satellite maintenance. The challenge lies in addressing the cross-domain variations in shape, texture, lighting, and motion patterns between simulated and real captured images. To tackle this issue, a novel domain adaptation 6DoF pose estimation algorithm is proposed to extract the geometric and semantic consistency between cross-domain training and testing datasets. Experimental results demonstrate that our pose estimation method achieves state-of-the-art performance on the SPARK2024 dataset.

关键词： Space vehicles Training Satellites Shape Pose estimation Semantics pattern recognition

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Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery

Superpixels and Graph Convolutional Neural Networks for Effi...

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

作者： Dadsetan, Saba Pichler, David Wilson, David Hovakimyan, Naira Hobbs, Jennifer Univ Pittsburgh Pittsburgh PA 15260 USA Intelinair Inc San Francisco CA USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781665448994

Advances in remote sensing technology have led to the capture of massive amounts of data. Increased image resolution, more frequent revisit times, and additional spectral channels have created an explosion in the amount of data that is available to provide analyses and intelligence across domains, including agriculture. However, the processing of this data comes with a cost in terms of computation time and money, both of which must be considered when the goal of an algorithm is to provide real-time intelligence to improve efficiencies. Specifically, we seek to identify nutrient deficient areas from remotely sensed data to alert farmers to regions that require attention;detection of nutrient deficient areas is a key task in precision agriculture as farmers must quickly respond to struggling areas to protect their harvests. Past methods have focused on pixel-level classification (i.e. semantic segmentation) of the field to achieve these tasks, often using deep learning models with tens-of-millions of parameters. In contrast, we propose a much lighter graph-based method to perform node-based classification. We first use Simple Linear Iterative Cluster (SLIC) to produce super-pixels across the field. Then, to perform segmentation across the non-Euclidean domain of superpixels, we leverage a Graph Convolutional Neural Network (GCN). This model has 4-orders-of-magnitude fewer parameters than a CNN model and trains in a matter of minutes.

关键词： Image segmentation Semantics Agriculture Real-time systems Computational efficiency pattern recognition Convolutional neural networks

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Semi-synthesis: A fast way to produce effective datasets for stereo matching

Semi-synthesis: A fast way to produce effective datasets for...

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

作者： He, Ju Zhou, Enyu Sun, Liusheng Lei, Fei Liu, Chenyang Sun, Wenxiu Johns Hopkins Univ Baltimore MD 21218 USA SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781665448994

Stereo matching is an important problem in computer vision which has drawn tremendous research attention for decades. Recent years, data-driven methods with convolutional neural networks (CNNs) are continuously pushing stereo matching to new heights. However, data-driven methods require large amount of training data, which is not an easy task for real stereo data due to the annotation difficulties of per-pixel ground-truth disparity. Though synthetic dataset is proposed to fill the gaps of large data demand, the fine-tuning on real dataset is still needed due to the domain variances between synthetic data and real data. In this paper, we found that in synthetic datasets, close-to-real-scene texture rendering is a key factor to boost up stereo matching performance, while close-to-real-scene 3D modeling is less important. We then propose semi-synthetic, an effective and fast way to synthesize large amount of data with close-to-real-scene texture to minimize the gap between synthetic data and real data. Extensive experiments demonstrate that models trained with our proposed semi-synthetic datasets achieve significantly better performance than with general synthetic datasets, especially on real data benchmarks with limited training data. With further fine-tuning on the real dataset, we also achieve SOTA performance on Middlebury and competitive results on KITTI and ETH3D datasets.

关键词： Training computer vision Three-dimensional displays Training data Benchmark testing Rendering (computer graphics) Environmental factors

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