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Occlusion-aware Data Augmentation and Reconstruction for Improving Student Engagement in Online Learning Environments

Occlusion-aware Data Augmentation and Reconstruction for Imp...

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International image processing, Applications and Systems conference (IPAS)

作者： Vincent Pham Sonit Singh School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The demand for adaptive learning in education is an important application for Computer Vision (CV)-based detection models which extract learners’ faces to classify engagement. However, a loss in visual data due to the partial occlusion of learners’ faces imposes challenge for practical use-cases. In this paper, we propose an occlusion-aware framework to improve the robustness of non-occlusion-aware models in engagement detection. Firstly, our framework consists of an occlusion-aware data augmentation pipeline that aims to simulate the types of partially occluded faces in-the-wild. Secondly, we investigate the application of masked auto-encoders (MAE) for occlusion recovery. Extensive experiments have been performed to measure the effectiveness of the proposed occlusion-aware framework. Specifically, on the challenging FER-2013 dataset our occlusion-aware data augmentation achieves a 5.24% improvement in accuracy on baseline models and a 1.54% improvement against models with augmentation methods used in the state-of-the-art. Alongside the application of MAE, our proposed framework improves baseline classification accuracy by 13.3% and 4.59% on the FER-2013, and DAiSEE dataset for engagement detection. Our investigation into occlusion-aware engagement detection using the DAiSEE dataset provides important insight into the limitations of occlusion-awareness in engagement detection.

关键词： Computer vision visualization Accuracy Computational modeling Pipelines Data augmentation Robustness Data models Faces image reconstruction

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CNN-Based Circular Economy in E-Waste Management with Blockchain and IoT Integration

CNN-Based Circular Economy in E-Waste Management with Blockc...

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visual Analytics and Data visualization (ICVADV), International conference on

作者： P. Santhuja A Suresh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Increasing electronic waste (e-waste) is a serious environmental and financial problem that calls for creative ideas for effective resource recovery and recycling. This paper proposes an IoT- and Blockchain-integrated e-waste management system based on Convolutional Neural Networks (CNNs) to boost automated waste classification and, thus, circular economy practices. While Blockchain guarantees open and safe tracking of waste transportation, IoT sensors and RFID tags allow real-time monitoring of e-waste. Training on a collection of 50,000 e-waste images, a CNN-based image classification model attained an accuracy of 96.2% in classifying components into recyclables, reusables, and hazardous items. Using automated decision-making, the system showed a 28% reduction in processing time and a 32% gain in sorting efficiency over conventional approaches. Blockchain incorporation enhanced traceability through 100% secure transaction records, lowering fraud and illegal disposal. Experimental data show that this method improves 23% resource recovery rates by promoting sustainable e-waste management. The proposed architecture minimizes environmental effects by encouraging ethical e-waste disposal and a closed-loop recycling system. These results show how Blockchain-secured, AI-driven IoT devices might help to advance circular economic ideas for world e-waste management.

关键词： Waste management Training Accuracy Electronic waste Real-time systems Recycling Blockchains Internet of Things Convolutional neural networks Sorting

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Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentation

Multi-Scale Denoising in the Feature Space for Low-Light Ins...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Joanne Lin Nantheera Anantrasirichai David Bull Visual Information Laboratory University of Bristol Bristol United Kingdom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Instance segmentation for low-light imagery remains largely unexplored due to the challenges imposed by such conditions, for example shot noise due to low photon count, color distortions and reduced contrast. In this paper, we propose an end-to-end solution to address this challenging task. Our proposed method implements weighted non-local blocks (wNLB) in the feature extractor. This integration enables an inherent denoising process at the feature level. As a result, our method eliminates the need for aligned ground truth images during training, thus supporting training on real-world low-light datasets. We introduce additional learnable weights at each layer in order to enhance the network’s adaptability to real-world noise characteristics, which affect different feature scales in different ways. Experimental results on several object detectors show that the proposed method outperforms the pre-trained networks with an Average Precision (AP) improvement of at least +7.6, with the introduction of wNLB further enhancing AP by upto +1.3.

关键词： Instance segmentation Training Head image color analysis Semantic segmentation Noise reduction Object detection Feature extraction Speech processing Photonics

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Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition

Col-OLHTR: A Novel Framework for Multimodal Online Handwritt...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chenyu Liu Jinshui Hu Baocai Yin Jia Pan Bing Yin Jun Du Qingfeng Liu University of Science and Technology of China Hefei China iFLYTEK Research Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Online Handwritten Text Recognition (OLHTR) has gained considerable attention for its diverse range of applications. Current approaches usually treat OLHTR as a sequence recognition task, employing either a single trajectory or image encoder, or multi-stream encoders, combined with a CTC or attention-based recognition decoder. However, these approaches face several drawbacks: 1) single encoders typically focus on either local trajectories or visual regions, lacking the ability to dynamically capture relevant global features in challenging cases; 2) multi-stream encoders, while more comprehensive, suffer from complex structures and increased inference costs. To tackle this, we propose a Collaborative learning-based OLHTR framework, called Col-OLHTR, that learns multimodal features during training while maintaining a single-stream inference process. Col-OLHTR consists of a trajectory encoder, a Point-to-Spatial Alignment (P2SA) module, and an attention-based decoder. The P2SA module is designed to learn image-level spatial features through trajectory-encoded features and 2D rotary position embeddings. During training, an additional image-stream encoder-decoder is collaboratively trained to provide supervision for P2SA features. At inference, the extra streams are discarded, and only the P2SA module is used and merged before the decoder, simplifying the process while preserving high performance. Extensive experimental results on several OLHTR benchmarks demonstrate the state-of-the-art (SOTA) performance, proving the effectiveness and robustness of our design.

关键词： Training Handwriting recognition visualization image recognition Text recognition Speech recognition Trajectory Decoding Speech processing Streams

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Enhancing Autonomous Driving through Dual-Process Learning with Behavior and Reflection Integration

Enhancing Autonomous Driving through Dual-Process Learning w...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xiao Zhang Kangsheng Wang Tianyu Hu Huimin Ma School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Contemporary autonomous driving (AD) methodologies, which predominantly convert visual features into control directives, face long-tail challenges due to constraints imposed by limited data distribution. Conversely, human drivers exhibit proficiency in such conditions, underscoring the significance of emulating human cognition in AD systems. Therefore, we introduce Dual-Process Learning (D-PL) approach for cognitive-enhanced decision-making. Inspired by dual-process theory, the D-PL method combines Behavior Pattern Learning (BPL) and Self-Reflective Learning (SRL) to integrate quick, intuitive decisions with deliberate, analytical reasoning, constructing a hierarchical decision model for sophisticated trajectory planning. Our approach improves decision-making, enhances adaptability, and tackles the crucial open-world generalization challenge encountered by current AD methods. Comprehensive evaluations on the nuScenes dataset validate the robustness of our method, demonstrating its superior performance in navigating the intricacies of real-world contrasting with conventional models.

关键词： Adaptation models visualization Heavily-tailed distribution Trajectory planning Decision making Power line communications Cognition Robustness Speech processing Autonomous vehicles

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PeT-KeyStAtion: Parameter-efficient Transformer with Keypoint-guided Spatial-temporal Aggregation for Video-based Person Re-identification

PeT-KeyStAtion: Parameter-efficient Transformer with Keypoin...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xingan Ma Jinhui Yi Juergen Gall University of Bonn Bonn Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Video-based Person Re-identification (ReID) is crucial in visual surveillance, focusing on matching video snippets of individuals across multiple non-overlapping cameras. Existing methods either conduct ReID at the image level without leveraging temporal information, or employ complex temporal information aggregation techniques, which results in substantial network size and reduced performance efficiency. Recent advances in Vision Transformer (ViT) architectures leverage diverse large-scale datasets alongside sophisticated architectures to achieve enhanced fine-grained feature discrimination. To fully explore the potential of ViT architectures without adding substantial additional modules for video-based ReID, we propose PeT-KeyStAtion: a Parameter-efficient Transformer with Keypoint- guided Spatial-temporal Aggregation using a Spatial-Temporal and Keypoint (STK) Module with lightweight adapters. Our framework effectively captures and aggregates spatial, temporal, and keypoint information with only 11% of the parameters compared to full fine-tuning. Extensive experiments show that our method outperforms state-of-the-art baselines on MARS and iLIDS-VID, and achieves promising performance on LS-VID.

关键词： Representation learning visualization Mars Computer vision Surveillance Focusing Signal processing Transformers Speech processing Identification of persons

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Adversarial Training and Cross-modal Feature Fusion in Multimodal Sentiment Analysis

Adversarial Training and Cross-modal Feature Fusion in Multi...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Junhuai Li Chuang Lin Huaijun Wang Yuxing Zhi Jing Chen Tao Huang School of Computer Science and Engineering Xi’an University of Technology China Intelligent Computing and Communications (ICC) Lab James Cook University Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal sentiment analysis recognizes emotions through text, audio, and visual modalities, but data incompleteness is a major challenge. Existing methods often focus on specific types of deficiencies and perform poorly when multiple types of noise are present simultaneously. To address this issue, we propose a noise-prompted adversarial training framework with a multimodal interaction model to enhance the model’s robustness to missing modalities. The model first extracts common and unique features from each modality using a BERT text encoder and a shared-private encoder. Correlation measurements are then used to calculate the similarity between modalities, and a weighting mechanism is applied to the shared features. These features are deeply fused using a Transformer, and adversarial training combined with semantic reconstruction supervision helps the model learn a unified representation of noisy and clean data. Experimental results show that this method significantly improves the performance of multimodal sentiment analysis.

关键词： Training Weight measurement Sentiment analysis Analytical models Adaptation models visualization Feature extraction Transformers Robustness Noise measurement

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imageFlowNet: Forecasting Multiscale image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical images

ImageFlowNet: Forecasting Multiscale Image-Level Trajectorie...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chen Liu Ke Xu Liangbo L. Shen Guillaume Huguet Zilong Wang Alexander Tong Danilo Bzdok Jay Stewart Jay C. Wang Lucian V. Del Priore Smita Krishnaswamy Yale University UCSF Mila Univ. de Montréal Mila McGill Yale NorCal Retina Vitreous Associates

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Advances in medical imaging technologies have enabled the collection of longitudinal images, which involve repeated scanning of the same patients over time, to monitor disease progression. However, predictive modeling of such data remains challenging due to high dimensionality, irregular sampling, and data sparsity. To address these issues, we propose imageFlowNet, a novel model designed to forecast disease trajectories from initial images while preserving spatial details. imageFlowNet first learns multiscale joint representation spaces across patients and time points, then optimizes deterministic or stochastic flow fields within these spaces using a position-parameterized neural ODE/SDE framework. The model leverages a UNet architecture to create robust multiscale representations and mitigates data scarcity by combining knowledge from all patients. We provide theoretical insights that support our formulation of ODEs, and motivate our regularizations involving high-level visual features, latent space organization, and trajectory smoothness. We validate imageFlowNet on three longitudinal medical image datasets depicting progression in geographic atrophy, multiple sclerosis, and glioblastoma, demonstrating its ability to effectively forecast disease progression and outperform existing methods. Our contributions include the development of imageFlowNet, its theoretical underpinnings, and empirical validation on real-world datasets.

关键词： visualization Multiple sclerosis Stochastic processes Organizations Predictive models Signal processing Data models Trajectory Speech processing Biomedical imaging

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MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Efficient PPE Compliance Detection

MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Effi...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Juntao Zan Yang Fang Qilie Liu Uswah Khairuddin Yan Li Kaiwei Sun School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Malaysia-Japan International Institute of Technology University of Technology Malaysia Kuala Lumpur Malaysia Department of Electrical and Computer Engineering Inha University Incheon South Korea Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tasks in complex industrial scenarios such as remote surveillance and extremely small targets. In addition, there is a lack of effective model lightweighting and knowledge transfer approaches for industrial deployment. To this end, this paper proposes a Multi-scale and Knowledge-Distilling YOLO (MKD-YOLO) based on YOLOv8n for efficient PPE compliance detection. Specifically, in backbone stage, we design an Efficient Multi-Scale Enhanced Convolution (C2f-EMSEC) module and Large Spatial Pyramid Pooling-Fast (LSPPF) module for multi-scale and global-contextual feature learning as well as reducing model complexity. Then, in neck stage, a refined Bidirectional feature Pyramid Network (BPNet) is designated to capture fine-grained details for extremely small object detection. Moreover, we apply channel-wise knowledge distillation to facilitate model lightweighting and domain-specific knowledge transfer learning. Experiments on our proposed dataset and public datasets show that the proposed MKD-YOLO achieves a new state-of-the-art (SOTA) detection performance and efficiency for practical PPE compliance detection tasks. Codes and the dataset are available at https://***/z1Zjt/MKD-YOLO.

关键词： Personal protective equipment YOLO Representation learning Convolution Surveillance Speech enhancement Feature extraction Neck Kernel Knowledge transfer

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Enhanced image Deblurring with the Fusion of Generative Adversarial Networks and Transformer Models 1st

Enhanced Image Deblurring with the Fusion of Generative Adve...

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1st International conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024

作者： Ranjan, Arti Ravinder, M. New Delhi India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (纸本)9783031808418

In the domain of image processing, deblurring remains a challenging task, particularly in restoring high-fidelity details from blurred images. This paper introduces a novel approach that synergizes the strengths of Generative Adversarial Networks (GANs) and Transformer models to address this challenge. Our methodology leverages the generative capability of GANs to produce realistic and sharp images, while incorporating the Transformer’s prowess in capturing long-range dependencies and intricate details within images. This adversarial training regime is designed to refine the model’s ability to generate deblurred images that are not only accurate in detail but also high in perceptual quality. Through extensive experiments on various datasets, our model demonstrates superior performance in deblurring, outperforming existing state-of-the-art methods in terms of both objective metrics and subjective visual quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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