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4th international conference on Detection Technology and Intelligence System, DTIS 2024

4th International Conference on Detection Technology and Int...

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4th international conference on Detection Technology and Intelligence System, DTIS 2024

The proceedings contain 30 papers. The topics discussed include: application of multichannel fluorescence signal processing for oil detection in water;advanced technical assessment of 40 MW large-scale calorimeter in full-scale n-heptane pool fire testing: precision measurement and analysis;application of audio magnetotelluric method in detecting buried faults in water diversion tunnels;a breathing rate detection method based on infrared thermal imaging video;an EMAT inspection technique based on self-driven internal detectors for industrial pipelines;a self-driven deformation detection robot technology for urban gas pipelines;and cable security state detection based on multi-scale information fusion and distributed fiber optic sensing.

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Memristor based Crossbar Array using CNN for Image processing Applications

Memristor based Crossbar Array using CNN for Image Processin...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Sritama Roy School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Image processing has become one of the popular neuromorphic computing applications in recent years. Most of the application areas require an efficient and real time image processing system. Memristor is a useful memory device which plays an important role to design an image processing system and realization of hardware design. Due to the non-volatile nature of memory and less power consumption, Memristor has a huge application in the field of image processing. Being a memory device, Memristor can preserve data and parameters during processing time and reducing the need for data transfer as well as storage. In this research, Memristor in the form of CNN architecture will store the image extracted data where pixel image is given as an input and voltage pulses are generated according to the pixel value. The voltage pulses are then applied as input to the Memristor crossbars and compared with a certain threshold voltage above which an output neuron responds. The above mention system is trained for image recognition applications in real time scenarios. The real time image processing based Memristor system can have huge application areas such as medical imaging, security, computer vision and robotics.

关键词： Semiconductor device modeling Analytical models Neuromorphic engineering Image processing Computational modeling Memristors robot sensing systems Real-time systems Threshold voltage Security

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OwlsEye: Real-Time Low-Light Video Instance Segmentation on Edge and Exploration of Fixed-Posit Quantization

OwlsEye: Real-Time Low-Light Video Instance Segmentation on ...

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international conference on VLSI Design

作者： Gaurav Shah Abhinav Goud Zaqi Momin Joycee Mekie Indian Institute of Technology Gandhinagar

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

Video Instance Segmentation (VIS) in low light conditions presents a significant challenge when deployed on resource-constrained edge devices, especially in autonomous vehicles, surveillance, robotics, or similar applications. This paper presents OwlsEye, which, to the best of our knowledge, is the first hardware implementation for real-time Video Instance Segmentation under low-light settings using an off-the-shelf RGB camera. Implemented on the Intel Nezha Embedded platform, OwlsEye demonstrates an improvement in Frames Per Second (FPS) from 0.6 to 28 FPS using model weight quantization, brightness verification, and asynchronous FIFO Pipelining. This paper also presents the EQyTorch framework, an extension of Qtorch+ for fixed-posit numbers, used in weight quantization for the YOLOv8 architecture. We show that fixed-posit quantization achieves an improvement in latency and power utilization of $2.35\times, 3.6\times$ , and $9.02\times, 87.91\times$ compared to INT8 and FP32 for 65nm CMOS. Furthermore, this paper presents a synthetic DarkCOCO2017 validation dataset to test the OwlsEye segmentation performance in enhanced, original, and dark images. Our work highlights a novel real-time low-light VIS system and the potential of using Fixed-Posit quantization for edge AI applications.

关键词： Instance segmentation Quantization (signal) Image edge detection Surveillance robot vision systems Streaming media Very large scale integration Real-time systems robots Pipeline processing

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Modular Prompt Learning Improves vision-Language Models

Modular Prompt Learning Improves Vision-Language Models

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhenhan Huang Tejaswini Pedapati Pin-Yu Chen Jianxi Gao Rensselaer Polytechnic Institute Troy USA IBM Research Yorktown Heights USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Pre-trained vision-language models are able to interpret visual concepts and language semantics. Prompt learning, a method of constructing prompts for text encoders or image encoders, elicits the potentials of pre-trained models and readily adapts them to new scenarios. Compared to fine-tuning, prompt learning enables the model to achieve comparable or better performance using fewer trainable parameters. Besides, prompt learning freezes the pre-trained model and avoids the catastrophic forgetting issue in the fine-tuning. Continuous prompts inserted into the input of every transformer layer (i.e. deep prompts) can improve the performances of pre-trained models on downstream tasks. For i-th transformer layer, the inserted prompts replace previously inserted prompts in the (i − 1)-th layer. Although the self-attention mechanism contextualizes newly inserted prompts for the current layer and embeddings from the previous layer’s output, removing all inserted prompts from the previous layer inevitably loses information contained in the continuous prompts. In this work, we propose Modular Prompt Learning (MPL) that is designed to promote the preservation of information contained in the inserted prompts. We evaluate the proposed method on base-to-new generalization and cross-dataset tasks. On average of 11 datasets, our method achieves 0.7% performance gain on the base-to-new generalization task compared to the state-of-the-art method. The largest improvement on the individual dataset is 10.7% (EuroSAT dataset). Our code is available at https://***/Zhenhan-Huang/Modular-Prompt-Learning.

关键词： Multiprotocol label switching Adaptation models Visualization Codes Semantics signal processing Performance gain Transformers Acoustics Speech processing

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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Egocentric Speaker Diarization with vision-Guided Clustering and Adaptive Speech Re-detection

Egocentric Speaker Diarization with Vision-Guided Clustering...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： He Huang Haoyuan Yu Daibo Liu Haowen Chen Minjie Cai Hunan University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker diarization aims to identify "who spoke when" in multi-person conversational scenarios. State-of-the-art audio-only diarization methods divide the task into multi-stages of speech segmentation, neural speaker embedding and unsupervised clustering. Egocentric speaker diarization (i.e., diarization in egocentric videos) is characterized by natural conversational scenarios involving a variety of noisy backgrounds, changing sound levels and overlapping speech. These cause difficulty in predicting the number of speakers from audio input, and heavily influence the performance of audio-based clustering. Although audio-visual modeling has been studied recently to enhance speaker diarization, unreliable visual information in egocentric videos may even degrade the performance. In this work, we propose a unified audio-visual diarization framework by incorporating visual guidance into the audio-only diarization pipeline. In addition, we also propose an adaptive speech re-detection strategy to detect and assign speaker identity to the mistakenly undetected audio segments. Experiments on the Ego4D dataset show that our method achieves state-of-the-art diarization performance in challenging egocentric scenarios. The code and model weights are available at https://***/YellowRiver2001/EgoDiarization.

关键词： Visualization Adaptation models Codes Pipelines signal processing algorithms Clustering algorithms signal processing Noise measurement Speech processing Videos

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Sound-VECaps: Improving Audio Generation with Visually Enhanced Captions

Sound-VECaps: Improving Audio Generation with Visually Enhan...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yi Yuan Dongya Jia Xiaobin Zhuang Yuanzhe Chen Zhuo Chen Yuping Wang Yuxuan Wang Xubo Liu Xiyuan Kang Mark D. Plumbley Wenwu Wang Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey ByteDance

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generative models have shown significant achievements in audio generation tasks. However, existing models struggle with complex and detailed prompts, leading to potential performance degradation. We hypothesize that this problem stems from the simplicity and scarcity of the training data. This work aims to create a large-scale audio dataset with rich captions for improving audio generation models. We first develop an automated pipeline to generate detailed captions by transforming predicted visual captions, audio captions, and tagging labels into comprehensive descriptions using a Large Language Model (LLM). The resulting dataset, Sound-VECaps, comprises 1.66M high-quality audio-caption pairs with enriched details including audio event orders, occurred places and environment information. We then demonstrate that training the text-to-audio generation models with Sound-VECaps significantly improves the performance on complex prompts. Furthermore, we conduct ablation studies of the models on several downstream audio-language tasks, showing the potential of Sound-VECaps in advancing audio-text representation *** and demos are available at https://***/Sound-VECaps-demo/.

关键词： Training Degradation Visualization Large language models Pipelines Training data Benchmark testing Tagging signal processing Speech processing

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Multi-frequency Fine-Grained Matching for Audio-Visual Segmentation 27th

Multi-frequency Fine-Grained Matching for Audio-Visual Segm...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Zhang, Yinhao Xu, Tianyang Wu, Xiao-Jun Zhao, Shaochuan Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9783031781094

Audio-visual segmentation is a recently proposed task, whose main goal is to locate the target of the sound in the image at the pixel level. In practical scenarios, multiple types of audio can coexist, with different objects emitting sounds at different frequencies. However, existing methods only use single-frequency audio information when fusing audio and visual modalities. Moreover, the process of combining images and audio can be quite rough. Therefore, we propose a multi-frequency fine-grained matching method for multiple sound sources scenario. Firstly, we use short-time Fourier transform (STFT) to extract different frequency spectrograms and input them into the audio encoder to extract multi-frequency audio features. Secondly, multi-frequency audio information serves as a prompt in the pixel decoder stage to guide model segmentation. To obtain high-quality prompts, we use an attention method in the Audio-Visual Matching Module (AVMM) to match visual and audio information. The experiments show that our method has a significant improvement over the baseline and achieves state-of-the-art results on the MS3 benchmark (64.1 mIoU on MS3). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Multi-Stage Multimodal Distillation for Audio-Visual Speaker Tracking

Multi-Stage Multimodal Distillation for Audio-Visual Speaker...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yidi Li Wenkai Zhao Zeyu Wang Zhenhuan Xu Bin Ren Nicu Sebe College of Computer Science and Technology Taiyuan University of Technology University of Pisa University of Trento

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker tracking plays a crucial role in various human-robot interaction applications. Recently, leveraging multimodal information, such as audio and visual signals, has become an important strategy for enhancing the robustness of the tracking system. However, current methods face challenges in effectively exploring the complementarity between audio and visual modalities. To this end, we propose an Audio-Visual Tracker based on Multi-Stage Multimodal Distillation (MSMD-AVT), which utilizes an audio-visual knowledge distillation framework to facilitate audio-visual information fusion over multiple stages progressively. MSMD-AVT is constructed based on an audio-visual teacher-student model incorporating three distinct distillation losses. During the feature extraction stage, the feature alignment distillation is designed to ensure that the feature representations from the student network remain consistent with the teacher encoding feature. Moreover, during the feature fusion stage, the fusion guidance distillation is proposed, using deep teacher features to guide the multimodal fusion process in the student network, optimizing the complementary benefits of audio-visual fusion. Finally, the logits distillation is applied during the position estimation stage to help the student model better capture localization features through knowledge transfer and output alignment. Additionally, we present a multimodal fusion module based on a bidirectional cross-attention mechanism in the student network, dynamically adjusting the effectiveness of different modal features for the tracking task by extracting complementary audio-visual contextual information. Extensive experimental results on the widely used AV16.3 dataset indicate that MSMD-AVT significantly outperforms existing state-of-the-art methods in terms of accuracy and robustness. Our code is publicly available at https://***/moyitech/MSMD-AVT.

关键词： Location awareness Knowledge engineering Visualization Estimation Human-robot interaction signal processing Feature extraction Robustness Speech processing Knowledge transfer

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Spatio-Temporal Domain-Aware Network for Skeleton-Based Action Representation Learning 27th

Spatio-Temporal Domain-Aware Network for Skeleton-Based Act...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Hu, Jiannan Wu, Cong Xu, Tianyang Wu, Xiao-Jun Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9783031781094

Considering its capability to extract implicit patterns from unlabeled data, contrastive learning has been widely employed in unsupervised skeleton-based action recognition. Spatio-temporal modeling is a key component in understanding skeleton sequence. However, existing methods often adopt rudimentary mechanisms or even completely overlook this aspect, leading to suboptimal performance in downstream tasks. In this paper, we propose a Spatio-Temporal Domain-Aware Network (STDA-Net). Firstly, the features extracted from the backbone extractor are further decoded into a triple-stream representation, corresponding to the spatial, temporal and global domains, respectively. Following that, an innovative approach named Triple Attention Transformer Module (TATM) is proposed to achieve customized spatio-temporal reasoning. TATM consists of three independent attention modules and a shared feedforward layer, thus achieving reasoning in different domains in a more efficient manner. Finally, domain-aware projectors are used to obtain richer spatio-temporal representations, providing a basis for the subsequent construction of inter-domain and intra-domain contrasts. Comprehensive experiments on NTU-RGB+D 60 &120 and PKU-MMD datasets demonstrate the superior performance of STDA-Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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