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Fully Auto-Regressive Multi-modal Large Language Model for Contextual Emotion Recognition

Fully Auto-Regressive Multi-modal Large Language Model for C...

引用

ieee Annual Ubiquitous Computing, Electronics & Mobile Communication conference (UEMCON)

作者： Hiroshi Nonaka Damian Valles Soka University of America Aliso Viejo USA Ingram School of Engineering Texas State University San Marcos USA

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

Large Language Models (LLMs) have gained popularity due to their high performance in natural language processing. This capability is underpinned by their ability to contextualize relations within text data. The application of LLMs also extends to multi-modal tasks, such as image captioning and video analysis. Research endeavors have been undertaken to leverage LLMs for emotion recognition tasks by utilizing visual, audio, and text data. Inspired by natural human approaches to emotion recognition, we propose a simple yet powerful, multi-modal LLM architecture for emotion recognition in conversations (ERC). Our proposed framework aims to contextualize the change of emotions better. We developed the Fully Auto-Regressive multi-modal LLM for Contextual Emotion Recognition (FARCER) based on this idea. This model consists of the instruction-tuned LLaMA3, a vision encoder, and linear layers that map visual embeddings to the LLM input space. FARCER significantly improved ERC benchmarks over the uni-modal LLaMA3-Instruct, although the LLM and vision encoder’s parameters remained frozen during training. Fine-tuned FARCER demonstrated high performance comparable to other state-of-the-art (SOTA) models, highlighting the potential of our context-focused design combined with conversational LLMs for ERC.

关键词： Training Emotion recognition visualization Large language models Benchmark testing Mobile communication Natural language processing Cognition Context modeling

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3D Near-field Fast Imaging Method for MMW MIMO Array Radar

3D Near-field Fast Imaging Method for MMW MIMO Array Radar

引用

Signal, Information and Data processing (ICSIDP), ieee International conference on

作者： Shuliang Gui Yue Li Hailiang Nie Zhiqin Zhao Luhong Fan Zengshan Tian Guangdong Institute of Electronic Information Engineering University of Electronic Science and Technology of China Dongguan China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

This paper proposes a three dimensional (3D) fast imaging algorithm for multiple inputs and multiple outputs (MIMO) array radar based on 3D wavenumber support domain approximation. Firstly, the MIMO array radar system and echo signal model are introduced and extended into the wavenumber support domain form. Then, the Born approximation theory of 3D wavenumber support domain of reference point for MIMO array is established to fast imaging. Based on that, the 3D fast imaging results with reference points of different sub-scenes are splicing and fusing to reconstruct the fine imaging result to eliminate the distortion and defocus caused by the approximation phase error. Finally, a real imaging experiments are conducted with a millimeter-wave MIMO array near field imaging system to demonstrate the performance and feasibility of the proposed algorithm.

关键词： Three-dimensional displays image resolution Filtering Splicing Imaging Metals Radar imaging Approximation algorithms Radar signal processing Signal resolution

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Generalizable Whole Slide image Classification with Fine-Grained visual-Semantic Interaction

Generalizable Whole Slide Image Classification with Fine-Gra...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Ying Chen Yifei Chen Rongshan Yu Wenxian Yang Liansheng Wang Bowen Ding Yuchen Han School of Informatics Xiamen University Huawei Aginome Scientific Xiamen China Department of Pathology Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Whole Slide image (WSI) classification is often formu-lated as a Multiple Instance Learning (MIL) problem. Re-cently, Vision-Language Models (VLMs) have demonstrated remarkable performance in WSI classification. However, existing methods leverage coarse-grained pathogenetic de-scriptions for visual representation supervision, which are insufficient to capture the complex visual appearance of pathogenetic images, hindering the generalizability of mod-els on diverse downstream tasks. Additionally, processing high-resolution WSIs can be computationally expensive. In this paper, we propose a novel “Fine-grained visual-Semantic Interaction” (FiVE) framework for WSI classi-fication. It is designed to enhance the model's general-izability by leveraging the interaction between localized visual patterns and fine-grained pathological semantics. Specifically, with meticulously designed queries, we start by utilizing a large language model to extract fine-grained pathological descriptions from various non-standardized raw reports. The output descriptions are then reconstructed into fine-grained labels used for training. By introducing a Task-specific Fine-grained Semantics (TFS) module, we enable prompts to capture crucial visual information in WSIs, which enhances representation learning and aug-ments generalization capabilities significantly. Further-more, given that pathological visual patterns are redun-dantly distributed across tissue slices, we sample a subset of visual instances during training. Our method demon-strates robust generalizability and strong transferability, dominantly outperforming the counterparts on the TCGA Lung Cancer dataset with at least 9.19% higher accu-racy in few-shot experiments. The code is available at: https://***/lslrius/WSI_FiVE.

关键词： Training Representation learning visualization Pathology Computational modeling Large language models Semantics

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Effectiveness of AI Techniques for the Classification of Alzheimer's Disease - A Review 3

Effectiveness of AI Techniques for the Classification of Alz...

引用

3rd International conference on Signal processing and Communication (ICPSC)

作者： Neethu, M. Jayasingh, Roopa J. Karunya Univ Dept ECE Coimbatore Tamil Nadu India

ISBN: (纸本)9781665428644

Alzheimer's is a disease affecting the brain by which the memory and related activities are being slowly and progressively diminishes. The classification of Alzheimer's disease is being done by the analysis of Magnetic Resonance images. Use of Artificial Intelligence techniques for this purpose is being under development all over the world. Various techniques are being developed for classifying Alzheimer's disease in the past two decades. Those related to Alzheimer's are studied in this research, in order to understand current developments and identify the future needs in this area of technology. Even though each technique possesses some inefficiency, all have certain advantages too. The accuracy of results shows a vibrant role in the field of medical science than any other engineering areas. According to this study, the development of new models by applying features of multiple techniques is required to overcome the current constraints.

关键词： Artificial Intelligence Alzheimer's disease Medical diagnosis image Classification Accuracy

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Mixed Global and Local Attention Alleviates Domain Shift Between Terahertz image Datasets

Mixed Global and Local Attention Alleviates Domain Shift Bet...

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ieee International conference on Signal processing, communications and Computing (ICSPCC)

作者： Rao Fu Shaoxing Cui Xiaoyi Feng School of Electronics and Information Northwestern Polytechnical University Xi'an China Faculty of Automation and Information Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Terahertz imaging technology has a broad application prospect in the field of security inspection due to its strong penetrability and negligible radiation effect on people. With the development of machine learning in recent years, the application of machine learning methods to security inspection can save labor costs and ensure the accuracy of long-time security inspection. However, terahertz imagers produce different bottom noise at different temperatures, which leads to a significant domain offset problem between data sets collected at different temperatures. This domain shift makes the model trained on the source domain dataset to have a significant error when applied to the target domain dataset. Therefore, how to overcome the temperature-induced domain offset is an urgent problem for terahertz imaging techniques. In this paper, we innovatively propose to apply the attention mechanism to solve the problem of domain bias in object detection. We propose a lightweight attention mechanism Mixed global and local attention(MGLA) aimed at mitigating the domain shift between the target and source domains without affecting the source domain detection effect. MGLA can take into account the local information while fusing the channel information and spatial information and global information to improve the representation of the network. The experiment results show a 10% improvement in mAP0.5 and a 6.3% improvement in mAP50:95 compared to YOLOV8 baseline.

关键词： Terahertz wave imaging Temperature distribution Radiation effects Attention mechanisms Noise Signal processing algorithms Object detection Inspection Security Faces

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Can Existing image Quality Metrics Assess Enhanced images? 4

Can Existing Image Quality Metrics Assess Enhanced Images?

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4th International conference on Information Communication and Signal processing, ICICSP 2021

作者： Huang, Gehang Liu, Xinwei HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

ISBN: (纸本)9781665407571

Most of image quality metrics are designed for degraded images, and their performance are highly correlated with the human visual system. The rapid development of image enhancement techniques raises a new issue: how to evaluate the performance of image enhancement methods? It is obvious that many benefits can be obtained if existing image quality metrics can be applied to the performance evaluation of image enhancement algorithms. However, quite a few studies have investigated such an issue. In this paper, we conduct psychophysical experiments to investigate the performance of existing image quality metrics on enhanced images. Two databases that specifically designed for enhanced images are selected in the experiments for the collection of subjective evaluation results. The objective evaluation results are obtained from six selected image quality metrics. By calculating the correlation between subjective and objective results we can answer the question: the performance of existing selected image quality metrics is low on enhanced images. © 2021 ieee.

关键词： image enhancement

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Edge-Assisted Lightweight Region-of-Interest Extraction and Transmission for Vehicle Perception

Edge-Assisted Lightweight Region-of-Interest Extraction and ...

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ieee conference on Global communications (GLOBECOM)

作者： Yan Cheng Peng Yang Ning Zhang Jiawei Hou School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Department of Electrical and Computer Engineering University of Windsor Windsor ON Canada

To enhance on-road environmental perception for autonomous driving, accurate and real-time analytics on high-resolution video frames generated from on-board cameras becomes crucial. In this paper, we design a lightweight object location method based on class activation mapping (CAM) to rapidly capture the region of interest (RoI) boxes that contain driving safety related objects from on-board cameras, which can not only improve the inference accuracy of vision tasks, but also reduce the amount of transmitted data. Considering the limited on-board computation resources, the RoI boxes extracted from the raw image are offloaded to the edge for further processing. Considering both the dynamics of vehicle-to-edge communications and the limited edge resources, we propose an adaptive RoI box offloading algorithm to ensure prompt and accurate inference by adjusting the down-sampling rate of each box. Extensive experimental results on four high-resolution video streams demonstrate that our approach can effectively improve the overall accuracy by up to 16 % and reduce the transmission demand by up to 49%, compared with other benchmarks.

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ODVISTA: An Omnidirectional Video Dataset for Super-Resolution and Quality Enhancement Tasks

ODVISTA: An Omnidirectional Video Dataset for Super-Resoluti...

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ieee International conference on image processing

作者： Ahmed Telili Ibrahim Farhat Wassim Hamidouche Hadi Amirpour Technology Innovation Institute Masdar City Abu Dhabi UAE Christian Doppler Laboratory ATHENA Alpen-Adria-Universität Klagenfurt Austria

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Omnidirectional or 360-degree video is being increasingly deployed, largely due to the latest advancements in immersive virtual reality (VR) and extended reality (XR) technology. However, the adoption of these videos in streaming encounters challenges related to bandwidth and latency, particularly in mobility conditions such as with unmanned aerial vehicles (UAVs). Adaptive resolution and compression aim to preserve quality while maintaining low latency under these constraints, yet downscaling and encoding can still degrade quality and introduce artifacts. Machine learning (ML)-based super-resolution (SR) and quality enhancement techniques offer a promising solution by enhancing detail recovery and reducing compression artifacts. However, current publicly available 360-degree video SR datasets lack compression artifacts, which limit research in this field. To bridge this gap, this paper introduces omnidirectional video streaming dataset (ODVista), which comprises 200 high-resolution and high-quality videos downscaled and encoded at four bitrate ranges using the high-efficiency video coding (HEVC)/H.265 standard. Evaluations show that the dataset not only features a wide variety of scenes but also spans different levels of content complexity, which is crucial for robust solutions that perform well in real-world scenarios and generalize across diverse visual environments. Additionally, we evaluate the performance, considering both quality enhancement and runtime, of two handcrafted and two ML-based SR models on the validation and testing sets of ODVista. Dataset URL: https://***/Omnidirectional-video-group/ODVista

关键词： Training visualization image coding Extended reality Superresolution Bandwidth Streaming media

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MiKey: Human Key-points Detection Using Millimeter Wave Radar

MiKey: Human Key-points Detection Using Millimeter Wave Rada...

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ieee conference on Wireless communications and Networking

作者： Lili Xie Hongchun Li Jun Tian Qian Zhao Masahiro Shiraishi Kenta Ide Takahiro Yoshioka Takeshi Konno Fujitsu Research & Development Center Co. Ltd. Beijing China Fujitsu Limited Kawasaki Japan

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Human key-points play a vital role in smart home, elderly-care, gaming, etc. The detection methods based on millimeter wave (mmWave) radars have attracted substantial attentions and been applied in various fields because of the contactless and no privacyinvasion characteristics. In this paper, we present a machining learning-based framework, Mikey, that utilizes the commercial frequency-modulated continuous-wave (FMCW) radar point cloud to estimate the key-points. The framework consists of three steps, including point cloud adaptive merging pre-processing, key-point detection and key-point post-processing. The point cloud adaptive merging pre-processing method not only addressed the challenges stemming from the point sparsity and specular reflection but also the individual and action differences. A point cloud set is constructed adaptively from consecutive multiple frames limited by both frame and point numbers. The detection model combines the temporal and spatial features by adopting a local feature encoding block and a global self-attention block. A key-point post-processing step is added to smooth the key-point predictions and remove the jagged edge by exploring the prediction coherence in consecutive frames. We also make up for the scarcity of mmWave radar dataset for a variety of targets and actions. A dataset with 88 participants for 8 different actions is collected. We evaluate the proposed framework using additional 5 participants and obtain an average mean absolute error smaller than 6cm, confirming the effectiveness of the proposed framework.

关键词： Point cloud compression Adaptation models image edge detection Merging Radar detection Machining Smart homes

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Comparative Study between Different image Fusion Techniques Applied on Biomedical images 9

Comparative Study between Different Image Fusion Techniques ...

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9th International Japan-Africa conference on Electronics, communications, and Computations, JAC-ECC 2021

作者： Ghandour, C. El-Shafai, Walid El-Rabaie, S. Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9781665482929

The significant area in varied image processing applications is medical image fusion. In recent years, more efforts, advancement has been made on expanding image fusion algorithms when there is a scarcity of benchmark and code library that can scale the state-of-the-art works. In this research, different image fusion algorithms are implemented on various medical images. A medical image fusion benchmark (MFB) that includes numerous evaluation metrics and fusion algorithms code library is exhibited. Furthermore, within the benchmark, inclusive experiments are also carried out to realize this performance, effective algorithms for powerful image fusion are distinguished by resolving quantitative and qualitative outcomes, and some observances on the states and future likelihoods of this field are granted. © 2021 ieee.

关键词： image fusion

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