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MMHS: Multimodal Model for Hate Speech Intensity Prediction 26th

MMHS: Multimodal Model for Hate Speech Intensity Prediction

引用

26th International conference on Speech and Computer

作者： Goel, Aman Poswal, Abhishek Carnegie Mellon Univ 5000 Forbes Ave Pittsburgh PA 15213 USA Hong Kong Univ Sci & Technol Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783031780134;9783031780141

This paper presents a novel multimodal model that integrates both image and large language model capabilities to enhance hate intensity prediction, traditionally a purely text-based task. Accurately assessing hate speech intensity is crucial for moderating and regulating vast online communities by normalizing hate speech [1], while ensuring a balance between free expression and responsible communication. Our approach leverages insights from both the visual and language domains, resulting in the novel Multimodal Model for Hate Speech (MMHS). We demonstrate that MMHS achieves state-of-the-art performance on the NACL dataset, surpassing previous benchmarks by scoring 0.350 lower in Root Mean Squared Error (RMSE) and 0.132, and 0.012 higher in Pearson, and Cosine metrics, respectively. Additionally, user preference surveys indicate a significant favoring of our predictions over those of Masud et al. [1] by 16.67%. This work advances the technical landscape of hate speech detection and enriches our understanding of online discourse, enabling more effective moderation strategies. (Disclaimer: This paper includes examples of hate speech which contain some profane words. These examples are only included for contextual understanding. We tried our best to censor vulgar, offensive, or hateful words. We assert that we do not support these views in any way.)

关键词： Online hate speech Multimodal Hate intensity information fusion

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Unlocking Eye Gestures with Earable Inertial Sensing for Accessible HCI

Unlocking Eye Gestures with Earable Inertial Sensing for Acc...

引用

International communication Systems and Networks and Workshops, COMSNETS

作者： Garvit Chugh Suchetana Chakraborty Sandip Chakraborty Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Eye gestures are widely used in many applications, including device control, biometrics, visual analytics, and health-care, like Alzheimer’s, accessibility, etc. The conventional method for eye gesture detection needs visual information processing devices or smart glasses to capture the movement patterns of the eyeballs. In this paper, we present a novel framework that utilizes inertial measurement unit (IMU) sensors embedded in earable devices for real-time eye gesture detection, which can work as a complementary to visual information processing devices. The framework translates specific eye movements into actionable commands, enabling hands-free interaction for users with limited mobility. Through extensive experimentation with 14 participants across diverse activities such as sitting, walking, running, and driving, the framework demonstrated high accuracy in eye gesture recognition, achieving a macro F1 score of 0.85 on our self-collected dataset.

关键词： Legged locomotion Vocabulary Measurement units visual analytics information processing Gesture recognition Real-time systems Research and development Intelligent sensors Smart glasses

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Transfer Learning Based Fusion of CT and MR Images

Transfer Learning Based Fusion of CT and MR Images

引用

International conference on Signal processing and communication (ICSC)

作者： Pragya Gupta Nishant Jain Department of Electronics and Communication Engineering Jaypee University of Information Technology Waknaghat Solan Himachal Pradesh India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

This paper presents a method for fusing MR and CT images to create a high-contrast fused image that integrates complementary information from both input images. The proposed approach employs wavelet transform to decompose the CT and MR images. Next, features are extracted from the decomposed images by applying transfer learning from the VGG19 model. The fusion of these decomposed images is performed based on the extracted features and the fusion rules outlined in this study. An inverse wavelet transformation is then applied to the fused decomposed images, resulting in a final fused image that encompasses information from both the CT and MR images. The effectiveness of the proposed method is evaluated and compared with five other advanced fusion techniques through both visual assessment and objective measures.

关键词： Wavelet transforms visualization Statistical analysis Hospitals Computed tomography Transfer learning Signal processing Feature extraction Convolutional neural networks Image fusion

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Deep Learning Technique for Computer Vision-Based Pose Estimation for Augmented Reality 3rd

Deep Learning Technique for Computer Vision-Based Pose Estim...

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3rd International conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Palanimeera, J. Ponmozhi, K. Jyothi, Kanagaraj Dargar, Shashi Kant Birla, Shilpi Department of Computer Applications Sathyabama Institute of Science and Technology Chennai India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Biotechnology Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819784288

Pose estimation approaches of open pose were suggested. Pose estimation in real-time is a difficult research subject with many computer vision applications. Pose estimation applications are Human Activity Estimation, Motion Transfer and Augmented Reality, Training Robots, Motion Tracking for Consoles, and Human Fall Detection. This paper describes the Augmented Reality (AR) of pose estimation. AR is an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli delivered via technology. One interesting use for obtaining the Open-Pose discovered important locations in each pose is in the field of augmented reality. A technique for 3D model overlays on an image using pose key point information that is presented in this study. To extract crucial information from the data, the fully convolutional networks deep learning technique is used. The technique used in this work comprises the real-time intersection of the picture plane with the intended 3D model and accurate model orientation based on the key point data. The performance will greatly depend on the Graphics processing Unit specifications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Generation of Scene Graph and Semantic Image: A Review and Challenge Ahead

Generation of Scene Graph and Semantic Image: A Review and C...

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International conference on Artificial Intelligence in information and communication (ICAiiC)

作者： Shao-Kai Hsieh Huey-Ing Liu Department of Electrical Engineering Fu Jen Catholic University New Taipei City Taiwan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Scene graph generation creates a structured representation of visual scenes by identifying objects and their attributes and the relationships between them. Conversely, semantic image generation converts semantic representations such as scene graphs, textual descriptions, or object layouts, into photorealistic images. This paper provides an overview of recent advancements in generations of scene graph and semantic image. The survey explores how innovations in both fields can complement each other, paving the way for advanced applications in autonomous systems, semantic communication, visual reasoning, and creative industries. Challenges such as data imbalance, scalability, and semantic coherence and future research directions including multimodal integration, prior processing etc. are throughly discussed.

关键词： Surveys visualization Technological innovation Image synthesis Reviews Scalability Semantics Semantic communication Artificial intelligence Photorealistic images

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Towards Unified Neural Decoding of Perceived, Spoken and Imagined Speech from EEG Signals 13

Towards Unified Neural Decoding of Perceived, Spoken and Ima...

引用

13th International Winter conference on Brain-Computer Interface, BCI 2025

作者： Lee, Jung-Sun Jo, Ha-Na Lee, Seo-Hyun Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Brain signals accompany various information relevant to human actions and mental imagery, making them crucial to interpreting and understanding human intentions. Brain-computer interface technology leverages this brain activity to generate external commands for controlling the environment, offering critical advantages to individuals with paralysis or locked-in syndrome. Within the brain-computer interface domain, brain-to-speech research has gained attention, focusing on the direct synthesis of audible speech from brain signals. Most current studies decode speech from brain activity using invasive techniques and emphasize spoken speech data. However, humans express various speech states, and distinguishing these states through non-invasive approaches remains a significant yet challenging task. This research investigated the effectiveness of deep learning models for non-invasive-based neural signal decoding, with an emphasis on distinguishing between different speech paradigms, including perceived, overt, whispered, and imagined speech, across multiple frequency bands. The model utilizing the spatial conventional neural network module demonstrated superior performance compared to other models, especially in the gamma band. Additionally, imagined speech in the theta frequency band, where deep learning also showed strong effects, exhibited statistically significant differences compared to the other speech paradigms. © 2025 IEEE.

关键词： Audio signal processing

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ALIC: Adaptive Fusion Entropy Model for Learned Image Compression

ALIC: Adaptive Fusion Entropy Model for Learned Image Compre...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Lingxue Liu, Meiqin Zhang, Yifan Tang, Qi Yao, Chao Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing China Visual Intelligence + X International Cooperation Joint Laboratory of MOE Beijing Jiaotong University China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350368741

Recently, learned image compression algorithms have achieved significant performance. The entropy model is crucial for improving the rate-distortion performance by estimating the probability distribution of latent representation. In this paper, we propose an adaptive fusion entropy model for learned image compression (ALIC). To explore the correlation between channel and global spatial features, an adaptive fusion entropy model (AFEM) is designed. AFEM first slices the latent representation along the channels and leverages the adaptive channel fusion context module (ACFC) to capture correlations between the decoded and current slices. Subsequently, AFEM uses the adaptive spatial fusion context module (ASFC) to further divide the current slice into encoding pixels and reference pixels, thus improving the accuracy of probability estimation. The attention map and modulation parameter are introduced in ACFC and ASFC to interact with channel and spatial features. In addition, the variable-rate residual transformer (VResFormer) is proposed to control dynamic bit-rate by selectively modulating the high-frequency component according to coefficient weight and bias. Experimental results indicate that our ALIC outperforms other learned image compression algorithms. Our ALIC saves 5.89% bit-rate compared with VVC (4:4:4) on Kodak dataset. © 2025 IEEE.

关键词： Entropy model Learned image compression Variable-rate

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Refiner: Fine-grained Cross-modal Concepts Refinement for Compositional Zero-Shot Learning

Refiner: Fine-grained Cross-modal Concepts Refinement for Co...

引用

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

作者： Xiao Zhang Haodong Jing Hui Chen Yongqiang Ma Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent Compositional Zero-Shot Learning (CZSL) methods increasingly adopt the pre-trained vision-language models to capture the contextual relations between image and text spaces. However, the single-class-token design from Transformer-based encoder inevitably captures contextual information from unrelated objects and background, thus hindering the modeling of fine-grained class-specific visual features. Suffering from cross-modal gap, prior methods also struggle to improve compositional recognition performance. To address these issues, we propose a fine-grained cross-modal concepts refinement framework, termed as Refiner, which comprises two pivotal components: (i) the fine-grained concepts refinement of image embeddings to capture state-object context within visual scenes, and (ii) the cross-modal information fusion to mitigate the modality gap. By leveraging learnable query vectors to capture region-specific semantic information pertinent to composition labels, our approach refines visual representations with fine-grained state-object context information. As for cross-modal information fusion, we construct a robust image-to-text mapping by aligning visual embeddings with states, objects, and compositions, respectively. Extensive experiments demonstrate that our Refiner achieves new state-of-the-art performance across all popular benchmarks in both closed- and open-world settings.

关键词： visualization Zero shot learning Semantics Benchmark testing Signal processing Transformers Vectors Acoustics Speech processing Context modeling

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MixSense : Mixture of Vision Sense

MixSense : Mixture of Vision Sense

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

作者： Jian Lin Zhuoran Wang Qibo Qiu Jianzhong Chen Zixian Ge Weizhong Jin Yuchao Yan Li Yu Research & Innovation Institute China Mobile (Zhejiang) Hangzhou China School of Computing and Information University of Pittsburgh Pittsburgh The United States

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

It is a new trend to fine-tune Large Multimodal Models (LMMs) to adapt to specific visual tasks through task-related conversation data. This approach provides a new paradigm for solving various vision-language tasks, however, it still faces two problems: (1) the global visual features input to the backbone Large Language Model (LLM) lack the focus on task-specific information, which may lead a sub-optimal performance on specific visual tasks; (2) The hallucinations on specific tasks are underestimated, which makes the model prone to factual errors during inference. In order to solve these two problems, we propose MixSense, a new LMM paradigm that aligns the visual features of specilized models and general models to the backbone LLM together, increasing the focus on specific task information while maintaining the vanilla capabilities of the model; evaluated and fine-tuned with the constructed task-specific negative samples, we can further assess the degree of hallucinations of the model on specific visual tasks and reduce it. We validate our method on a popular Object Detection (OD) task, Referring Expression Comprehension (REC), and extensive experiments demonstrate the effectiveness of our proposal.

关键词： Training visualization Pipelines Oral communication Object detection Signal processing Lead Market research Proposals Speech processing

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Towards Dynamic Neural communication and Speech Neuroprosthesis Based on Viseme Decoding

Towards Dynamic Neural Communication and Speech Neuroprosthe...

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

作者： Ji-Ha Park Seo-Hyun Lee Soowon Kim Seong-Whan Lee Dept. of Artificial Intelligence Korea University Seoul Republic of Korea Dept. of Brain and Cognitive Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Decoding text, speech, or images from human neural signals holds promising potential both as neuroprosthesis for patients and as innovative communication tools for general users. Although neural signals contain various information on speech intentions, movements, and phonetic details, generating informative outputs from them remains challenging, with mostly focusing on decoding short intentions or producing fragmented outputs. In this study, we developed a diffusion model-based framework to decode visual speech intentions from speech-related non-invasive brain signals, to facilitate face-to-face neural communication. We designed an experiment to consolidate various phonemes to train visemes of each phoneme, aiming to learn the representation of corresponding lip formations from neural signals. By decoding visemes from both isolated trials and continuous sentences, we successfully reconstructed coherent lip movements, effectively bridging the gap between brain signals and dynamic visual interfaces. The results highlight the potential of viseme decoding and talking face reconstruction from human neural signals, marking a significant step toward dynamic neural communication systems and speech neuroprosthesis for patients.

关键词： visualization communication systems Lips Neuroprostheses Signal processing Phonetics Brain modeling Decoding Speech processing Image reconstruction

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