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共 18110 条记录，以下是261-270 订阅

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A Smart Tutor Avatar for Mimicking Characters of Bangla Sign Language

A Smart Tutor Avatar for Mimicking Characters of Bangla Sign...

引用

international conference on robotics,Electrical and signal processing Techniques (ICREST)

作者： Apu Islam Somaya Al Sadia Rahman Pratick Bhowmick Ishraque Arefin Rafi Sudipta Mondal Golam Rabiul Alam Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

A smart tutor avatar teaches Bangla Sign Language (BSL) by mimicking its 49 characters, pro-viding a visual and immersive learning experience to bridge communication gaps for the hearing-impaired. This research introduces a novel avatar system designed specifically for BSL, enhancing the learning process through interactive and engaging methods. The avatar uses machine learning and image processing to analyze hand gestures and replicate BSL signs, creating an interactive learning tool. Initial dataset preprocessing trains the system to produce accurate sign visuals. The user interface is intuitive, allowing learners to engage in physical exercises to improve sensorimotor skills, with immediate feedback provided via cosine similarity. While avatar tutors have been explored for American Sign Language, this study is pioneering in its approach to BSL. The proposed research has the potential to make BSL learning more accessible and enjoyable for a wider audience.

关键词： Hands Sign language Visualization Technological innovation Accuracy Avatars Image processing User interfaces signal processing robot sensing systems

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Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage

Shifting Spotlight for Co-supervision: A Simple yet Efficien...

引用

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

作者： Yang Hu Jinxia Zhang Kaihua Zhang Yin Yuan Jiale Huang Zechao Zhan Xin Wang Key Laboratory of Measurement and Control of CSE School of Automation Southeast University China Advanced Ocean Institute of Southeast University China School of Computer Science Nanjing University of Information Science and Technology China Alibaba Group China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, we propose the Co-Supervised Spotlight Shifting Network (CS 3 Net), a compact single-branch framework inspired by how shifting light source exposes camouflage. Our spotlight shifting strategy replaces multi-branch designs by generating supervisory signals that highlight boundary cues. Within CS 3 Net, a Projection Aware Attention (PAA) module is devised to strengthen feature extraction, while the Extended Neighbor Connection Decoder (ENCD) enhances final predictions. Extensive experiments on public datasets demonstrate that CS 3 Net not only achieves superior performance, but also reduces Multiply-Accumulate operations (MACs) by 32.13% compared to state-of-the-art COD methods, striking an optimal balance between efficiency and effectiveness.

关键词： Computer vision Image edge detection Object detection signal processing Feature extraction Acoustics Decoding Computational efficiency Speech processing Light sources

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Diagram Formalization Enhanced Multi-Modal Geometry Problem Solver

Diagram Formalization Enhanced Multi-Modal Geometry Problem ...

引用

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

作者： Zeren Zhang Jo-Ku Cheng Jingyang Deng Lu Tian Jinwen Ma Ziran Qin Xiaokai Zhang Na Zhu Tuo Leng School of Mathematical Sciences Peking University Beijing China 01.AI Beijing China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China School of Computer Engineering and Science Shanghai University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Mathematical reasoning remains an ongoing challenge for AI models, especially for geometry problems, which require both linguistic and visual signals. As the vision encoders of most MLLMs are trained on natural scenes, they often struggle to understand geometric diagrams, performing no better in geometry problem-solving than LLMs that only process text. This limitation is further amplified by the lack of effective methods for representing geometric relationships. To address these issues, we introduce the Diagram Formalization Enhanced Geometry Problem Solver (DFE-GPS), a new framework that integrates visual features, geometric formal language, and natural language representations. Specifically, we propose a novel synthetic data approach and construct a large-scale geometric dataset, SynthGeo228K, annotated with formal and natural language captions, designed to enhance the vision encoder to understand geometric structures better. Our framework improves MLLMs’ ability to process geometric diagrams and extends their application to open-ended tasks on the formalgeo7k dataset.

关键词： Geometry Visualization Pipelines Reinforcement learning signal processing Search problems Mathematical models Problem-solving Speech processing Synthetic data

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Bilevel Learning for Low-Light Image Enhancement and Detection

Bilevel Learning for Low-Light Image Enhancement and Detecti...

引用

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

作者： Wei Wang Bojian Song Xi Chen Yaxin Gao Weimin Lei School of Computer Science and Engineering Northeastern University Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China He University Shenyang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Object detection in low-light scenes is a challenging but widely discussed topic in computer vision. A common approach in low-light object detection involves employing cascaded architectures to connect enhancement and detection networks. This strategy aims to bridge the gap between low-light and normal-light images in both the image and feature domains, enabling the effective application of existing object detection networks to low-light scenarios. However, the cascade architecture ignores the intrinsic connection between low-light image enhancement and object detection tasks. To address this problem, we use a bilevel learning architecture to bridge these two tasks. Specifically, we take the parameters of the enhancement network as the main optimization objective and the parameters of the object detection network as the learnable constraints. In addition, to improve the practicality, we propose a method based on implicit function theory to approximate the solution of the corresponding gradient. Numerous experiments have demonstrated that our method significantly improves enhancement quality and detection accuracy.

关键词： Computer vision Accuracy Computational modeling Object detection Computer architecture Speech enhancement signal processing Feature extraction Image enhancement Optimization

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BrainChat: Interactive Semantic Information Decoding from fMRI Using Large-Scale vision-Language Pretrained Models

BrainChat: Interactive Semantic Information Decoding from fM...

引用

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

作者： Wanqiu Huang Ke Ma Tingyu Xie Hongwei Wang The College of Computer Science and Technology Zhejiang University Hangzhou China ZJUI Zhejiang University Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic information is crucial for human awareness. The ability to extract such information interactively from brain activity using non-invasive technologies like functional Magnetic Resonance Imaging (fMRI) is valuable for medical assistive technologies. However, research in this domain remains relatively limited. To address this gap, we proposes BrainChat, an interactive framework designed to decode semantic information from fMRI. BrainChat leverages a large-scale vision-language model, functioning through fMRI-based captioning and, optionally, question answering. First, a pair of fMRI encoder and decoder is trained to map fMRI data into a latent space representation using Masked Brain Modeling, a self-supervised approach. On the second stage, a projector is added to align these fMRI representations with both pretrained image and text embeddings, yielding a unified representation. A text decoder is also added at this stage, adopting cross-attention with the unified fMRI representation to guide the generation of semantic information. During this stage, the fMRI encoder, the projector, and the text decoder are trained together by minimizing a combined contrastive loss and caption loss. BrainChat achieves state-of-the-art performance in fMRI captioning and implements fMRI question answering, enabling interactive clinical applications. The code is available on Github 1 .

关键词： Training Semantics Functional magnetic resonance imaging signal processing Brain modeling Question answering (information retrieval) Decoding Data mining Speech processing Software development management

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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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Sample Efficient Reinforcement Learning via Large vision Language Model Distillation

Sample Efficient Reinforcement Learning via Large Vision Lan...

引用

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

作者： Donghoon Lee Tung M. Luu Younghwan Lee Chang D. Yoo Robotics Program KAIST Daejeon South Korea Electrical Engineering KAIST Daejeon South Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent research highlights the potential of multi-modal foundation models in tackling complex decision-making challenges. However, their large parameters make real-world deployment resource-intensive and often impractical for constrained systems. Reinforcement learning (RL) shows promise for task-specific agents but suffers from high sample complexity, limiting practical applications. To address these challenges, we introduce LVLM to Policy (LVLM2P), a novel framework that distills knowledge from large vision-language models (LVLM) into more efficient RL agents. Our approach leverages the LVLM as a teacher, providing instructional actions based on trajectories collected by the RL agent, which helps reduce less meaningful exploration in the early stages of learning, thereby significantly accelerating the agent’s learning progress. Additionally, by leveraging the LVLM to suggest actions directly from visual observations, we eliminate the need for manual textual descriptors of the environment, enhancing applicability across diverse tasks. Experiments show that LVLM2P significantly enhances the sample efficiency of baseline RL algorithms. The code is available at https://***/i22024/LVLM2P

关键词： Visualization Limiting Foundation models Decision making signal processing algorithms Reinforcement learning Cognition Trajectory Complexity theory Speech processing

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Flexible Event-Driven Biological Imaging via Bayesian Inference

Flexible Event-Driven Biological Imaging via Bayesian Infere...

引用

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

作者： Yayin Tan Tianle Zhao Zhiqin Chu Department of Electrical and Electronic Engineering the University of Hong Kong Department of Electronic Engineering The Chinese University of Hong Kong

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Event-driven imaging is gaining more and more attention in the field of biological imaging, where conventional frame-based imaging techniques confront with the bottleneck of capturing weak and fast-changing biological signals. However, existing methods for processing event camera signals, such as detection, de-noising, etc, are mainly designed for computer vision problems like tracking and surveillance and do not well adapts to biological applications. Particularly, a typical biological imaging system focuses on static scenes with fast-changing and weak illumination. There lacks effective detection algorithms under this extreme lighting condition. To this end, we proposed a novel framework for recovering the weak biological signals from the noisy event camera measurements. Based on Bayesian inference, the proposed approach offers a flexible solution for diverse imaging modalities. We validated our method on real-world biological imaging experiments, demonstrating its robustness and effectiveness of detecting weak and fast varying targets. This unlocks new possibilities for weak biological signal detection under extreme lighting conditions in a variety of imaging applications.

关键词： Surveillance Lighting signal processing algorithms Modulation Cameras Biology Inference algorithms Bayes methods Noise measurement Speech processing

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Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement

Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement

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

作者： Junyu Wang Zizhen Lin Tianrui Wang Meng Ge Longbiao Wang Jianwu Dang Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Electronic Information Sichuan University China Huiyan Technology (Tianjin) Co. Ltd Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent speech enhancement (SE) research, transformer and its variants have emerged as the predominant methodologies. However, the quadratic complexity of the self-attention mechanism imposes certain limitations on practical deployment. Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision due to its strong capabilities in modeling long sequences and relatively low computational complexity. In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks. By leveraging bidirectional Mamba to model forward and backward dependencies of speech signals at different resolutions, and incorporating skip connections to capture multi-scale information, our approach achieves state-of-the-art (SOTA) performance. Experimental results on the VCTK+DEMAND dataset indicate that Mamba-SEUNet attains a PESQ score of 3.59, while maintaining low computational complexity. When combined with the Perceptual Contrast Stretching technique, Mamba-SEUNet further improves the PESQ score to 3.73.

关键词： Computer vision Computational modeling Computer architecture Speech enhancement Transformers Natural language processing State-space methods Computational efficiency Computational complexity signal resolution

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KARST: Multi-Kernel Kronecker Adaptation with Re-Scaling Transmission for Visual Classification

KARST: Multi-Kernel Kronecker Adaptation with Re-Scaling Tra...

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

作者： Yue Zhu Haiwen Diao Shang Gao Long Chen Huchuan Lu Dalian University of Technology Dalian China The Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Fine-tuning pre-trained vision models for specific tasks is a common practice in computer vision. However, this process becomes more expensive and resource-intensive as models grow larger. Recently, parameter-efficient fine-tuning (PEFT) methods have emerged as a popular solution to improve training efficiency and reduce storage needs by tuning additional low-rank modules within pre-trained backbones. Despite their advantages, they struggle with limited representation capabilities and misalignment with pre-trained intermediate features. To address these issues, we introduce an innovative Multi-Kernel Kronecker Adaptation with Re-Scaling Transmission (KARST) for various recognition tasks. Specifically, KARST’s multi-kernel design extends Kronecker projections horizontally and separates adaptation matrices into multiple complementary spaces, reducing parameter dependency and creating more compact subspaces. Besides, it incorporates extra learnable re-scaling factors to better align with pre-trained feature distributions, allowing for more flexible and balanced feature aggregation. Extensive experiments on diverse downstream datasets validate that our KARST not only outperforms other PEFT counterparts across model types and data domains, but also surpasses full fine-tuning with a negligible inference cost due to its re-parameterization characteristics.

关键词： Training Adaptation models Visualization Computational modeling signal processing Performance gain Data models Speech processing Kernel Tuning

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