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DEP-SLAM: A Dynamic Environment Perception SLAM System with Large Language Models

DEP-SLAM: A Dynamic Environment Perception SLAM System with ...

引用

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

作者： Ying He E Richard Yu Fei Ma Ming Li Guang Zhou College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Information Technology Carleton University Ottawa ON Canada Guangdong Lab of Al and Digital Economy Shenzhen China DeepRoute Al Inc. Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Simultaneous localization and mapping (SLAM) is crucial for robot navigation and building environment maps in real time. Most visual SLAM (VSLAM) systems assume that objects in the environment are static. However, in highly dynamic environments where this assumption fails, the system’s efficiency can be seriously affected. Furthermore, current VSLAM systems are unable to interact with the environment or adapt their operating strategies to environmental changes. In this paper, we introduce DEP-SLAM, a dynamic environment perception SLAM system combined with large language models. The lightweight object detection network YOLOV7-Tiny is used to obtain semantic information. Large language models are used to dynamically perceive changes in the environment. To minimize manual hyper-parameter tuning, we propose an enhanced geometric constraint method to better filter out and eliminate dynamic feature points. Experimental results demonstrate the superiority of DEP-SLAM over existing schemes, especially in terms of accuracy and robustness in highly dynamic indoor environments.

关键词： Visualization Simultaneous localization and mapping Large language models Semantics signal processing Robustness Real-time systems Speech processing Tuning robots

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Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions

Mitigating Hallucinations on Object Attributes using Multivi...

引用

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

作者： Zhijie Tan Yuzhi Li Shengwei Meng Xiang Yuan Weiping Li Tong Mo Bingce Wang Xu Chu School of Software and Microelectronics Peking University Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current popular Large vision-Language Models (LVLMs) are suffering from Hallucinations on Object Attributes (HoOA), leading to incorrect determination of fine-grained attributes in the input images. Leveraging significant advancements in 3D generation from a single image, this paper proposes a novel method to mitigate HoOA in LVLMs. This method utilizes multiview images sampled from generated 3D representations as visual prompts for LVLMs, thereby providing more visual information from other viewpoints. Furthermore, we observe the input order of multiple multiview images significantly affects the performance of LVLMs. Consequently, we have devised Multiview Image Augmented VLM (MIAVLM), incorporating a Multiview Attributes Perceiver (MAP) submodule capable of simultaneously eliminating the influence of input image order and aligning visual information from multiview images with Large Language Models (LLMs). Besides, we designed and employed negative instructions to mitigate LVLMs’ bias towards "Yes" responses. Comprehensive experiments demonstrate the effectiveness of our method.

关键词： Visualization Three-dimensional displays Large language models signal processing Benchmark testing Acoustics Speech processing

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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...

引用

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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PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization

PromptTA: Prompt-driven Text Adapter for Source-free Domain ...

引用

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

作者： Haoran Zhang Shuanghao Bai Wanqi Zhou Jingwen Fu Badong Chen Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Source-free domain generalization (SFDG) tackles the challenge of adapting models to unseen target domains without access to source domain data. To deal with this challenging task, recent advances in SFDG have primarily focused on leveraging the text modality of vision-language models such as CLIP. These methods involve developing a transferable linear classifier based on diverse style features extracted from the text and learned prompts or deriving domain-unified text representations from domain banks. However, both style features and domain banks have limitations in capturing comprehensive domain knowledge. In this work, we propose Prompt-Driven Text Adapter (PromptTA) method, which is designed to better capture the distribution of style features and employ resampling to ensure thorough coverage of domain knowledge. To further leverage this rich domain information, we introduce a text adapter that learns from these style features for efficient domain information storage. Extensive experiments conducted on four benchmark datasets demonstrate that PromptTA achieves state-of-the-art performance. The code is available at https://***/zhanghr2001/PromptTA.

关键词： Adaptation models Codes Benchmark testing signal processing Feature extraction Data models Acoustics Speech processing

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Explainable Detection of Alzheimer’s Disease Through Analysis of Human Behavior in Video

Explainable Detection of Alzheimer’s Disease Through Analys...

引用

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

作者： Bao-Hsuan Huang Po-Chih Kuo Likai Huang Chaur-Jong Hu Cheng-Yu Chen Department of Computer Science National Tsing Hua University Hsinchu Taiwan Center and Department of Neurology Shuang-Ho Hospital New Taipei City Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diagnostic methods for Alzheimer’s disease (AD), such as brain imaging and cognitive impairment questionnaires, are costly and time-consuming, making early detection challenging. In this work, we develop a computer vision-based method to detect AD using behavioral data collected from the Timed Up and Go (TUG) test and the Cookie Theft (CT) picture description task. By analyzing body joints and facial landmarks through Convolutional Neural Networks and Support Vector Machines, we classified subjects into AD and Non-AD categories across four subtasks: Walking, Sit-Stand, Turning, and Describing. Our approach achieved an F1-score of 0.92±0.03, demonstrating the potential of video-based analysis for AD detection. To enhance the explainability of our model, we applied model explanation methods, identifying key features and symptoms involved in the decision-making process.

关键词： Support vector machines Neuroimaging Computational modeling Medical services signal processing Turning Behavioral sciences Alzheimer's disease Speech processing Medical diagnostic imaging

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Learning Hierarchical Attribute Prompt for vision-Language Models

Learning Hierarchical Attribute Prompt for Vision-Language M...

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

作者： Jun Liang Yang Peng Rui Luo Yunyu Zou Yalong Cheng Bingzhi Chen School of Artificial Intelligence South China Normal University Foshan China School of Artificial Intelligence Beijing Institute of Technology Zhuhai Zhuhai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Prompt learning is a common strategy for adapting Visual Language Models (VLMs) to downstream tasks by fine-tuning prompts for task-specific performance. However, existing methods face two key challenges: overfitting to base classes, which limits generalization to novel classes, and the dependence on manually generated or LLM-based descriptions, which are time-consuming and error-prone. To address these issues, we propose the Learning Hierarchical Attribute Prompt (LHAP) method, which introduces fine-grained semantic alignment through hierarchical prompts. By autonomously extracting visual attributes from images, LHAP generates local-level prompts (LLP) to capture fine-grained semantics and global-level prompts (GLP) to model overall semantics. The combination of LLP and GLP not only improves generalization but also mitigates errors and inefficiencies from manual or LLM-based descriptions. Extensive experiments on multiple benchmark datasets consistently demonstrate the superiority and robustness of LHAP over state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Focusing signal processing Benchmark testing Robustness Speech processing Faces Overfitting

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Impact of Glyph Information on Latent Space Diffusion Models for Accurate Handwritten Text Generation

Impact of Glyph Information on Latent Space Diffusion Models...

引用

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

作者： Ying-Li Lin Hao-Chung Cheng Chung-I Huang Chien-Yao Wang Jia-Ching Wang National Central University Taoyuan Taiwan National Center for High-Performance Computing Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The generation of high-quality stylized handwritten text images is a challenging task in computer vision and artificial intelligence. While advanced approaches using Latent Diffusion Models (LDMs) for generating stylized handwritten text have shown effectiveness, they often struggle with maintaining the structural integrity of certain characters, resulting in issues such as missing or extraneous strokes. In this work, we propose GlyphLDM, an innovative model that integrates glyph image information into both the diffusion and denoising processes in the latent space, enhancing the structural accuracy of generated text images. In the early training stages, our method demonstrated a significant improvement in the structural accuracy of the generated text images, with the Average Confidence Score increasing by approximately 40% compared to the baseline method. These experimental results indicate that incorporating glyph image information has promising potential to enhance the structural accuracy and overall quality of generated text images. This approach provides an effective solution for generating more accurate and diverse handwritten text images.

关键词： Training Measurement Handwriting recognition Accuracy Optical character recognition Noise reduction Diffusion processes signal processing Diffusion models Speech processing

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Exploring Graph-aware Reasoning and Bidirectional Selection for vision-Language Navigation

Exploring Graph-aware Reasoning and Bidirectional Selection ...

引用

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

作者： Dongming Zhou Jinsheng Deng Zhengbin Pang Wei Li School of Computer Science National University of Defense Technology Changsha China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha China School of Computer and Electronic Information Guangxi University Nanning China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Inspired by structured state space models and graph neural network modeling, we proposes a novel graph-aware reasoning (GAR) model to effectively solve the problem between memory utilization efficiency and reasoning navigation. First, we introduce graph networks into the navigation framework to enhance the model modeling ability for long sequence dependencies. It is integration graph neural networks into the state space to ensures that the agent can accurately find topological paths based on instructions and environmental interactions. Then, we design a bidirectional selective state space model to enhance the agent-aware of spatial information. When without relying on the attention network, we capture the context information in the image through position embedding. Finally, we fuison the features in the bidirectional state space and effectively compresses and improves the fine-grained image features by a residual connection methods. Our experimental results on R2R and REVERIE datasets show that GAR reduces GPU memory usage by 4.45% compared with the DUET baseline model.

关键词： Visualization Navigation Computational modeling Memory management signal processing Transformers Cognition Graph neural networks Speech processing Context modeling

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Diffusion-based Data Augmentation for Object Counting Problems

Diffusion-based Data Augmentation for Object Counting Proble...

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

作者： Zhen Wang Yuelei Li Jia Wan Nuno Vasconcelos Department of Electrical and Computer Engineering University of California San Diego Harbin Institute of Technology (Shenzhen)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Crowd counting, an important problem in computer vision, is commonly solved with deep learning approaches, such as convolutional networks and transformers. However, Deep networks often overfit when the available labeled crowd data is scarce. To overcome this, we have designed a pipeline that utilizes a diffusion model to generate extensive training data. We pioneer using diffusion models to generate images from high-density head location dot maps (a binary dot map that specifies the location of human heads) and are the first to use these diverse synthetic data to augment the crowd counting models. Our proposed smoothed density map input for ControlNet significantly improves ControlNet’s performance in generating crowds in the correct locations. Also, our proposed counting loss and guidance sampling for the diffusion model effectively minimize the discrepancies between the location dot map and the crowd images generated. Moreover, our versatile framework can be easily adapted to all kinds of counting problems. Extensive experiments demonstrate that our framework improves the counting performance on the ShanghaiTech, NWPU-Crowd, UCF-QNRF, and TRANCOS datasets.

关键词： Deep learning Head Pipelines Training data signal processing Diffusion models Transformers Data models Speech processing Synthetic data

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Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning

Semantic Hierarchical Prompt Tuning for Parameter-Efficient ...

引用

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

作者： Haowei Zhu Fangyuan Zhang Rui Qin Tianxiang Pan Junhai Yong Bin Wang School of Software Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As the scale of vision models continues to grow, Visual Prompt Timing (VPT) has emerged as a parameter-efficient transfer learning technique, noted for its superior performance compared to full fine-tuning. However, indiscriminately applying prompts to every layer without considering their inherent correlations, can cause significant disturbances, leading to suboptimal transferability. Additionally, VPT disrupts the original self-attention structure, affecting the aggregation of visual features, and lacks a mechanism for explicitly mining discriminative visual features, which are crucial for classification. To address these issues, we propose a Semantic Hierarchical Prompt (SHIP) fine-tuning strategy. We adaptively construct semantic hierarchies and use semantic-independent and semantic-shared prompts to learn hierarchical representations. We also integrate attribute prompts and a prompt matching loss to enhance feature discrimination and employ decoupled attention for robustness and reduced inference costs. SHIP significantly improves performance, achieving a 4.9% gain in accuracy over VPT with a ViT-B/16 backbone on VTAB-1k tasks. Our code is available at https://***/haoweiz23/SHIP.

关键词： Visualization Semantics Transfer learning signal processing Performance gain Robustness Timing Marine vehicles Speech processing Tuning

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