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Active Visual Learning for robots with Dueling Deep Q-Networks and Transformer Encoders

Active Visual Learning for Robots with Dueling Deep Q-Networ...

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

作者： Hongliang Zeng Ping Zhang Fang Li Qinpeng Yi Jiahua Wang Tingyu Ye South China University of Technology Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Active vision learning aims to develop intelligent systems capable of actively exploring and understanding their surroundings to optimize detection performance. Although current research has begun to explore how reinforcement learning can drive robots to actively perceive their environment, it often overlooks the critical role of integrating historical contextual information. To address this, we propose an innovative approach to active vision learning, designed to enhance target detection performance in unknown environments. Specifically, this method combines reinforcement learning with deep neural network techniques and cleverly designs a sliding window mechanism to integrate observations over multiple steps into the state input. We employ PointNet++ for feature extraction and utilize a Transformer encoder module to process spatial contextual information, thereby constructing a comprehensive representation of the robot’s environment. Additionally, our carefully designed reward mechanism encourages the robot to prioritize the exploration of diverse object categories. With these reward strategies, we achieve significant improvements in detection accuracy and sampling efficiency. We validated our approach on the real-world 3D dataset R3ED, and the results demonstrate that our method outperforms other baseline methods in terms of performance.

关键词： Visualization Three-dimensional displays Large language models Reinforcement learning Object detection Artificial neural networks signal processing Transformers Speech processing robots

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Artificial Intelligence Algorithms for robotic Harvesting of Agricultural Produce 1

Artificial Intelligence Algorithms for Robotic Harvesting of...

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1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Kolhalkar, Nilesh R. Pandit, Anupama A. Kedar, Shridhar Ashok Yedukondalu, G. MKSSS's Cummins College of Engg. For Women Department of Mechanical Engineering MH Pune India Department of Computer Science & Engg. Pune MH Pune India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Mechanical Engineering A.P. Vijayawada India

ISBN: (纸本)9798350355611

robotic harvesting of fruits and vegetables is an advanced technology that leverages robotics, Artificial Intelligence, and Machine vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. Machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations. © 2025 IEEE.

关键词： Machine vision

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CPSNet: Comprehensive Enhancement Representation for Polyp Segmentation Task

CPSNet: Comprehensive Enhancement Representation for Polyp S...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Cai, Jiati Liu, Xiaogang Yang, Hongjie Ding, Yi Zhong, Ting Qin, Zhen Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Department of Gastroenterology and Hepatology University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Accurately segmenting polyp regions in colonoscopy images is crucial for the diagnosis and intervention of colorectal cancer. However, the task of polyp segmentation remains challenging due to the diverse size and shape variations among polyps, their extreme similarity to the background, and frequent rotation of the lens, which further increases the diversity in polyp presentation. To address these challenges effectively, we propose a comprehensive polyp segmentation network (CPSNet). Specifically, we introduce a Comprehensive Spatial Feature Extraction Module (CFEM) that progressively and densely integrates features while forming receptive windows with various shapes. This enables enhanced perception of polyps at manifold sizes and shapes. Additionally, we propose a Fine-grained Region Strengthen Module (FGSM) to supplement uncertain areas around polyps by mitigating background noise interference. In terms of training strategy, we further introduce a Rotation-augmented Constrained Loss (RC Loss), which reinforces consistency constraints on polyp images under multiple rotation angles. Qualitative and quantitative experiments conducted on five public datasets demonstrate both the plug-and-play capability of CFEM as well as the effectiveness and excellence achieved by CPSNet. © 2025 IEEE.

关键词： colonoscopy computer vision polyp segmentation representation enhancement

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Dual-Triple Transformer Networks for Accurate CT Pleural Effusion Segmentation

Dual-Triple Transformer Networks for Accurate CT Pleural Eff...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yang, Jianwei Fan, Wenkang Fang, Hao Zhu, Zirui Luo, Xiongbiao Department of Computer Science and Technology Xiamen University Xiamen361102 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361102 China Zhongshan Hospital Xiamen University Xiamen361004 China

ISBN: (纸本)9798350368741

Pleural effusion segmentation in computed tomography images is essential to its precise diagnosis and treatment but remains challenging due to blurred boundaries, heterogeneous morphology, and low contrast with adjacent anatomical structures. This work shows a first study on pleural effusion segmentation by introducing a new deep learning architecture of dual-triple transformer networks. Specifically, this architecture builds a dual encoder of swin transformer and 3D deformable convolution, leveraging the multiscale representation capability to capture global contextual information and model complex deformations. Moreover, a triple decoder with a fusion module, a boundary-awareness mechanism, and a transposed-residual convolution block is introduced to effectively propagate these global and local features and refine the segmentation by mitigating ambiguity at the interfaces with surrounding tissues. We validate our method on 143 chest computed tomography scans. The experimental results demonstrate that our proposed model significantly outperforms state-of-the-art segmentation approaches, greatly improving the dice similarity coefficient and mean intersection over union while critically reducing both Hausdorff distance 95 and average symmetric surface distance. © 2025 IEEE.

关键词： Boundary Awareness Dual Encoder Image Segmentation Pleural Effusion vision Transformer

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Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images

Prompting DirectSAM for Semantic Contour Extraction in Remot...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Miao, Shiyu Chen, Delong Liu, Fan Zhang, Chuanyi Gu, Yanhui Guo, Shengjie Zhou, Jun College of Computer Science and Software Engineering Hohai University Nanjing China Hong Kong University of Science and Technology Hong Kong School of Computer and Electronic Information Nanjing Normal University Nanjing China School of Information and Communication Technology Griffith University QLD Australia

ISBN: (纸本)9798350368741

The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where semantic contour extraction-such as identifying buildings, road networks, and coastlines-holds significant practical value. Those applications are currently handled via training specialized small models separately on small datasets in each domain. We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which not only inherits the strong segmentation capability acquired from natural images, but also benefits from a large-scale dataset we created for remote sensing semantic contour extraction. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. DirectSAM-RS integrates a prompter module: a text encoder and cross-attention layers attached to the DirectSAM architecture, which allows flexible conditioning on target class labels or referring expressions. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks. © 2025 IEEE.

关键词： Remote sensing Semantic contour extraction vision-language learning

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Semantic-oriented Visual Prompt Learning for Class Incremental Learning

Semantic-oriented Visual Prompt Learning for Class Increment...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Guo, Shuai Gu, Yang Ma, Yuan Zhang, Yingwei Weng, Weining Liu, Jun Dai, Weiwei Chen, Yiqiang Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Digital Ophthalmology and Visual Science Changsha Aier Eye Hospital Changsha China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350368741

Class-incremental learning (CIL) enables models to continuously learn new classes while addressing catastrophic forgetting. With the introduction of pre-trained models, new tuning paradigms have emerged for CIL. This paper revisits parameter-efficient fine-tuning (PEFT) methods in the context of incremental learning. Prior studies reveal that PEFT methods' extended parameters do not directly contribute to semantic perception, limiting performance with significant category and domain gaps. To address this, we propose semantic-oriented visual prompt learning (SVPL), which enhances semantic perception and improves task-specific knowledge extraction. SVPL assigns learnable prompts to each class, using a contrastive group alignment to align prompts to task-specific semantic spaces, thus preserving relationships between old and new knowledge. Additionally, hierarchical semantic delivery allows the semantic transformation of prompt groups from shallow to deep layers to facilitate efficient knowledge mining and enable effective learning of new knowledge. Extensive experimental results on five benchmarks demonstrate the superior performance of our methods. © 2025 IEEE.

关键词： catastrophic forgetting class incremental learning pre-trained model prompt learning vision transformer

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QSVG: Quaternion vision Graph Neural Network with Structure Inference for Texture Classification 16

QSVG: Quaternion Vision Graph Neural Network with Structure ...

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16th international conference on signal processing Systems, ICSPS 2024

作者： Wen, Haiyuan Huang, Guoheng Cheng, Lianglun Liao, Xianglian Wu, Shiting Yuan, Xiaochen Zhong, Guo School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China Faculty of Applied Sciences Macao Polytechnic University China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510006 China

ISBN: (纸本)9781510689251

In the realm of texture classification, while traditional approaches like Bag of Words (BoW) and Convolutional Neural Networks (CNNs) are commonly used, recent research highlights the remarkable flexibility offered by Graph Neural Networks (GNNs). Specifically, GNNs can effectively represent image blocks as one-dimensional multi-channel node features from three-dimensional data, interconnecting node feature channels. However, existing methods tend to process channels separately during graph convolution, overlooking critical inter-channel connections that are integral to capture intricate relationships between texture features, potentially limiting the effectiveness of GNNs in texture classification. Moreover, textures contain vital global information essential for classification performance, but GNNs tend to focus on information from similar neighboring nodes, which can restrict their capability to capture the global feature of the entire image during data processing. To address the aforementioned challenges, we propose a novel GNN architecture called Quaternion Structure inference vision Graph neural network (QSVG) for texture classification, which combines quaternions and structure inference. Unlike previous approaches, QSVG comprises two key components: first, Quaternion Max-related convolution (Qmax), which uses the Hamilton transform to enhance node characterization by exploiting inter-channel connections;second, Structure Inference Block (SIF Block), which enriches node information through Bidirectional Gated Recurrent Unit (Bi-GRU)-based structure inference, fusing graph neural network node features with global features. Experimental results on three standard texture datasets demonstrate the competitive classification performance of our method. © 2025 SPIE.

关键词： Graph neural networks

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PETer: A lightweight human action recognition network based on millimeter-wave radar sparse point clouds 16

PETer: A lightweight human action recognition network based ...

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16th international conference on signal processing Systems, ICSPS 2024

作者： Sun, Kangsheng Jin, Biao Cai, Yan Li, Rongmin Jiangsu University of Science and Technology Ocean College Zhenjiang China Suzhou Zadar Vision Technology Co. Ltd. Suzhou China

ISBN: (纸本)9781510689251

Millimeter-wave radar, with its excellent environmental adaptability, high resolution, strong penetration capability, and privacy protection, is widely used in smart homes, elderly care, and security monitoring. However, due to the high sparsity of millimeter-wave point cloud data, it carries less information compared to video or LiDAR, posing significant challenges for precise and rapid human action recognition. This paper proposes a lightweight long-sequence spatiotemporal edge convolution Transformer network (PETer). The model first enhances the data, then separates the spatial and temporal information of the point cloud sequence, utilizes EdgeConv to capture the spatial geometric structure information of single-frame point clouds, and employs a Transformer to extract temporal features from the multi-frame spatial information. To validate the effectiveness of the model, this paper uses the IWR1443-ISK millimeter-wave radar as the detection sensor to capture human actions and generate a 3D point cloud dataset. This dataset covers 12 types of human actions from 7 volunteers, including walking, waving, boxing, and standing. The proposed model achieves an average accuracy of 98.50% on the dataset and 100% accuracy on the MARS human skeleton dataset. Compared to various point cloud networks, the PETer network is the smallest in size, fastest in speed, and highest in accuracy, demonstrating its superior performance in millimeter-wave radar human action recognition tasks. © 2025 SPIE.

关键词： Musculoskeletal system

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Facial Deblurring and Recognition Using Image processing and Machine Learning Techniques 8th

Facial Deblurring and Recognition Using Image Processing and...

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8th IFIP TC 12 international conference on Computer, Communication and signal processing, ICCCSP 2024

作者： Subrahmanyam, B.R. Janavi, T.S. Keerthana, V. Serajudeen, Ayesha Jumana Arunnagiri, A.M. Department of Electronics and Communication Engineering College of Engineering Guindy Anna University Chennai600025 India

ISBN: (纸本)9783031736162

The project addresses the challenge of accurately identifying blurred faces in computer vision and facial recognition. It introduces a novel framework that integrates deblurring techniques, utilizing point spread function deconvolution to enhance facial image quality. Principal Component Analysis (PCA) is employed for feature extraction, and a K-Nearest Neighbors (KNN) classifier is applied for face identification. The combined deblurring and PCA-transformed features improve matching and identification accuracy, particularly in scenarios with initially blurred images. Experimental validation on a real-world dataset demonstrates the efficacy of the proposed methodology. This approach not only enhances facial recognition accuracy but also lays the groundwork for future research in challenging practical applications, such as security and law enforcement. © IFIP international Federation for Information processing 2025.

关键词： Optical transfer function

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Integrating Failures in robot Skill Acquisition with Offline Action-Sequence Diffusion RL

Integrating Failures in Robot Skill Acquisition with Offline...

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

作者： Hecheng Wang Lizhe Qi Yunquan Sun Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advancements in robot learning leverage large language models (LLMs) and sampling-based task and motion planning (TAMP) modules for automatic and scalable robot data generation. This method yields both success trajectories and a large number of failure trajectories. Prior works typically filter out failure data and adopt behavior cloning (BC) to train policy. However, this significantly reduces the sample efficiency of the method and results in a policy limited by the data collection behavior policy. In this paper, we introduce a vision-language-conditioned action-sequence diffusion policy and an action-sequence diffusion policy learning with Q-guided refinement for its training. We first redefine the reverse process of the diffusion model as the distribution of action sequences conditioned on visual observations and language instructions. We then employ BC to pretrain the policy on the success sub-dataset. Next, we optimize the action-sequence Q-value function by minimizing the temporal difference error across the complete dataset. Finally, we integrate guidance from the Q-value function into the BC loss of the reverse diffusion chain. Our method significantly outperforms baseline methods in terms of success rate and sample efficiency. By effectively leveraging failure data to optimize the policy, our method can achieve results comparable to those trained with the complete success sub-dataset while requiring 20%-30% less success data.

关键词： Training Visualization Uncertainty Large language models Data collection signal processing robot learning Trajectory Planning Speech processing

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