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共 18085 条记录，以下是141-150 订阅

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A Key to Effective Multi-task Learning: Separate Query Selection for Task-Synergized Handling and Node Utilization

A Key to Effective Multi-task Learning: Separate Query Selec...

引用

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

作者： Shan-Ya Yang Hao-Chung Cheng Chien-Yao Wang Jia-Ching Wang Chun-Yi Lee National Taiwan University National Central University Academia Sinica Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the realm of computer vision, effectively handling multi-tasks simultaneously presents a challenge that necessitates innovative solutions. To better address multiple vision problems, we introduce SeTano, an integrated Graph Neural Network (GNN)-based framework. This framework comprises a Dynamic Edge-Sensing GNN (DES-GNN) backbone, which can dynamically adjust edges to extract more pivotal features, and a downstream design which includes a node reduction and a separate query selection strategy. To validate our approach, we perform multi-task experiments on the ImageNet and MS COCO datasets. The results indicate that the integrated design of SeTano leads to enhanced performance in various vision multi-tasks.

关键词： Computer vision Image edge detection signal processing Integrated design Multitasking Feature extraction Graph neural networks Acoustics Speech processing

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Low-cost design and modelling of mobile manipulator with RRT* based path planning

Low-cost design and modelling of mobile manipulator with RRT...

引用

international conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Sathish Kumar M Hari Krishna S Dhanraj E Selva Kathiresan S Sivapalanirajan M Department of EEE National Engineering College Kovilpatti EEE National Engineering College Kovilpatti

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

Mobile manipulator design is critical in implementation as it needs the coordination of mobile base and robotic arm mechanisms. In this work, the kinematics of the two mechanisms are combined synchronously to make the mobile manipulator reach the goal location with appreciable accuracy. RRT* algorithm is used to design the desired path for the robotic arm to avoid obstacles and track the shortest route. The control methodology for the operation of the manipulator is based on sensor signal fusion, which controls the movement of the manipulator. MPU 6050 IMU sensor is employed to estimate the orientation of the robotic arm tips at the base, upper and lower arm, which synchronously estimate the position and orientation (POSE) of the robotic structure. A comparison between the hardware prototype and the simulation model indicates the Design of low-cost, off-the-shelf components for a mobile manipulator application with obstacle avoidance.

关键词： Wireless communication Tracking robot kinematics signal processing algorithms Prototypes signal processing Manipulators robot sensing systems Trajectory Collision avoidance

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StableTalk: Advancing Audio-to-Talking Face Generation with Stable Diffusion and vision Transformer 27th

StableTalk: Advancing Audio-to-Talking Face Generation with...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Nazarieh, Fatemeh Kittler, Josef Rana, Muhammad Awais Kanojia, Diptesh Feng, Zhenhua School of Computer Science and Electronic Engineering University of Surrey Guildford United Kingdom Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Institute for People-Centred AI University of Surrey Guildford United Kingdom School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (纸本)9783031781711

Audio-to-talking face generation stands at the forefront of advancements in generative AI. It bridges the gap between audio and visual representations by generating synchronized and realistic talking faces. Despite recent progress, the lack of realism in animated faces, asynchronous audio-lip movements, and computational burden remain key barriers to practical applications. To address these challenges, we introduce a novel approach, StableTalk, leveraging the emerging capabilities of Stable diffusion models and vision Transformers for Talking face generation. We also integrate the Re-attention mechanism and adversarial loss to improve the consistency of facial animations and synchronization with a given audio input. More importantly, the computational efficiency of our method has been notably enhanced by optimizing operations within the latent space and dynamically adjusting the focus on different parts of the visual content based on the provided conditions. Our experimental results demonstrate the superiority of StableTalk over the existing approaches in image quality, audio-lip synchronization, and computational efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Animation

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Fall intention recognition based on semantic interval localization via hard attention for robot-assisted walking 24

Fall intention recognition based on semantic interval locali...

引用

2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Chen, Junxin Chen, Yaoyao Xu, Pengyao Chen, Chao Song, Rui Li, Weihua Di, Chong Qilu University of Technology Qilu China Shandong First Medical University Shandong Academy of Medical Sciences Shandong China Shandong University Shandong China Harbin Institute of Technology Weihai China

ISBN: (纸本)9798400710681

In the care of the elderly or disabilities, fall prevention through wearable sensors has been a key research direction. However, previous studies mainly focused on the detection of the completed fall activity, and could not effectively recognize the action when it just happened. Therefore, based on the acceleration sensor, this article designs a network for acceleration signals, focusing on the recognition of fall action just occurred, so as to achieve the capture of the action before it is completed, which is called fall intention recognition. Inspired by the field of computer vision— fine-grained classification, the recognition of picture categories only needs to pay attention to the unique feature region of the category. Similarly, the recognition of an action does not need the entire and complete signal sequence, but only needs to pay attention to the semantic interval that is different from other activities. In other words, it is the segment when the fall action starts, because the acceleration data varies with the direction of the fall. At the same time, acceleration data, especially those collected on the body, are prone to external interference, so this article dose not abandon the pre-fall data, also combines with linear acceleration data, which considered all of them are distinguishing features of fall intention. It is verified on the UniMiB SHAR standard dataset, and the experimental results show that the fall intention recognition algorithm in this paper is effective. © 2024 Copyright held by the owner/author(s).

关键词： Wearable sensors

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SVTNet: Dual Branch of Swin Transformer and vision Transformer for Monocular Depth Estimation

SVTNet: Dual Branch of Swin Transformer and Vision Transform...

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

作者： Shuwen Jia Yongxiong Wang Han Chen Shuai Huang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In monocular depth estimation, effective acquisition of global and local information is the key to improving accuracy. We introduce a novel dual branch network called Swin vision Transformer Net (SVTNet), where the Swin Transformer and vision Transformer are combined to learn features with global and local information. In Swin Transformer branch, we remove the Feature Pyramid Networks which reduces the noise introduced by interpolation and maintains the original characteristics of the hierarchical features extracted from each layer of Swin Transformer. In vision Transformer Branch, we remove the multi-scale convolutional structure and keep the same-scale feature maps extracted by vision Transformer for reducing the consumption of memory and extracting more global features. The experimental results on the NYU-Depth v2, KITTI, and CityScapes datasets show that compared with existing advanced methods, the SVTNet has achieved significant performance improvements in multiple evaluation metrics.

关键词： Computer vision Interpolation Convolution Depth measurement Noise Memory management Transformers Feature extraction Acoustics Speech processing

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Experiments on Taking Loss Methods in Imitation Learning With Bilateral Control

Experiments on Taking Loss Methods in Imitation Learning Wit...

引用

international conference on Mechatronics (ICM)

作者： Yusuke Tagashira Kazuya Okamura Tetsuro Akagawa Sho Sakaino Electrical and Information Engineering Course National Institute of Technology Hachinohe College Aomori Japan Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Japan

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

Recently, motion generation technology using deep learning has been attracting attention as a method for robots to acquire motions adapted to their environment. The combination of imitation learning and bilateral control enables imitation of movements at speeds comparable to those of humans. Previous studies have investigated learning that inputs external information, such as one-hot vectors or image features, but learning to choose actions based on dynamic external information is still being studied. If a camera is used to collect external information, high-level image processing is required, resulting in heavy processing. We thought that the robot's motion generation could cover the problem without high-level image processing. Therefore, this paper examines the task of grasping an object that moves autonomously, independent of the robot, using low-resolution images. We first tried a model that takes loss based on the transformation of angle information into arm hand coordinates, whereas the previous learning model takes loss based on angle information. The results showed that, in imitation learning based on bilateral control, using hand coordinate loss caused a bias in learning the joint angle responses. It also became clear that it is important to learn the joint angle responses with equal weights.

关键词： Hands robot motion Torque Mechatronics Imitation learning robot kinematics Image processing robot vision systems Grasping Vectors

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You Only Speak Once to See

You Only Speak Once to See

引用

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

作者： Wenhao Yang Jianguo Wei Wenhuan Lu Lei Li College of Intelligence and Computing Tianjin University Tianjin China University of Washington University of Copenhagen

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Grounding objects in images using visual cues is a well-established approach in computer vision, yet the potential of audio as a modality for object recognition and grounding remains underexplored. We introduce YOSS, "You Only Speak Once to See," to leverage audio for grounding objects in visual scenes, termed Audio Grounding. By integrating pre-trained audio models with visual models using contrastive learning and multi-modal alignment, our approach captures speech commands or descriptions and maps them directly to corresponding objects within images. Experimental results indicate that audio guidance can be effectively applied to object grounding, suggesting that incorporating audio guidance may enhance the precision and robustness of current object grounding methods and improve the performance of robotic systems and computer vision applications. This finding opens new possibilities for advanced object recognition, scene understanding, and the development of more intuitive and capable robotic systems.

关键词： YOLO Visualization Computer vision Grounding Computational modeling Contrastive learning signal processing Text to speech Object recognition Speech processing

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MambaNext: An Enhanced Backbone Network with Focus Linear Attention

MambaNext: An Enhanced Backbone Network with Focus Linear At...

引用

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

作者： Dafeng Zhang Shizhuo Liu Samsung Research China - Beijing (SRC-B) Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In response to the limitations of current linear attention models, such as vision Mamba, which fail to mimic the human visual system’s ability to focus on objects and then shift attention to the surrounding context when ambiguity arises, we introduce the MambaNext model. This novel backbone network incorporates two key innovations: the Focus Linear Attention Module (FLAM) and the Star Fusion strategy. FLAM is designed to enhance object recognition by emulating the human visual system’s focused center, thereby reducing the interference from background elements. On the other hand, Star Fusion acts as a unique operation that utilizes global information as an attention map to guide local information toward more relevant features. Additionally, it also implicitly increases the feature dimensions to improve linear separability. The experimental results demonstrate that our MambaNext has achieved state-of-the-art performance across multiple computer vision tasks including classification, detection, and segmentation, outperforming existing vision Mamba methods.

关键词： Visualization Image segmentation Adaptation models Technological innovation Stars Interference Visual systems signal processing Speech processing Context modeling

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Multimodal and Multiple Prompts for Biology

Multimodal and Multiple Prompts for Biology

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

作者： Chonghuinan Wang Xi Chen Hongxun Yao Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Prompt learning for vision-language models, e.g. CLIP, is a rapidly advancing topic, focusing on leveraging pre-trained foundational models to generalize to downstream tasks with minimal training samples. Current prompt-tuning methods primarily optimize on general datasets, yet their application in biological datasets remains limited due to the field’s specificity. To address this gap, we introduce Multimodal and Multiple Prompts for Biology (MMPB). Our approach incorporates a vision Prompt Injection module, which injects diverse biological visual information from the vision branch into the language branch. Additionally, we propose a multiple prompts scheme tailored to the fine-grained naming conventions in biology. We assess the method’s effectiveness on generation to novel classes, and results demonstrate that MMPB significantly outperforms existing approaches across eight biological datasets. Specifically, MMPB achieves a 14.90% absolute improvement in novel classes performance and an 11.71% gain in harmonic mean compared to the state-of-the-art.

关键词： Training Visualization Technological innovation Biological system modeling Focusing signal processing Harmonic analysis Acoustics Complexity theory Speech processing

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Patch Attention Excitation Based vision Transformer for Small-Sized Datasets

Patch Attention Excitation Based Vision Transformer for Smal...

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

作者： Akash Verma Shiv Ram Dubey Satish Kumar Singh Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

vision Transformers have proven their mettle across a variety of computer vision problems, however, their reliance on pretraining with very large-scale datasets such as JFT-300M is also no secret, as large amounts of data is very conductive to effective feature learning. In this paper, however, we propose a novel vision Transformer architecture PAEViT that aims to effectively learn generalized features from small-sized datasets, usually having only a few hundred images per class at most. By refining the attention scores based on patch-level interactions and modulating it to enhance the trained model’s ability to focus more on task relevant patches, PAEViT is able to significantly improve upon the performance of the regular ViT model, as well as other variants of the same, when the model is being trained solely on small-sized datasets. In data-constrained situations and visual recognition tasks that do not conform well with the existing large-scale datasets, PAEViT can be used to create effective and scalable solutions with all the features and attention scores being based only on relevant data. Our code is publicly available at https://***/AkashVermaIN/PAEViT.

关键词： Representation learning Computer vision Visualization Codes Computational modeling Refining Computer architecture Speech enhancement signal processing Transformers

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