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DMFormer: Closing the gap Between CNN and vision Transformers 48

DMFormer: Closing the gap Between CNN and Vision Transformer...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Wei, Zimian Pan, Hengyue Li, Lujun Lu, Menglong Niu, Xin Dong, Peijie Li, Dongsheng National University of Defense Technology College of Computer China Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728163277

vision transformers have shown excellent performance in computer vision tasks. As the computation cost of their self-attention mechanism is expensive, recent works tried to replace the self-attention mechanism in vision transformers with convolutional operations, which is more efficient with built-in inductive bias. However, these efforts either ignore multi-level features or lack dynamic prosperity, leading to sub-optimal performance. In this paper, we propose a Dynamic Multi-level Attention mechanism (DMA), which captures different patterns of input images by multiple kernel sizes and enables input-adaptive weights with a gating mechanism. Based on DMA, we present an efficient backbone network named DMFormer. DMFormer adopts the overall architecture of vision transformers, while replacing the self-attention mechanism with our proposed DMA. Extensive experimental results on ImageNet-1K and ADE20K datasets demonstrated that DMFormer achieves state-of-the-art performance, which outperforms similar-sized vision transformers(ViTs) and convolutional neural networks (CNNs). © 2023 IEEE.

关键词： Convolutional neural networks

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Low-voltage Topology Recognition Based on Carrier signal Injection 2

Low-voltage Topology Recognition Based on Carrier Signal Inj...

引用

2nd international conference on Image processing, Computer vision and Machine Learning, ICICML 2023

作者： Zhang, Zhi Chen, Zhiru Zhao, Xi Du, Yan Shandong Jinan250002 China

ISBN: (纸本)9798350331417

Aiming at the problem of unclear low-voltage topology caused by the current low-voltage distribution network user information changes, meter fault replacement, station upgrades and other reasons, this paper designs a low-voltage topology identification method based on carrier signal injection. The method uses the signal generating device in the smart meter module to generate a carrier current signal with characteristic code bits in the power grid. According to the shunt characteristics of the current in the circuit, the branch carrier current signal of the injected carrier current signal has the greatest strength, and the other branch carrier current signals are much smaller than the injected signal branch. By comparing the strength of the carrier current signal to identify the low-voltage topology. Simulations and experiments show that this method can effectively identify low-voltage topology. © 2023 IEEE.

关键词： Carrier signal Carrier signal shunt FFT analysis User-transformer relationship recognition

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vision Technology in Underwater: Applications, Challenges and Perspectives 4

Vision Technology in Underwater: Applications, Challenges an...

引用

4th international conference on Control and robotics (ICCR)

作者： Li, Yiyang Sun, Kai Han, Zekai Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781665486415

vision technology is growing vigorously, but underwater vision remains challenges and problems, which is a field with application value and development prospects. The visual information obtained underwater is often color-biased and blurred, and it is highly susceptible to the influence of the surrounding environment and its stability cannot be controlled. Therefore, the current research and applications of vision technologies in underwater vision provide ways to solve the described problems, including image processing, target detection and recognition, localization and tracking technologies. The purpose of this review is to summarize the development of underwater vision technology and the results achieved. The mainstream underwater vision technologies are classified according to the different theories or algorithms used, and the current new research progress in each field is introduced in detail. By summarizing and analyzing the above research results, the application of each key technology of underwater vision is sorted out, and its further development direction is foreseen.

关键词： underwater vision underwater image processing underwater target detection and recognition underwater localization and tracking deep learning

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AUTONODE: A Neuro-Graphic Self-Learnable Engine for Cognitive GUI Automation 29

AUTONODE: A Neuro-Graphic Self-Learnable Engine for Cognitiv...

引用

29th international conference on Automation and Computing (ICAC)

作者： Datta, Arkajit Verma, Tushar Chawla, Rajat Mukunda, N. S. Bhola, Ishaan SuperAGI Res Palo Alto CA 94306 USA

ISBN: (纸本)9798350360882;9798350360899

In recent advancements within the domain of Large Language Models (LLMs), there has been a notable emergence of agents capable of addressing robotic Process Automation (RPA) challenges through enhanced cognitive capabilities and sophisticated reasoning. This development heralds a new era of scalability and human-like adaptability in goal attainment. In this context, we introduce AUTONODE (Autonomous User-interface Transformation through Online Neuro-graphic Operations and Deep Exploration). AUTONODE employs advanced neuro-graphical techniques to facilitate autonomous navigation and task execution on web interfaces, thereby obviating the necessity for predefined scripts or manual intervention. Our engine empowers agents to comprehend and implement complex workflows, adapting to dynamic web environments with unparalleled efficiency. Our methodology synergizes cognitive functionalities with robotic automation, endowing AUTONODE with the ability to learn from experience. We have integrated an exploratory module, DoRA (Discovery and mapping Operation for graph Retrieval Agent), which is instrumental in constructing a knowledge graph that the engine utilizes to optimize its actions and achieve objectives with minimal supervision. The versatility and efficacy of AUTONODE are demonstrated through a series of experiments, highlighting its proficiency in managing a diverse array of web-based tasks, ranging from data extraction to transaction processing. The implementation of our paper can be accessed at :https://***/TransformerOptimus/AutoNode

关键词： Self-Operating Computer Generative AI LLMs Transformers vision-Transformers Graphs Reinforcement Learning

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Diffusion Models are Zero-Shot Generative Text-vision Retrievers

Diffusion Models are Zero-Shot Generative Text-Vision Retrie...

引用

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

作者： Li, Bao Xie, Zeke Zhang, Xiaomei Zhu, Xiangyu Lei, Zhen MAIS Institute of Automation Chinese Academy of Sciences Beijing China School of Artifical Intelligence University of Chinese Academy of Sciences Beijing China Guangzhou China CAIR HKISI Chinese Academy of Sciences Hong Kong

ISBN: (纸本)9798350368741

Large-scale text-to-image diffusion models have demonstrated impressive capabilities for downstream tasks by leveraging strong vision-language alignment from generative pre-training. Recently, a number of works have explored how to use the power of text-to-image diffusion models for text-image matching tasks. While previous generative text-image matching methods have shown potential in retrieving the most challenging candidates, they still suffer from extremely slow retrieval speeds and lack the ability to handle temporal dimensions, making them impractical for text-video retrieval. In this paper, we propose ZSGenRet, a simple yet effective zero-shot generative text-vision retrieval framework for both text-image and text-video tasks, based on pre-trained text-to-image diffusion models. We further incorporate inversion saliency detection to identify key frames in videos and enhance the semantic representation of the vision encoder. Experimental results demonstrate that ZSGenRet significantly improves text-video retrieval performance and achieves competitive results on text-image retrieval while remarkably improving efficiency. To the best of our knowledge, the proposed ZSGenRet is the first to explore zero-shot text-video retrieval based on diffusion models. © 2025 IEEE.

关键词： diffusion models Text-vision retrieval vision-language models zero-shot learning

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A vision-ASSISTED HEARING AID SYSTEM BASED ON DEEP LEARNING

A VISION-ASSISTED HEARING AID SYSTEM BASED ON DEEP LEARNING

引用

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

作者： Michelsanti, Daniel Tan, Zheng-Hua Rotger-Griful, Sergi Jensen, Jesper Oticon AS Smorum Denmark Aalborg Univ Dept Elect Syst Aalborg Denmark Eriksholm Res Ctr Snekkersten Denmark

ISBN: (纸本)9798350302615

Audio-visual speech enhancement (SE) is the task of reducing the acoustic background noise in a degraded speech signal using both acoustic and visual information. In this work, we study how to incorporate visual information to enhance a speech signal using acoustic beamformers in hearing aids (HAs). Specifically, we first trained a deep learning model to estimate a time-frequency mask from audio-visual data. Then, we apply this mask to estimate the inter-microphone power spectral densities (PSDs) of the clean and the noise signal. Finally, we used the estimated PSDs to build acoustic beamformers. Assuming that a HA user wears an add-on device comprising a camera pointing at the target speaker, we show that our method can be beneficial for HA systems especially at low signal to noise ratios (SNRs).

关键词： Audio-visual hearing aids beamforming deep learning

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vision-Radar Fusion-Based Dynamic Sparse Intrusion UAV Detection for Low-Air Security 16

Vision-Radar Fusion-Based Dynamic Sparse Intrusion UAV Detec...

引用

16th international conference on Wireless Communications and signal processing, WCSP 2024

作者： Wan, Yiyao Ji, Jiahuan Zhou, Fuhui Wu, Qihui Quek, Tony Q. S. College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics China Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (纸本)9798350390643

Precise intrusion unmanned aerial vehicle (UAV) detection over long distances is of crucial importance for guaranteeing the low-air security. Although many deep learning-based vision detectors have been developed, they still rely on a large amount of hand-crafted fixed feature priors. Thus, the existing static dense-based detectors suffer from the severe mismatch and imbalance between the small size and high mobility of UAVs. To solve the problem, a novel vision-radar fusion-based dynamic sparse network (Vira-DSNet) is proposed for more balanced and precise UAV detection. The Vira-DSNet exploits our designed dynamic sparse candidate generator and radar-guided semantic feature transform module to generate a small set of customized high-quality object candidates and UAV semantic features based on the radar data. Furthermore, based on Hungarian bisection matching, the proposed Vira-DSNet eliminates the post-processing and is completely end-to-end differentiable. Moreover, the Vira-DSNet is deployed in our developed actual vision-radar fusion-based early intrusion UAV detection system to evaluate the performance in practical applications. Experimental results demonstrate that our Vira-DSNet achieves an average precision AP50 of 88.2%. It is also shown that the average recall AR1 of Vira-DSNet is higher than the state-of-the-art scheme by 10.1 %. © 2024 IEEE.

关键词： Deep learning

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ATTENTION-GUIDED CONTRASTIVE MASKED IMAGE MODELING FOR TRANSFORMER-BASED SELF-SUPERVISED LEARNING 30

ATTENTION-GUIDED CONTRASTIVE MASKED IMAGE MODELING FOR TRANS...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Zhan, Yucheng Zhao, Yucheng Luo, Chong Zhang, Yueyi Sun, Xiaoyan Univ Sci & Technol China Hefei Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781728198354

Self-supervised learning with vision transformer (ViT) has gained much attention recently. Most existing methods rely on either contrastive learning or masked image modeling. The former is suitable for global feature extraction but underperforms in fine-grained tasks. The later explores the internal structure of images but ignores the high information sparsity and unbalanced information distribution. In this paper, we propose a new approach called Attention-guided Contrastive Masked Image Modeling (ACoMIM), which integrates the merits of both paradigms and leverages the attention mechanism of ViT for effective representation. Specifically, it has two pretext tasks, predicting the features of masked regions guided by attention and comparing the global features of masked and unmasked images. We show that these two pretext tasks complement each other and improve our method's performance. The experiments demonstrate that our model transfers well to various downstream tasks such as classification and object detection. Code is available at https://***/yczhan/ACoMIM.

关键词： Self-Supervised Learning vision transformer Masked image modeling

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MULTISCALE REPRESENTATIONS LEARNING TRANSFORMER FRAMEWORK FOR POINT CLOUD CLASSIFICATION 30

MULTISCALE REPRESENTATIONS LEARNING TRANSFORMER FRAMEWORK FO...

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30th IEEE international conference on Image processing (ICIP)

作者： Sun, Yajie Zia, Ali Zhou, Jun Griffith Univ Sch Informat & Commun Technol Brisbane Qld Australia CSIRO Agr & Food Northam WA Australia Australian Natl Univ Coll Sci Canberra ACT Australia

ISBN: (纸本)9781728198354

Extracting and aggregating multiple feature representations from various scales have become the key to point cloud classification tasks. vision Transformer (ViT) is a representative solution along this line, but it lacks the capability to model detailed multi-scale features and their interactions. In addition, learning efficient and effective representation from the point cloud is challenging due to its irregular, unordered, and sparse nature. To tackle these problems, we propose a novel multi-scale representation learning transformer framework, employing various geometric features beyond common Cartesian coordinates. Our approach enriches the description of point clouds by local geometric relationships and group them at multiple scales. This scale information is aggregated and then new patches can be extracted to minimize feature overlay. The bottleneck projection head is then adopted to enhance the information and feed all patches to the multi-head attention to capture the deep dependencies among representations across patches. Evaluation on public benchmark datasets shows the competitive performance of our framework on point cloud classification.

关键词： Point cloud classification multi-scale features geometric features multi-scale transformer 3D computer vision

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BITS-NET: BLIND IMAGE TRANSPARENCY SEPARATION NETWORK 30

BITS-NET: BLIND IMAGE TRANSPARENCY SEPARATION NETWORK

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30th IEEE international conference on Image processing (ICIP)

作者： Zhou, Chao Lyu, Zhaoyan Rodrigues, Miguel R. D. UCL Dept Elect & Elect Engn London England

ISBN: (纸本)9781728198354

This research presents a new approach for blind single-image transparency separation, a significant challenge in image processing. The proposed framework divides the task into two parallel processes: feature separation and image reconstruction. The feature separation task leverages two deep image prior (DIP) networks to recover two distinct layers. An exclusion loss and deep feature separation loss are used to decompose features. For the image reconstruction task, we minimize the difference between the mixed image and the re-mixed image while also incorporating a regularizer to impose natural priors on each layer. Our results indicate that our method performs comparably or outperforms state-of-the-art approaches when tested on various image datasets.

关键词： blind image separation deep image prior deep learning computer vision

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