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Relation-aware Semantic Alignment Network for Text-to-image Person Retrieval

Relation-aware Semantic Alignment Network for Text-to-Image ...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yong Wu Rongxi Zhou Hongchao Li Ze Zhou Feifei Wei Min Li Guodui He School of Computer and Information Anhui Normal University Wuhu China Guangxi Academy of Agricultural Sciences Nanning China China Communications Service Guangxi Technical Service Branch Nanning China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Text-to-image Person Retrieval (TIPR) aims to utilize natural language descriptions as queries to retrieve pedestrian images. However, existing methods only concentrated on aligning individual text-image pairs and ignored the specific self-representations within both visible images and textual descriptions of the same identity. This neglects the impact of intra-modal information distribution on TIPR. In this paper, a novel Relation-aware Semantic Alignment Network (RSAN) is proposed to learn reliable and comprehensive semantic visual-textual associations across different modalities. Specifically, A Global Semantic Alignment Matching (GSAM) loss is introduced to enhance the coherence of inter-modality features while preserving intra-modal representations for cross-modal matching. Additionally, an Adapter-assisted Information Aggregation (AIA) module is designed to further complement contextual information fusion between the image features and text embeddings. Extensive experiments conducted on two public benchmark datasets demonstrate the superiority of the proposed RSAN.

关键词： visualization Pedestrians Semantics Natural languages Text to image Coherence Benchmark testing Signal processing Reliability Speech processing

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Training-free Adapter for Multi-Modal image Matching for All-Day visual Place Recognition

Training-free Adapter for Multi-Modal Image Matching for All...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Anuradha Uggi Sumohana Channappayya Department of Electrical Engineering IIT Hyderabad Kandi India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

visual Place Recognition (VPR) identifies an image’s location by matching a query image of an unknown location against geotagged reference images. This has been a problem of interest for the computer vision community for many years. Consequently, many successful methods with impressive performance have been proposed in the literature. However, these works are primarily for the visible spectrum, restricting recognition to RGB images. This study reveals the shortcomings of the popular VPR methods for handling multi-modal RGB-Thermal (RGB-T) retrieval. Additionally, we introduce a straightforward yet effective training-free aggregator that can be integrated with any backbone model. The proposed method, motivated by the rich structures in the images, attempts to capture the correlation between them. We call it Self-Neighbourhood Support Maps (SNSM). Simple to comprehend and implement, experiments on various RGB-T datasets reveal that SNSM performs better than widely used unsupervised techniques such as VLAD by a considerable margin. The source code at: https://***/lfovia/SNSM.

关键词： Computer vision Adaptation models image recognition Correlation Source coding Speech recognition Signal processing Acoustics Speech processing visual place recognition

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Stable Control visual AutoRegressive Model: Precise and Efficient image Generation via Scale Alignment

Stable Control Visual AutoRegressive Model: Precise and Effi...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Feng Xie Dahua Gao Ruichao Liu Minxi Yang Yibo Zhang Wenlong Wang School of Artificial Intelligence Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Although diffusion models advance condition-based visual generation, they suffer from speed and cost issues, unlike faster AutoRegressive methods that are limited in performance. To address these, we introduce the Stable Control visual AutoRegressive Model (SCVAR). SCVAR ensures stable control by aligning visual conditions on multiple scales. Rather than unfolding the 2D image into a 1D raster, SCVAR decouples it into multiple scales. This shifts the sequential representation in SCVAR from tokens to scales, satisfying the unidirectional dependency of the AR model while preserving the 2D structure of the image. Compared to indiscriminate conditional guidance, cross-scale alignment provides more precise constraints, enabling SCVAR to achieve state-of-the-art performance in experiments against diffusion models, with 10x faster generation speed. The decoupled condition also reduces training costs. Compared to end-to-end conditional computation, experiments demonstrate that SCVAR matches performance with only 40% additional parameters.

关键词： Training visualization Costs image synthesis Computational modeling Signal processing Diffusion models Acoustics Speech processing

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MAITFuse:Multi-Dimension Adaptive Interaction Transform Network For Infrared-visible image Fusion

MAITFuse:Multi-Dimension Adaptive Interaction Transform Netw...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yabin Sun Wentai Lei Ziyi Zhang Jiongchang Liu Chenxu Li Tao Zhang Artificial Intelligence Jiangnan University Wu Xi China Communications Engineering Central South University Chang Sha China School of Electronic Information Central South University Chang Sha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, Transformers have achieved significant success in image fusion. These methods utilize self-attention mechanism across different spatial or channel dimensions and have demonstrated impressive performance. However, existing methods only optimize along a single dimension and struggle to simultaneously capture the complex dependencies between spatial and channel dimensions. To address this problem, we propose a novel multi-dimensional adaptive interaction transformer network, named as MAITFuse, to enhance the multilevel information expression and detail retention capabilities of images. We design a Multi-Dimensional Feature Extraction (MDFE) module to extract features across spatial and channel dimensions in parallel, and introduce a novel weighted cross-attention fusion method to integrate multi-dimensional information effectively. Experimental results show that, compared to existing fusion methods, our proposed method achieves superior fusion performance across various datasets.

关键词： Adaptive systems Transforms Signal processing Feature extraction Transformers Acoustics Data mining Speech processing image fusion visual perception

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Deformable Attention-Based Edge-Aware Network for Single image Super-Resolution

Deformable Attention-Based Edge-Aware Network for Single Ima...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ju Zhang Baojiang Zhong Kai-Kuang Ma School of Computer Science and Technology Soochow University Suzhou China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately reconstructing object edges is a key challenge in single image super-resolution (SISR), as it greatly influences our visual perception of image quality. To address this fundamental issue, we propose a novel SISR approach named the deformable attention-based edge-aware (DAE) network. The DAE network features a deformable attention block that dynamically adjusts attention weights to align with edge structures, thereby improving edge awareness and reconstruction quality. Moreover, our network incorporates a multi-patterns window block that captures fine-grained edge details and enhances information flow across network layers. This combination results in visually superior SISR outputs. Extensive experiments and comparisons with the state-of-the-art methods demonstrate that our DAE network excels in both quantitative metrics and visual quality.

关键词： Measurement visualization image edge detection Superresolution Stacking Refining Speech processing image reconstruction Signal resolution visual perception

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Multi-modal Entity Alignment under Imbalanced visual Modality Information

Multi-modal Entity Alignment under Imbalanced Visual Modalit...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xin Zhang Yu Liu Shimin Shan School of Software Dalian University of Technology Dalian China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multi-modal entity alignment (EA) is an important extension of the EA task. Existing multi-modal EA methods use randomly generated embeddings to supplement the missing image features of entities, ignoring the impact of correlation between entities and images. In this paper, we propose IVMEA, a multi-modal EA framework under imbalanced visual modality information. Specifically, IVMEA first establishes a mapping network from semantic features to image features, and then completes the training of the mapping network in terms of both alignment loss and cross-modal loss to obtain semantic-aware image embeddings for multi-modal EA. Finally, the multi-modal EA task is completed based on the similarities of the multi-modal feature embeddings. Experimental results show that our proposed IVMEA achieves the state-of-the-art (SOTA) performance on three cross-lingual datasets with limited parameters and runtime in various imbalanced visual modality scenarios.

关键词： Training visualization Runtime Correlation Attention mechanisms Semantics Signal processing algorithms Signal processing Speech processing Computational complexity

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Infrared and Visible image Fusion with Hierarchical Human Perception

Infrared and Visible Image Fusion with Hierarchical Human Pe...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Guang Yang Jie Li Xin Liu Zhusi Zhong Xinbo Gao School of Electronic Engineering Xidian University Xi’an China Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

image fusion combines images from multiple domains into one image, containing complementary information from source domains. Existing methods take pixel intensity, texture and high-level vision task information as the standards to determine preservation of information, lacking enhancement for human perception. We introduce an image fusion method, Hierarchical Perception Fusion (HPFusion), which leverages Large Vision-Language Model to incorporate hierarchical human semantic priors, preserving complementary information that satisfies human visual system. We propose multiple questions that humans focus on when viewing an image pair, and answers are generated via the Large Vision-Language Model according to images. The texts of answers are encoded into the fusion network, and the optimization also aims to guide the human semantic distribution of the fused image more similarly to source images, exploring complementary information within the human perception domain. Extensive experiments demonstrate our HPFusoin can achieve high-quality fusion results both for information preservation and human visual enhancement Our code is available at https://***/SSyangguangZHPFusion.

关键词： visualization Codes Semantics visual systems Signal processing Acoustics Speech processing image fusion Optimization Standards

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Lightweight image Quality Prediction Guided by Perceptual Ranking Feedback

Lightweight Image Quality Prediction Guided by Perceptual Ra...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Aymen Sekhri Mohamed-Chaker Larabi Seyed Ali Amirshahi CNRS Université de Poitiers XLIM Poitiers France Norwegian University of Science and Technology Gjøvik Norway

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Automatic image Quality Assessment (IQA) remains a difficult challenge due to the complexity of mimicking the Human visual System (HVS) and the limitations of traditional objective image Quality Metrics (IQM). Existing learnable methods often involve high computational costs and fail to adequately capture the nuanced perceptual characteristics of the HVS, including the human ability to rank image quality and human sensitivity to differences in areas with high-frequency. In this study, we propose an effective approach that addresses these challenges by incorporating the characteristics of HVS and the perceptual classification into a lightweight IQM framework based on the transformer architecture. This allows our method to capture long-range dependencies effectively. Our approach leverages Objective Error Maps (OEMs) to enhance sensitivity to visual errors and employs a ranking module as an objective function, providing feedback on the perceptual quality at the feature level. Experimental results demonstrate that our approach not only achieves competitive performance compared to state-of-the-art IQMs but also significantly reduces computational complexity.

关键词： image quality visualization Sensitivity Computer architecture visual systems Signal processing Transformers Linear programming Computational efficiency Speech processing

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EvaSR: Rethinking Efficient visual Attention Design for image Super-Resolution

EvaSR: Rethinking Efficient Visual Attention Design for Imag...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhijian Wu Chenhan Zhang Dingjiang Huang School of Data Science and Engineering East China Normal University China Trenton Collegiate Institute Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the advantages of long-range modeling via the self-attention mechanism, Transformer has taken various vision tasks by storm, including image super-resolution (SR). In this study, we reveal that the convolutional neural network (CNN) with proper visual attention is a more simple and effective paradigm than Transformer in image SR tasks. We reexamine the successful SR models and discover several key characteristics that contribute to accurate image reconstruction. Built on this recipe, we propose a pure CNN-based SR network using efficient visual attention, dubbed EvaSR. Benefiting from the carefully designed visual attention, our EvaSR can favorably capture both local structure and long-range dependencies, and achieve adaptivity in spatial and channel dimensions while retaining the simplicity and efficiency of CNNs. The experimental results demonstrate that our EvaSR achieves state-of-the-art performance among the existing efficient SR methods. Especially, the tiny version of EvaSR needs 21.4% and 15.2% parameters of IMDN and SMSR with better performance.

关键词： Performance evaluation visualization Storms Computational modeling Superresolution Transformers Convolutional neural networks Speech processing Spatial resolution Signal resolution

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ViT-PQC: Vision Transformer-Based Patch Quality Controller for Edge-Assisted visual-SLAM

ViT-PQC: Vision Transformer-Based Patch Quality Controller f...

引用

Consumer communications and Networking conference, CCNC ieee

作者： Yuma Katsuki Hayato Itsumi Yusuke Shinohara Koichi Nihei Anan Sawabe Takanori Iwai Secure System Platform Research Laboratories NEC Corporation Kawasaki Kanagawa Japan

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Edge-based visual simultaneous localization and mapping (visual-SLAM) enables real-time indoor localization of robots by using substantial computing resources of an edge server. However, this system can place a burden on resource-constrained wireless networks because robots transmit their video data to the edge server via wireless networks. Given the sharing of the limited network capacity among multiple devices, the video bitrate needs to be reduced so as not to overshoot the available bandwidth. Although lowering the video quality can reduce the video bitrate, it also results in a loss of detailed image features and can negatively impact localization accuracy. In this paper, we propose a Vision Transformer-based patch quality controller (ViT-PQC) that reduces the video bitrate below the available bandwidth while preserving localization accuracy. ViT-PQC segments the video frame into patches and assigns an optimal image quality to each patch by taking into account both the image features of the recent frame and time shift features between the two most recent frames. Evaluation results demonstrate that ViT-PQC reduces the number of bitrate overshoots by 94% while preserving localization accuracy.

关键词： Location awareness visualization Accuracy Wireless networks image edge detection Bit rate Bandwidth Streaming media Transformers Video recording

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