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AGIAA-2K: A Fine-grained Dataset for Aesthetic and Alignment Evaluation of AI-Generated images

AGIAA-2K: A Fine-grained Dataset for Aesthetic and Alignment...

引用

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

作者： Bo Hu Nanxiang Li Lihuo He Wen Lu Leida Li Xinbo Gao Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China School of Electronic Engineering Xidian University Xi’an China School of Artificial Intelligence Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the advancement of AI-generated content technologies, AI-generated images (AGIs) have become increasingly influential in artistic creation and visual communication. However, the aesthetic quality of AGIs varies significantly due to technical limitations and the influence of user input, underscoring the urgent need for systematic aesthetic evaluation of AGIs. In addition, it is difficult to ensure the consistency of text-to-image, which compresses the application space of AGIs. To address these issues, a fine-grained dataset for Aesthetic and Alignment evaluation of AGIs (AGIAA-2K) is presented. This dataset contains 2,064 images generated using 172 well-designed prompts across six different AGI models, with each image annotated based on the subjective experiment. Then, the rationality of the dataset is verified by data analysis. Finally, the performances of the existing algorithms are evaluated in terms of image aesthetic assessment and text-to-image alignment of AGIs. The results demonstrate that these algorithms cannot effectively evaluate these two aspects. The AGIAA-2K is available at https://***/BoHu90/AGIAA-2K.

关键词： Measurement Technological innovation Systematics image coding Data analysis visual communication Text to image Signal processing algorithms Signal processing Speech processing

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Overcoming Core Challenges in Monocular Camera Drone Navigation of Unknown Maze 11

Overcoming Core Challenges in Monocular Camera Drone Navigat...

引用

11th International conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Danial, Jeryes Asher, Yosi Ben CS. University of Haifa Israel

ISBN: (纸本)9798331509293

Typically, systems for autonomous monocular navigation of a drone in an indoor unknown maze of obstacles consists of three main components: 1) sensing-part which is usually visual Simultaneous Localization and Mapping (VSLAM- computing a 3D-map of obstacles and pose-estimation of the drone), 2) the policy part (usually computing an obstacle-free path), and 3) the control part that executes the flight instructions. Though realizing these components (e.g. sensing=VSLAM and policy=freePath) yields a drone that works the resulting system will not be robust enough to cover all types of mazes. Thus, realizing these components is not enough to produce a robust working system. For example, when searching in a maze of rooms and doors, often there can be smooth surfaces (such as doors and white walls) that have no key points to detect. Hence, VSLAM (and other alternatives) will fail in building its 3D map and calculating the drone position. In this work, we describe a set of additional 'sub-problems' whose solution enables any monocular-based sensing+policy to work in general real-life scenarios. This is especially true for a lightweight drone limited to a simple monocular camera and using Raspberry Pi for control. We used a system whose sensing consists of an obstacle/free-space detector ODV [3] and the policy is based on the Zigzag algorithm [2]. The set of sub-problems we have found include: obtaining fast processing rates, Fast accurate pose estimation, Overcoming featureless areas such as white-walls, and obtaining smooth flight trajectories. The experiments we describe, fully show that the solution of these sub-problems allows the Zigzag algorithm to efficiently scan rooms even when featureless surfaces exist. © 2025 ieee.

关键词： image segmentation

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Efficient visual Storytelling through Descriptive Words Distillation and Dynamic Decoding

Efficient Visual Storytelling through Descriptive Words Dist...

引用

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

作者： Zixuan Xiong Hai Lin Lichen Bai Yinghui Li Hai-Tao Zheng Hong-Gee Kim Shenzhen International Graduate School Tsinghua University Shenzhen China Shenzhen International Graduate School Tsinghua University Pengcheng Laboratory Shenzhen China Seoul National University Seoul Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

visual storytelling, a complex task in natural language generation, aims to create coherent and engaging narratives from a sequence of images, requiring more intricate and lengthy descriptions than typical image captioning. Current methods generally employ sophisticated modal interaction modules and require substantial additional data for training. This paper introduces two innovative techniques to advance visual storytelling capabilities. First, we propose a method to distill descriptive word embeddings from model parameters as an extension to the model’s pre-trained embeddings. This approach focuses the training process on these descriptive words, allowing for efficient fine-tuning and eliminates the need for additional visual modules and reduces computational overhead. Second, we develop a dynamic decoding strategy that adapts the text generation process based on the characteristic of the image features. This ensures a improved alignment with image features and the generated stories. Our experiments on the VIST dataset demonstrate that these methods achieve an impressive performance. Ablation studies show significant improvements in training efficiency and narrative quality compared to existing approaches.

关键词： Training visualization Adaptation models Technological innovation Computational modeling Natural language generation Signal processing Linguistics Decoding Speech processing

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Automated Blood Group Detection System Using image processing for Non-Invasive Antigen Feature Extraction and Classification

Automated Blood Group Detection System Using Image Processin...

引用

2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Bala Weslin, D. Sophia, S. Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

The project intends to design an RFID based automated blood group detection system through image processing and a specific CNN. First, the provided images of blood cells are processed and the system extracts features relevant to the given antigen for the purpose of identifying the blood group. With the help of a set of 12,500 augmented images of blood cells, the System processes and normalizes these images for classification. The presented method can be considered as more effective to detect blood groups in comparison with the traditional method that requires invasive procedures and requires manual inspection. It is a system that optimizes the detection of blood groups leading to improvements in medical diagnosis and medical imaging in general. © 2025 ieee.

关键词： Photointerpretation

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Refiner: Fine-grained Cross-modal Concepts Refinement for Compositional Zero-Shot Learning

Refiner: Fine-grained Cross-modal Concepts Refinement for Co...

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

作者： Xiao Zhang Haodong Jing Hui Chen Yongqiang Ma Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent Compositional Zero-Shot Learning (CZSL) methods increasingly adopt the pre-trained vision-language models to capture the contextual relations between image and text spaces. However, the single-class-token design from Transformer-based encoder inevitably captures contextual information from unrelated objects and background, thus hindering the modeling of fine-grained class-specific visual features. Suffering from cross-modal gap, prior methods also struggle to improve compositional recognition performance. To address these issues, we propose a fine-grained cross-modal concepts refinement framework, termed as Refiner, which comprises two pivotal components: (i) the fine-grained concepts refinement of image embeddings to capture state-object context within visual scenes, and (ii) the cross-modal information fusion to mitigate the modality gap. By leveraging learnable query vectors to capture region-specific semantic information pertinent to composition labels, our approach refines visual representations with fine-grained state-object context information. As for cross-modal information fusion, we construct a robust image-to-text mapping by aligning visual embeddings with states, objects, and compositions, respectively. Extensive experiments demonstrate that our Refiner achieves new state-of-the-art performance across all popular benchmarks in both closed- and open-world settings.

关键词： visualization Zero shot learning Semantics Benchmark testing Signal processing Transformers Vectors Acoustics Speech processing Context modeling

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BrainVis: Exploring the Bridge between Brain and visual Signals via image Reconstruction

BrainVis: Exploring the Bridge between Brain and Visual Sign...

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

作者： Honghao Fu Hao Wang Jing Jih Chin Zhiqi Shen Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Analyzing and reconstructing visual stimuli from brain signals effectively advances our understanding of the human visual system. However, EEG signals are complex and contain significant noise, leading to substantial limitations in existing approaches of visual stimuli reconstruction from EEG. These limitations include difficulties in aligning EEG embeddings with fine-grained semantic information and a heavy reliance on additional large-scale datasets for training. To address these challenges, we propose a novel approach called BrainVis. This approach introduces a self-supervised paradigm to learn EEG time-domain features and incorporates frequency-domain features to enhance EEG representations. We also propose a multi-modal alignment method called semantic interpolation to achieve fine-grained semantic reconstruction. Additionally, we employ cascaded diffusion models to reconstruct images. Using only 9.1% of the training data required by previous mask modeling works, our proposed BrainVis outperforms state-of-the-art methods in both semantic fidelity reconstruction and generation quality. The code is available at https://***/RomGai/BrainVis.

关键词： Training visualization Time-frequency analysis Semantics Training data visual systems Diffusion models Brain modeling Electroencephalography image reconstruction

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DLM-VMTL:A Double LayerMapper For Heterogeneous Data Video Multi-Task Prompt Learning

DLM-VMTL:A Double LayerMapper For Heterogeneous Data Video M...

引用

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

作者： Zeyi Bo Ye Jin Wuxi Sun Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, the parameters of backbones of Video Understanding tasks continue to increase and even reach billion-level. Whether fine-tuning a specific task on the Video Foundation Models(VFMs) or pre-training the model designed for the specific task, incurs significant overhead. How to enable these models to play roles other than those corresponding to their own tasks becomes a worthy issue. Multi-Task Learning(MTL) makes a visual task acquire the rich shareable knowledge from other tasks while joint training. It is fully explored in image Recognition tasks especially dense predict tasks. Nevertheless, it is rarely used in video domain due to the lack of multi-labels video data. In this paper, a heterogeneous data video multi-task prompt learning (VMTL) method is proposed to address above problem. It’s different from it in image domain, a Double-Layers Mapper(DLM) is proposed to extract the shareable knowledge into visual prompts and align it with representation of primary task to fine-tune the primary task. Extensive experiments prove that our DLM-VMTL performs better than baselines on 6 different video understanding tasks and 11 datasets.

关键词： Training visualization image recognition Signal processing Multitasking Acoustics Speech processing

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MLSwinTNet: A Multi-Level Feature Interaction Network for Low-Light image Enhancement

MLSwinTNet: A Multi-Level Feature Interaction Network for Lo...

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

作者： Mingyang Sun Xinxin Wang Ru Yi College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-Light image Enhancement (LLIE) is crucial for improving image quality and visual analysis. This study proposes MLSwinTNet, an LLIE network based on the Swin Transformer. The core MLSwinT module adopts a UNet-like design, integrating a dual-branch feature extraction module and a multi-level feature interaction module. The former combines CNN and Swin Transformer to efficiently capture both local and global features, while the latter optimizes detail and color restoration by adjusting and fusing feature levels. Experimental results demonstrate that MLSwinTNet outperforms state-of-the-art methods on standard datasets, achieving a PSNR gain of 27.33 dB and an SSIM score of over 0.96, providing a more competitive solution for LLIE.

关键词： Measurement visualization image color analysis Transformer cores Speech enhancement Feature extraction Transformers image restoration Standards image enhancement

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Prompt-UIE: A Unified Prompt-Driven Framework for Underwater image Enhancement

Prompt-UIE: A Unified Prompt-Driven Framework for Underwater...

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

作者： Yanling Zhang Linxuan Luo Pan Mu Cong Bai College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The complex and diverse underwater environment causes various types of degradation in underwater images. However, most existing methods focus on single underwater datasets, where the similarities in degradation limit the model’s exploration of different degradation characteristics. To address this challenge, we developed a new unified model for underwater image enhancement based on prompt learning, called PromptUIE, focusing on the common attributes of underwater images. Prompt-UIE is designed to adapt the pre-trained specific model to various underwater conditions without the need of multiple datasets. It builds upon a specific model and integrates a visual prompt module along with a reverse transmission map (RTM) guided loss function. First, the visual prompt module based on background light guides the specific model to perform enhancements based on different water types through a carefully designed visual prompt strategy. Next, the RTM guided loss function improves the model’s ability to handle non-uniform degradation. Experiments on both full-reference and no-reference datasets demonstrate the effectiveness and robustness of our method. The code is available at https://***/Zjut-MultimediaPlus/Prompt-UIE.

关键词： Degradation Adaptation models visualization Lighting Focusing Speech enhancement Signal processing Propagation losses Robustness image enhancement

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Optimal Performance of SCARA Robot System for Handling Objects Based on image processing

Optimal Performance of SCARA Robot System for Handling Objec...

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ieee SSD International Multi-conference on Systems, Signals and Devices

作者： Abdullah Al-Ani Ali Amer Ahmed Alrawi Yousif Al Mashhadany Sameer Algburi Kasim M. Al-Aubidy Electrical Engineering Department Engineering College University of Anbar Anbar Iraq Mechatronics Department Tishk International University Erbil Iraq

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper discusses the use of image processing to improve object handling in the SCARA robot system. The proposed approach aims to achieve faster and safer movement of the SCARA robot arm for object handling. During the experiment, the optimal performance modification determines the subsystem required to accommodate and compensate for the system's image processing capability. In general, images are processed at lower speeds, as higher speeds could potentially damage the object during system transportation. The results of the proposed approach indicate the development of new object-handling techniques for SCARA robot movements by introducing visual feedback to display and control objects. To improve object handling by the SCARA robot, several parameters in the image are described in the subsequent process. This new application of the SCARA robot system is significant, as the collision-free machine modifications emphasize the safety of the working setup. It was found that the precise manipulation of the SCARA robot when handling objects in open-loop control lacks sufficient technical representation.

关键词： Performance evaluation visualization image processing Transportation Tail Robot sensing systems Safety Collision avoidance Robots Optimization

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