With the surge in realistic text tampering, detecting fraudulent text in images has gained prominence for maintaining information security. However, the high costs associated with professional text manipulation and annotation limit the ...

• A new dataset for text manipulation detection with diverse handcraft manipulations
• An asymmetric dual-stream baseline framework to exploit different transformed domains
• An aggregation hub and a fusion module for efficient multi-...

The accurate segmentation of retinal vessels (RV) and Foveal Avascular Zone (FAZ) is crucial for the retina health assessment. However, fusing OCT and OCTA images while projecting these 3D data for segmentation is a challenge. In this paper, we ...

• Propose a novel multi-modal fusion enhancement network for RV and FAZ joint segmentation based on OCT and OCTA.
• Design an end-to-end kernel adaptive projection (KAPM) for 3D to 2D and volume fusion (VFM) for data reuse.
• Achieve ...

LiDAR-only 3D detection methods struggle with the sparsity of point clouds. To overcome this issue, multi-modal methods have been proposed, but their fusion is a challenge due to the heterogeneous representation of images and point clouds. This ...

• LiDAR-based 3D detection faces point cloud sparsity challenges.
• A novel Homogeneous Sparse Fusion multi-modal approach is introduced.
• Homogeneous Sparse Fusion adaptively extracts foreground features.
• Cross-modality consistency ...

Data from multi-modal sensors, such as Red-Green-Blue (RGB) cameras, thermal cameras, microphones, and mmWave radars, have gradually been adopted in various classification problems for better accuracy. Some sensors, like RGB cameras and microphones, ...

Multi-modal knowledge graphs (MKGs) contain diverse modalities of feature, such as text, audio and image. Due to the emergence of novel entities or an insufficiency in multi-modal corpora, MKGs confront data incompleteness frequently. Thus, MKGs ...

Irony detection is a complex task that often stumps both humans, who frequently misinterpret ironic statements, and artificial intelligence (AI) systems. While the majority of AI research on irony detection has concentrated on linguistic cues, the role ...

Identifying urban land-use types is crucial for effective resource management, urban planning, and sustainable development. However, classifying land use is complex due to the complexity of the city and the poor data available in undeveloped areas. In ...

The rapid evolution and advancement of technology in adult healthcare have paved the way for improving infant care through enhanced monitoring and assistance systems. Traditional infant monitoring systems face challenges, including the need for large ...

With the rapid development of generative models, multimodal fake media has proliferated across the Internet. Detecting and Grounding forgery images and text is crucial in advancing cybersecurity. Most existing approaches utilize image-text ...

Human-centric perception tasks are essential to many human-agent interaction applications. Unsupervised multi-modal human-centric pre-training models have been recently proposed to lay the foundation for various downstream tasks. However, existing ...

• A comprehensive review of correlation filter-based tracking algorithms.
• Review of feature-based classification in correlation filter-based tracking.
• Review of recent deep learning-based correlation filter tracking from different ...

In recent years, correlation filter-based (CF) tracking algorithms have gained momentum in the field of visual tracking. CF tracking algorithms have achieved compelling performance by addressing its limitations such as boundary effect and filter ...

In this study, we proposed a wavelet-based deep learning network to estimate retinal function from retinal structure in patients with Retinitis Pigmentosa. We used macular integrity assessment microperimetry to measure retinal sensitivities (...${}^{\mathrm{}}$

Early screening and classification of Age-related Macular Degeneration (AMD) are crucial for precise clinical treatment. Currently, most automated methods focus solely on dry and wet AMD classification. However, the classification of wet AMD into ...

Compound-protein interaction (CPI) prediction plays a crucial role in drug discovery and drug repositioning. Early researchers relied on time-consuming and labor-intensive wet laboratory experiments. However, the advent of deep ...

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• Introducing MMCL-CPI, a novel method for CPI prediction integrating sequence and image modalities.
• The multi-modal CPI model outperforms others across various datasets.
• Pioneering multi-modal pre-training in bioinformatics, our ...

Smart Fashion is reshaping people’s lives, and affects people’s choices and outfits. Existing computer-vision-enabled fashion technology has covered many aspects, such as fashion detection, fashion recognition, fashion segmentation, virtual ...

Visual Geo-localization (VG) refers to the process to identify the location described in query images, which is widely applied in robotics field and computer vision tasks, such as autonomous driving, metaverse, augmented reality, and SLAM. In fine-...

The promotional video "CPC" serves as a vital material for showcasing the image of the Communist Party of China around the world. By integrating multi-modalities such as visual, auditory, and textual elements, it effectively conveys information, ...

Diffusion models have garnered great interest lately in Magnetic Resonance (MR) image reconstruction. A key component of generating high-quality samples from noise is iterative denoising for thousands of steps. However, the complexity of ...

• We propose a ODE-based flow method to solve the accelerated MR Imaging problem and apply domain transfer to MRI reconstruction task for the first time. It is a multi-modal method that transfer domain from undersampled k-space measurements ...

3D facial reconstruction and animation has advanced significantly in the past decades. However, existing methods based on single-modal input struggle with specific facial part control and require post-processing for natural rendering. Recent approaches ...

3D semantic segmentation is significant for scene understanding in various domains, such as autonomous driving, mapping, and robotics. Existing research often enhances prediction accuracy by integrating data from camera and LiDAR (light detection ...

• A multi-modal network architecture APPFNet is proposed based on Transformer.
• A new module NFAM is proposed to enhance point cloud information.
• APPFNet performs semantic segmentation using images and complete point clouds.
• The ...