In this paper, a contrastive learning approach for morphological disambiguation (MD) using large language models (LLMs) is presented. A contrastive loss function is introduced for training the approach, which reduces the distance between the correct...
The widespread deployment of large language models (LLMs) has raised urgent demands for verifiable content attribution and misuse mitigation. Existing text watermarking techniques often struggle in black-box or sampling-based scenarios due to limitat...
Culturally inclusive large language models (LLMs) should adapt to heterogeneous value systems without conflating national survey aggregates with comprehensive intercultural competence. We introduce Prototype-driven Contrastive Cultural Alignment (Pro...
Cross-project defect prediction (CPDP) is a significant solution in software defect prediction (SDP) when historical data is unavailable. In CPDP, the defect data of another project (called the source project) is used to train the model. Different tr...
Text-attributed graphs (TAGs) couple graph topology with node-level text, but real data often contain spurious edges, missing links, and text–structure mismatch that destabilize learning under scarce labels. We propose CoACL (Coupled Augmentati...
Pre-trained language model-based neural rankers have been widely applied in information retrieval (IR). However, the robustness issue of current IR models has not received sufficient attention, which could significantly impact the user experience in...
Vision-language models (VLMs) are increasingly evaluated for retinal image interpretation, but end-task discrimination does not establish whether a prediction depends on the optical coherence tomography (OCT) image. We evaluated RetinaVLM, LLaVA-Med,...
Assessing severe scoliosis requires the analysis of posturographic X-ray images. One way to analyse these images may involve the use of open-source artificial intelligence models (OSAIMs), such as the contrastive language–image pretraining (CLI...
Synthesizing multi-phase contrast-enhanced CT images from non-contrast CT (NCCT) may provide complementary phase-specific cues for preliminary assessment. After rigorous clinical validation, such images could provide clinical decision support or aid...
Preserving long-tail, minority information during model compression has been linked to algorithmic fairness considerations. However, this assumes that large models capture long-tail information and smaller ones do not, which raises two questions. One...
With the growing demand for multilingual financial information, cross-lingual financial sentiment recognition faces significant challenges, including semantic misalignment, ambiguous sentiment expression, and insufficient transferability. To address...
Domain adaptation of pre-trained language models remains challenging, especially for specialized text collections that include distinct vocabularies and unique semantic structures. Existing contrastive learning methods frequently rely on generic mask...
This survey provides a comprehensive guide to Multimodal Large Language Models (MLLMs) with a focus on vision–language tasks, including image captioning, visual question answering, cross-modal retrieval, visual grounding, multi-image reasoning,...
Forestry is recognized as one of the most dangerous industries in the world. To enhance forestry safety, autonomous machinery and safety systems for such machinery are essential. This study aims to introduce large language models (LLMs)—especia...
With the progress of agricultural modernization, intelligent fruit harvesting is gaining importance. While fruit detection and recognition are essential for robotic harvesting, existing methods suffer from limited generalizability, including adapting...
Weakly supervised crack segmentation aims to create pixel-level crack masks with minimal human annotation, which often only differentiate between crack and normal no-crack patches. This task is crucial for assessing structural integrity and safety in...
Foundation models (FMs) and vision-language models (VLMs) have emerged as a transformative paradigm in artificial intelligence, reshaping the landscape of medical image analysis and clinical decision support. Building upon large-scale pretraining on...
Zero-shot classification presents a challenge since it necessitates a model to categorize images belonging to classes it has not encountered during its training phase. Previous research in the field of remote sensing (RS) has explored this task by tr...
Multimodal sentiment analysis aims to acquire and integrate sentimental cues from different modalities to identify the sentiment expressed in multimodal data. Despite the widespread adoption of pre-trained language models in recent years to enhance m...
Text classification aims to classify text according to pre-defined categories. Despite the success of existing methods based on the fine-tuning paradigm, there is a significant gap between fine-tuning and pre-training. Currently, prompt learning meth...
Large language models have demonstrated impressive capabilities in many domains. But they sometimes generate irrelevant or nonsensical text, or produce outputs that deviate from the provided input, an occurrence commonly referred to as hallucination....
Bug localization is the task of automatically identifying the source files responsible for a reported defect. It is a critical step in software maintenance that accelerates defect resolution. Information retrieval (IR) methods are simple and effectiv...
Medical imaging description and disease diagnosis are vitally important yet time-consuming. Automated diagnosis report generation (DRG) from medical imaging description can reduce clinicians’ workload and improve their routine efficiency. To ad...
Weakly supervised video anomaly detection (WS-VAD) localizes anomalous events in untrimmed videos using only video-level annotations. While CLIP-based methods have advanced this task through vision–language alignment, widely adopted approaches...
To address the limitation of 2D methods in inferring absolute scrap dimensions from images, we propose Scrap-SAM-CLIP (SSC), a vision-language model integrating the segment anything model (SAM) and contrastive language-image pre-training in Chinese (...
Automatic radiology report generation is a task that combines artificial intelligence and medical information processing, and it fully relies on computer vision and natural language processing techniques. Nowadays, automatic radiology report generati...
Remote sensing vision–language models, such as RemoteCLIP and GeoRSCLIP, have advanced image–text representation learning. However, they rely on manually curated caption datasets that are expensive to scale and provide only global image-l...
Large Language Models (LLMs) have demonstrated extraordinary capabilities in natural language generation; however, their real-world deployment is frequently hindered by the generation of factually incorrect or biased content, along with an inherent d...
Citation intent classification identifies the underlying functions of scholarly citations and provides semantic information beyond citation counts. However, existing methods pay limited attention to temporal information in citation relationships and...
Temporal Knowledge Graph Inference (TKGI) is a cornerstone for intelligent decision-making in dynamic scenarios, but existing models face critical bottlenecks, including inadequate complex-context modeling, a lack of entity importance quantification,...
Aesthetics primarily focuses on the study of art, encompassing the aesthetic categories of beauty and ugliness, as well as human aesthetic activities. Image Aesthetics Assessment (IAA) seeks to automatically evaluate the aesthetic quality of images b...
In large-scale state-owned enterprise audit and compliance tasks, ensuring that similar violations receive consistent disciplinary decisions is essential for procedural fairness and institutional credibility. However, existing retrieval methods face...
With the proliferation of mobile internet and location-based services, location-based social networks (LBSNs) have accumulated extensive user check-in data, driving the advancement of next Point-of-Interest (POI) recommendation systems. Although exis...
Photoplethysmography (PPG) has emerged as an attractive modality for non-invasive cardiovascular monitoring due to its low cost, unobtrusive nature, and ubiquity in consumer wearable devices. Despite its potential, existing PPG-based arrhythmia detec...
Generalized category discovery (GCD) aims to classify known categories while discovering novel ones in unlabeled data, yet existing methods lack mechanisms to correct boundary-ambiguous samples that receive noisy pseudo-labels, as they primarily rely...
With the rapid advancement of global digital transformation, enterprises and financial institutions face increasing challenges in managing and processing receipt-like financial documents. Traditional manual document processing methods can no longer m...
The main challenge in long-text image–text retrieval lies in the fact that some existing vision–language models mainly rely on global image and text features for matching. Without explicit local alignment constraints, these models may str...
We extended real estate property listings on the online prop-tech platform. On the platform, the images were classified into the specified classes according to quality criteria. The necessary interventions were made by measuring the platform’s...
Fine-grained Multimodal Named-Entity Recognition and Grounding (FMNERG) aims to extract entity–type–object triples from image–text pairs. However, existing methods still struggle with two major challenges: distinguishing semanticall...
Retrieval-augmented generation (RAG) has emerged as an effective approach for analyzing massive and diverse data. It offers promising avenues for energy management and intelligent decision support amid the accelerating digital transformation of the p...
Multimodal Knowledge Graphs (MMKGs) offer a promising paradigm for integrating heterogeneous sources into a unified, queryable, semantically structured representation. However, existing MMKG construction pipelines remain predominantly text-centric, e...
Achieving sustainable design requires balancing environmental performance, resource efficiency, functional feasibility, and aesthetic acceptance throughout the product life cycle. However, traditional design approaches often struggle to quantitativel...
Research in visual intelligence has shifted from task-specific convolutional networks trained on curated labels toward large-scale pretrained architectures that transfer across image, video, and language tasks with minimal fine-tuning. Existing surve...
With the exponential growth of multimodal data, the limitations of traditional unimodal models in cross-modal understanding and complex scenario reasoning have become increasingly evident. Built upon the foundation of Large Language Models (LLMs), Mu...
Olfactory information, crucial for human perception, is often underrepresented compared to visual and textual data. This work explores methods for understanding smell descriptions within a multimodal context, where scent information is conveyed indir...
While sign language combines sequential hand motions with concurrent non-manual cues (e.g., mouth shapes and head tilts), current recognition systems lack multimodal annotation methods capable of capturing their hierarchical semantics. To bridge this...
The stylistic emulation of historical building facades presents significant challenges for artificial intelligence (AI), particularly for complex and data-scarce styles like Krakow’s Eclecticism. This study aims to develop a methodological fram...
The CNI model generates C, N, and I parameters to measure people’s mental processes—consequence sensitivity (C), norm sensitivity (N), and generalized inaction/action preferences (I)—in moral decision making. Given the limitations o...
Short videos have become a primary medium for news delivery, but their low cost, rapid diffusion, and multimodal nature make misinformation easier to generate and harder to verify. Existing methods often rely on single-modality cues or shallow cross-...
Sentiment analysis plays a crucial role in domains such as financial news, user reviews, and public opinion monitoring, yet existing approaches face challenges when dealing with long and domain-specific texts due to semantic dilution, insufficient co...
of 15