Aspect-based sentiment analysis (ABSA) plays a vital role in deriving fine-grained sentiment from textual content. As large language models (LLMs) are increasingly adopted for automated data annotation in natural language processing (NLP), concerns h...
With the growing scale of pre-trained language models (PLMs), full parameter fine-tuning becomes prohibitively expensive and practically infeasible. Therefore, parameter-efficient adaptation techniques for PLMs have been proposed to learn through inc...
Event cameras capture sparse, high-temporal-resolution visual information, making them attractive for challenging scenarios with fast motion and severe illumination changes. However, event-based depth models trained on one real-world benchmark often...
The rapid evolution of online learning environments has generated diverse and complex data ecosystems. Recommender systems play a central role in leveraging such heterogeneous data to support personalised learning experiences. However, many deep lear...
Adapting Large Language Models (LLMs) to specialized domains like geriatric care remains a significant challenge due to the limited availability of domain-specific data and the difficulty of achieving efficient yet effective fine-tuning. Current meth...
End-to-end voice cloning models like VITS require large amounts of data to produce high-quality speech, making speaker adaptation difficult when target data is limited. To address this problem, this paper introduces AdaptVITS, a parameter-efficient f...
Accurate rooftop segmentation from aerial imagery is essential for large-scale urban analysis, including applications such as solar potential assessment and urban monitoring. However, it remains constrained by the high cost of dense annotation and th...
Automated CT lung nodule classification suffers from domain shift across centers. We propose LoRA-GRL, combining Low-Rank Adaptation (LoRA) with adversarial domain harmonization via a Gradient Reversal Layer. Rank-8 LoRA modules were inserted into al...
Colorectal polyp segmentation requires accurate boundary delineation, but adapting large vision foundation models to endoscopic images can be computationally expensive. This study investigates whether the Segment Anything Model (SAM) can be specializ...
Recent studies on parameter-efficient fine-tuning (PEFT) have introduced effective and efficient methods for fine-tuning large language models (LLMs) on downstream tasks using fewer parameters than required by full fine-tuning. Low-rank decomposition...
Dynamic Facial Expression Recognition (DFER) has recently attracted significant interest due to its vital role in enabling empathetic and human-compatible technologies. Developing models that remain robust under in-the-wild variability is a key motiv...
Whisper is a transformer-based multilingual model that has illustrated state-of-the-art behavior in numerous languages. However, the efficiency remains persistent with the limited computational resources. To address this issue, an experiment was perf...
Reliable monocular depth estimation (MDE) is essential for agricultural robots and unmanned platforms, where low-cost visual perception is required for safe navigation and scene understanding in complex field environments. However, general-purpose de...
Parameter-efficient fine-tuning (PEFT) enables large language models to adapt to downstream tasks with low computational cost. As a representative high-rank PEFT method, MoRA (High-Rank Updating for Parameter-Efficient Fine-Tuning) improves update ex...
Accurate estimation of pasture biomass components from imagery is essential for sustainable grazing management and precision agriculture. Conventional methods such as destructive harvesting, rising plate meters, and remote sensing are limited by scal...
To address the trade-off between parameter scale and generation quality in Vision-Language Models (VLMs), this study proposes a Multi-Feature Dynamic Instruction Tuning (MFDIT) image captioning model based on LLaMA. By integrating CLIP-based global f...
The digital transformation of oil and gas pipeline networks has generated substantial volumes of unstructured maintenance documentation from communication systems, creating an urgent need for automated summarization to improve operational efficiency....
This study focuses on the neural machine translation task for the TR-EN language pair, which is considered a low-resource language pair. We investigated fine-tuning strategies for pre-trained language models. Specifically, we explored the effectivene...
Parameter-efficient fine-tuning (PEFT) reduces adaptation costs, but the predictive trade-offs may vary across datasets and encoder backbones. This study evaluated Hybrid Prefix–Prompt tuning for sentiment classification using BERT-base-uncased...
The challenge of detecting nuanced, context-dependent offensive language highlights the need for Large Language Model (LLM) adaptation strategies that can effectively address inherent data and task asymmetries. Standard Parameter-Efficient Finetuning...
Multimodal aspect-based sentiment analysis (MABSA) predicts the sentiment expressed toward a target aspect by jointly using textual and visual information, supporting fine-grained opinion analysis in product reviews, brand monitoring, and customer fe...
Legal judgment prediction (LJP) increasingly relies on large language models whose full fine-tuning is memory-intensive and susceptible to catastrophic forgetting. We present LawLLM-DS, a two-stage Low-Rank Adaptation (LoRA) framework that first perf...
Remote sensing image segmentation is a foundational task in Earth observation. With the rapid growth of remote sensing datasets in terms of scale, modality diversity, semantic openness, and spatio-temporal complexity, the field is evolving from task-...
Large language models (LLMs) have demonstrated strong potential in medical knowledge applications; however, their reliability in knowledge-intensive medical reasoning—remains limited due to hallucination, inadequate domain grounding, and unstab...
Extremely low-resource machine translation poses a difficult cross-lingual adaptation problem: improving target-language performance while preserving capabilities acquired through multilingual pretraining. This study compares three mathematically dis...
Automated Optical Inspection (AOI) of Printed Circuit Boards (PCBs) suffers from scarce labeled data and frequent domain shifts caused by variations in camera optics, illumination, and product design. These limitations hinder the development of accur...
Source code plagiarism is a significant challenge in software engineering and computer science education, affecting academic integrity, intellectual property rights, and software quality assurance. However, Source Code Plagiarism Classification (SCPC...
High-quality text corpora are essential for knowledge graph construction and domain-specific large model pre-training in technology-intensive industries, with the steel metallurgy sector serving as a representative case. However, many industrial docu...
In industrial scenarios, image segmentation is essential for accurately identifying defect regions. Recently, the emergence of foundation models driven by powerful computational resources and large-scale training data has brought about a paradigm shi...
Image captioning with pretrained vision and language models often requires substantial model adaptation, while lightweight settings restrict the number of components that can be updated. This study investigates an input-level visual conditioning appr...
Pointer gauges remain widely deployed in industrial environments because they are inexpensive, resistant to electromagnetic interference, and readable from a distance. However, automatic reading remains difficult in practice because reliable predicti...
High-resolution satellite imagery is pivotal for accurate analysis in remote sensing applications, including land-use monitoring, urban planning, and environmental assessment. However, obtaining such data is often costly and limited. Consequently, su...
Full fine-tuning of large-scale vision-language models for video action recognition incurs prohibitive computational cost and often degrades pre-trained spatial representations. To address this, we propose VETA-CLIP, a Video Efficient Temporal Adapta...
Multispectral remote sensing image super-resolution (RSISR) aims to reconstruct high-frequency details while preserving cross-band structural consistency under strict computational budgets. However, real-world satellite imagery exhibits heterogeneous...
Knowledge graph completion (KGC) is commonly studied under static settings, whereas real-world knowledge graphs evolve continuously with newly emerging entities, relations, and facts. In continual knowledge graph completion (CKGC), the model is requi...
Recent advancements in generative artificial intelligence have significantly progressed the field of text-to-music generation, enabling users to create music from natural language descriptions. Despite the success of various models, such as MusicLM,...
The accurate modelling of video memorability, or the intrinsic properties that render a piece of audiovisual content more likely to be remembered, will facilitate the development of automatic systems that are more efficient in retrieving, classifying...
Infrared small target detection (ISTD) plays a crucial role in many real-world applications. However, this task remains highly challenging due to the extremely small target size, low contrast, and complex background interference as infrared small tar...
Video compression is central to large-scale video delivery, where better rate–distortion efficiency directly reduces bandwidth and storage cost. A practical way to improve efficiency is to encode a low-resolution video stream with a standard co...
Financial return prediction increasingly relies on both financial text and structured market covariates, but adapting large language models across financial institutions remains difficult because raw data cannot be centralized and clients often exhib...
Time-series foundation models are designed to continue observed trajectories, whereas model predictive control (MPC) must compare counterfactual rollouts under candidate future actions. A forecaster whose predictions are invariant to those actions ca...
Multi-label remote sensing scene classification (MLRSSC) requires autonomous discovery of all relevant land-cover categories without human guidance. Conventional expert classifiers return only label vectors without spatial evidence, while foundation...
Semantic change detection aims to localize changed regions and identify the corresponding land-cover transitions from bi-temporal remote sensing images, which is crucial for applications such as urban expansion analysis, disaster assessment, and envi...
Rapidly growing medical imaging volumes have increased radiologist workloads, creating demand for automated tools that support interpretation and reduce reporting delays. Vision-language models (VLMs) can generate clinically relevant captions to acce...
Stacking task adapters on a frozen domain adapter is a common approach for modular specialization of large language models, usually justified by knowledge injection; whether the surface-form graders standard in this setting can measure such specializ...
Emergency-information analysts need to organize image-text reports into humanitarian categories before reports can be verified and prioritized. This study asks whether fine-grained evidence refinement can be added to an established classifier without...
Fine-tuning is often treated as a prerequisite for deploying large language models in low-resource languages, yet its value for recent open-weight models is unclear. We evaluate eleven checkpoints of 7–14 billion parameters before and after Low...
Applying large language models (LLMs) to industrial fault classification is hindered by the mismatch between tabular sensor data and text-based inputs and by the high memory cost of fine-tuning billion-parameter models on edge hardware. This paper pr...
Adverse weather poses a major challenge to ground-based electro-optical (EO) aircraft surveillance. Existing parameter-efficient fine-tuning (PEFT) methods operate mainly in the spatial domain and treat weather as a generic domain shift rather than a...
Construction work zones are a difficult case for open-vocabulary semantic segmentation. Their layouts are temporary, safety-relevant objects that are often small and long-tailed, and generic models readily confuse them with background. We address the...
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