Papers Zero-Shot Learning
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Improving Cross-Lingual Token Representations by Adding a Pinch of SALT
Cross-lingual sentence encoders enable scalable transfer across hundreds of languages, powering applications such as translation mining and zero-shot learning in low-resource settings. Although trained for sentence-level…
Zero-Shot LearningCAST: Closed-form Analytic Semantic Transfer for Zero-Shot Classifier Extension
Large pre-trained models have become foundational components of modern machine learning systems. Yet adapting these models to novel categories typically requires examples from the target distribution. In many domains, ho…
Zero-Shot LearningPrototype Adaptation for Zero-Shot sEMG Movement Classification
Surface electromyography (sEMG) enables the control of prostheses, allowing upper-limb amputees to re-gain some hand function. Most current research focuses on recognizing basic movements for prosthesis control. However,…
Zero-Shot LearningEvaluating Vision-Language Models as a Zero-Shot Learning Alternative to You Only Look Once and Optical Character Recognition for Nigerian License Plate Recognition
License Plate Recognition (LPR) systems are critical tools in traffic monitoring, security enforcement, and urban mobility management. Traditional LPR systems often rely on a multi-stage pipeline involving object detecti…
License Plate RecognitionZero-Shot LearningObject DetectionCV-DCLR: Causal-Visual Dynamic Label Refinement for Robust Zero-Shot Learning
Zero-Shot Learning (ZSL) facilitates knowledge transfer via shared semantic spaces. However, a critical bottleneck in this paradigm is Semantic Entanglement, where visual representations are inevitably conflated with vis…
Zero-Shot LearningEmergent Alignment
Can Large Language Models (LLMs) discern when their own outputs are misaligned with human ethics? And can they self-correct? We endow an LLM with a conscience step that reviews its own reasoning and outputs, and we exten…
Zero-Shot LearningDehaze-GaussianImage: Zero-Shot Dehazing via Efficient 2D Gaussian Splatting Representation
Existing single image dehazing methods are often constrained by computational redundancy in pixel-level optimization and the lack of physical interpretability in implicit neural networks. These limitations hinder the bal…
Single Image DehazingZero-Shot LearningPhysics-Driven Zero-Shot MRI Reconstruction with Non-local Image Priors
Zero-Shot Self-Supervised Learning (ZS-SSL) has emerged as a promising paradigm for accelerated Magnetic Resonance Imaging (MRI) reconstruction, eliminating the reliance on fully-sampled external datasets. However, learn…
Self-Supervised LearningZero-Shot LearningMRI ReconstructionLabel Shift Aware Adaptation for Online Zero-shot Learning with Contrastive Language-Image Pre-Training (CLIP)
Vision-language models like Contrastive Language-Image Pre-Training (CLIP) have been extensively studied in data-scarce scenarios. A particularly challenging and realistic task in this area is online zero-shot learning w…
Zero-Shot LearningDomain AdaptationClosing the Modality Gap in Zero-Shot HAR: Contrastive Training and Separability-Optimized Prototypes on IMU Data
Zero-shot learning (ZSL) for inertial measurement unit (IMU)-based human activity recognition (HAR) faces a central challenge: bridging the gap between sensor embeddings and semantic class representations. We systematica…
Human Activity RecognitionZero-Shot LearningZero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline
Large Visual Language Models (LVLMs) have achieved remarkable success in vision tasks. However, the significant differences between industrial and natural scenes make applying LVLMs challenging. Existing LVLMs rely on us…
Zero-Shot LearningDomain AdaptationFrom Data to Insights: Exploring Program-of-Thoughts Prompting for Chart Summarization
Charts play a critical role in conveying numerical data insights through structured visual representations. However, semantic visual understanding and numerical reasoning requirements hinder the accurate description of c…
Zero-Shot LearningDynamic Visual-semantic Alignment for Zero-shot Learning with Ambiguous Labels
Zero-shot learning (ZSL) aims to recognize unseen classes without visual instances. However, existing methods usually assume clean labels, overlooking real-world label noise and ambiguity, which degrades performance. To …
Zero-Shot LearningPrompt-Driven Code Summarization: A Systematic Literature Review
Software documentation is essential for program comprehension, developer onboarding, code review, and long-term maintenance. Yet producing quality documentation manually is time-consuming and frequently yields incomplete…
Prompt EngineeringZero-Shot LearningCLIP Architecture for Abdominal CT Image-Text Alignment and Zero-Shot Learning: Investigating Batch Composition and Data Scaling
Vision-language models trained with contrastive learning on paired medical images and reports show strong zero-shot diagnostic capabilities, yet the effect of training batch composition on learned representations remains…
Contrastive LearningZero-Shot LearningSentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?
Sentiment analysis in software engineering focuses on understanding emotions expressed in software artifacts. Previous research highlighted the limitations of applying general off-the-shelf sentiment analysis tools withi…
Sentiment AnalysisZero-Shot LearningIncentivizing Generative Zero-Shot Learning via Outcome-Reward Reinforcement Learning with Visual Cues
Recent advances in zero-shot learning (ZSL) have demonstrated the potential of generative models. Typically, generative ZSL synthesizes visual features conditioned on semantic prototypes to model the data distribution of…
Reinforcement LearningZero-Shot LearningMutually Causal Semantic Distillation Network for Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize the unseen classes in the open-world guided by the side-information (e.g., attributes). Its key task is how to infer the latent semantic knowledge between visual and attribute f…
Zero-Shot LearningA Simple Efficiency Incremental Learning Framework via Vision-Language Model with Nonlinear Multi-Adapters
Incremental Learning (IL) aims to learn new tasks while preserving previously acquired knowledge. Integrating the zero-shot learning capabilities of pre-trained vision-language models into IL methods has marked a signifi…
Incremental LearningZero-Shot LearningCarbonBench: A Global Benchmark for Upscaling of Carbon Fluxes Using Zero-Shot Learning
Accurately quantifying terrestrial carbon exchange is essential for climate policy and carbon accounting, yet models must generalize to ecosystems underrepresented in sparse eddy covariance observations. Despite this cha…
Time Series RegressionZero-Shot LearningTransfer Learning