Zero-Shot Learning: Unlocking AI’s Potential Beyond Labeled Data

Latest 23 papers on zero-shot learning: Aug. 17, 2025

Zero-shot learning (ZSL) has emerged as a groundbreaking frontier in AI/ML, enabling models to recognize, process, or act upon data categories they’ve never explicitly seen during training. This capability is paramount for real-world applications where obtaining vast amounts of labeled data is impractical, expensive, or impossible. From medical imaging to industrial quality control, and even understanding human cognition, ZSL promises to unlock new levels of adaptability and intelligence. Recent research has pushed the boundaries of ZSL across diverse domains, showcasing innovative approaches that blend vision-language models, specialized architectures, and novel pre-training strategies. Let’s dive into some of these exciting breakthroughs.

The Big Idea(s) & Core Innovations

The core challenge in zero-shot learning is to enable models to generalize to unseen classes. Many recent innovations revolve around leveraging rich, pre-existing knowledge and frameworks like vision-language models (VLMs) and large language models (LLMs) to bridge the gap between seen and unseen data. For instance, in industrial quality control, the paper MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning by Ylli Sadikaj and colleagues introduces a novel approach for multi-type anomaly detection and segmentation. Their key insight is using defect-specific knowledge from pre-trained VLMs to improve precision in identifying various defect types, even those never encountered before. This is the first work to tackle multi-type anomaly segmentation in a zero-shot setting.

Similarly, the ability to understand and predict complex interactions is critical. Funnel-HOI: Top-Down Perception for Zero-Shot HOI Detection presents a top-down perception model for zero-shot Human-Object Interaction (HOI) detection, demonstrating how hierarchical reasoning can significantly improve generalization to unseen object categories. This contrasts with approaches that might struggle with novel combinations.

The concept of leveraging diverse knowledge sources is also evident in A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and Disorder by Xinxu Wei and the Lehigh University team. They introduce BrainGFM, a pioneering brain foundation model that integrates multiple brain parcellations and atlases. Their hybrid graph prompt-tuning and language prompting approach allows for few-shot and zero-shot adaptation across previously unseen neurological disorders, a crucial step for clinical translation where labeled patient data is scarce.

Furthermore, zero-shot capabilities are transforming how we approach complex scientific and engineering problems. Discovery Learning accelerates battery design evaluation by Jiawei Zhang and colleagues at the University of Michigan and Farasis Energy introduces ‘Discovery Learning’ (DL), a paradigm combining active learning, physics-guided learning, and zero-shot learning. DL enables efficient and rapid prediction of battery lifetime with minimal experimental data, drastically reducing development time and energy consumption. This showcases how ZSL can accelerate real-world R&D by minimizing the need for extensive prototyping.

In the realm of robotic manipulation, Ag2x2: Robust Agent-Agnostic Visual Representations for Zero-Shot Bimanual Manipulation by Ziyin Xiong and UC Berkeley researchers offers an agent-agnostic framework. Their robust visual representations enable zero-shot bimanual manipulation tasks without requiring expert demonstrations or complex engineered rewards, paving the way for more autonomous and versatile robots.

Finally, for vision-language models (VLMs) themselves, the paper Accelerating Conditional Prompt Learning via Masked Image Modeling for Vision-Language Models by Phuoc-Nguyen Bui and co-authors from Sungkyunkwan University and Deakin University, introduces ProMIM. This plug-and-play framework uses masked image modeling (MIM) to generate robust, instance-conditioned prompts, improving generalization and mitigating overfitting in VLMs for zero-shot scenarios.

Under the Hood: Models, Datasets, & Benchmarks

These advancements are powered by innovative model architectures, novel datasets, and rigorous benchmarking:

• MultiADS (Anomaly Detection): Leverages pre-trained vision-language models (e.g., CLIP) and introduces a novel Knowledge Base for Anomalies (KBA) to enhance defect-aware text prompts. Evaluated on five benchmark datasets: MVTec-AD, Visa, MPDD, MAD, and Real-IAD. Code: https://github.com/boschresearch/MultiADS
• BrainGFM (Neuroscience): A graph-based foundation model for fMRI data. Constructed a massive pre-training dataset of 25,000 subjects, 60,000 scans, and 400,000+ graph samples, integrating 25 disorders and 8 brain parcellations. Code: Not explicitly provided but paper is https://arxiv.org/pdf/2506.02044.
• Discovery Learning (Battery Design): A novel machine learning paradigm integrating active learning, physics-guided learning, and zero-shot learning. Uses proprietary battery lifetime data to achieve significant error reduction with minimal experimental cycles. Code: https://github.com/FarasisEnergy/DiscoveryLearning
• Ag2x2 (Robotics): Focuses on robust visual representations for bimanual manipulation. Utilizes general purpose computer vision tools like YOLOv5 for object detection (Code: https://github.com/ultralytics/yolov5). Resources: https://ziyin-xiong.github.io/ag2x2.github.io/
• ProMIM (Vision-Language Models): A plug-and-play framework using masked image modeling (MIM) for conditional prompt learning in VLMs. Evaluated on standard VLM benchmarks to demonstrate improved generalization. Code: https://arxiv.org/abs/2411
• Prototype-Guided Curriculum Learning (PGCL) for ZSL: Introduces dynamic curriculum generation based on prototype-based clustering. Demonstrates improved performance on multiple benchmark tasks. Code: https://github.com/your-organization/pgcl.
• PSRP-CPI (Drug Discovery): A pre-training method for compound-protein interaction (CPI) prediction using subsequence reordering and length-variable augmentation. Demonstrated on four widely used CPI benchmark datasets. Code: https://github.com/Hoch/Zhang/DrugDiscovery-DTI/
• UPRE (Zero-Shot Domain Adaptation for Object Detection): Utilizes multi-view prompts and a visual representation enhancement module to generate domain style variations. Code: https://github.com/AMAP-ML/UPRE
• CLIP-Fed (Federated Learning Security): The first VLP-guided FL backdoor defense framework, utilizing CLIP for global model rectification. Outperforms existing methods on CIFAR-10. Code: https://anonymous.4open.science/r/CLIP-Fed
• Zero-shot self-supervised learning of single breath-hold magnetic resonance cholangiopancreatography (MRCP) reconstruction: Introduces a novel shallow training approach for MRCP reconstruction. Provides an open dataset for research and validation. Code: https://github.com/wtclarke/pymapvbvd, https://github.com/mckib2/pygrappa
• Modelling and Classifying the Components of a Literature Review: Introduces Sci-Sentence, a multidisciplinary benchmark with manually and automatically annotated sentences, evaluating 37 LLMs. Code: https://github.com/fcobolanos/Classifying-the-Components-of-a-Literature-Review/tree/main/code

Impact & The Road Ahead

The ripple effect of these zero-shot learning advancements is profound. From accelerating drug discovery and optimizing industrial processes to enhancing medical diagnostics and ensuring AI security, ZSL is making AI systems more adaptable, robust, and deployable in data-scarce environments. The ability to generalize without extensive new data collection or retraining is a game-changer for deploying AI in rapidly evolving or highly specialized fields.

Looking ahead, the convergence of zero-shot learning with large-scale foundation models, as seen in BrainGFM and the use of VLMs, points towards increasingly powerful and versatile AI. The lessons learned from areas like cybersecurity, where LLMs currently struggle with consistency (as highlighted in Large Language Models are Unreliable for Cyber Threat Intelligence), underscore the importance of robust evaluation and the continuous need for innovative ZSL techniques to handle real-world complexities. Further research will likely focus on improving the interpretability of ZSL models, refining prompt engineering for better generalization, and exploring its applications in dynamic, real-time scenarios, such as autonomous systems (e.g., driver attention modeling in VISTA and wireless communication adaptation with LLMs, as discussed in Large Language Models for Wireless Communications: From Adaptation to Autonomy). The journey toward truly intelligent, adaptable AI capable of understanding the unknown has just begun, and zero-shot learning is leading the charge.