A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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FreeNeRF introduces free frequency regularization to improve few-shot neural rendering by addressing frequency-related challenges in NeRF training.
Chirag Vashist, Shichong Peng, Ke Li
RS-IMLE improves few-shot image synthesis by redesigning the prior distribution to align training and inference latent codes, yielding higher quality images than existing GAN and IMLE methods.
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This paper introduces a few-shot neuro-symbolic imitation learning framework that combines symbolic planning with neural control for long-horizon tasks.
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This paper proposes a method to factorize spatio-temporal foundation models to reduce computational costs while maintaining zero/few-shot generalization performance.
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This paper demonstrates that scaling up language models greatly improves task-agnostic, few-shot performance.
Yisheng Song, Ting Wang, Puyu Cai, et al.
A comprehensive survey of 200+ few-shot learning papers, introducing a knowledge pyramid and analyzing challenges, applications, and future directions.
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This paper introduces Mma, a multi-modal adapter for vision-language models that enhances few-shot transfer learning by integrating visual and textual features.
Yan Hu, Qingyu Chen, Jingcheng Du, et al.
This paper shows that task-specific prompt engineering, incorporating medical knowledge and few-shot examples, significantly improves GPT-3.5 and GPT-4 performance on clinical NER tasks, though still below BioClinicalBERT.
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NuNER is a compact NER foundation model pre-trained via contrastive learning on GPT-3.5-annotated data, outperforming larger models in few-shot settings.
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Flamingo introduces a visual language model that processes interleaved images and text, achieving state-of-the-art few-shot learning across vision-language tasks.
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Parameter-efficient fine-tuning (PEFT) achieves better performance and lower cost than in-context learning for few-shot tasks.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.
This paper systematically surveys prompt-based learning in NLP, a paradigm where pre-trained language models are adapted to tasks via textual prompts for few-shot or zero-shot learning.