From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models (2506.07280v2)

Abstract: Video Diffusion Models (VDMs) have emerged as powerful generative tools, capable of synthesizing high-quality spatiotemporal content. Yet, their potential goes far beyond mere video generation. We argue that the training dynamics of VDMs, driven by the need to model coherent sequences, naturally pushes them to internalize structured representations and an implicit understanding of the visual world. To probe the extent of this internal knowledge, we introduce a few-shot fine-tuning framework that repurposes VDMs for new tasks using only a handful of examples. Our method transforms each task into a visual transition, enabling the training of LoRA weights on short input-output sequences without altering the generative interface of a frozen VDM. Despite minimal supervision, the model exhibits strong generalization across diverse tasks, from low-level vision (for example, segmentation and pose estimation) to high-level reasoning (for example, on ARC-AGI). These results reframe VDMs as more than generative engines. They are adaptable visual learners with the potential to serve as the backbone for future foundation models in vision.

• The paper demonstrates that video diffusion models can adapt to various visual tasks with minimal training data using classifier-free diffusion and LoRA fine-tuning.
• It introduces a framework where tasks are modeled as visual transitions, enabling applications in segmentation, pose estimation, and abstract reasoning.
• Experimental results show competitive performance, especially in the ARC-AGI benchmark, underscoring VDMs’ potential as flexible learners in data-limited scenarios.

Overview of Few-Shot Learning in Video Diffusion Models

The paper "From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models" by Acuaviva et al. explores the novel application of Video Diffusion Models (VDMs) beyond their traditional generative tasks, examining their latent capabilities in few-shot learning. Through a finely crafted experimental setup, the research investigates how pre-trained VDMs can be adapted for a wide range of visual tasks using minimal training data, highlighting their potential as generalizable and efficient learners.

The authors propose a framework wherein tasks are expressed as visual transitions, enabling the application of Classifier-Free Diffusion Models to tasks such as segmentation, pose estimation, and even abstract visual reasoning (ARC-AGI). This process leverages parameter-efficient fine-tuning techniques, particularly Low-Rank Adaptation (LoRA), to allow VDMs to internalize structured representations from a small number of examples. Key aspects of their methodology include constructing artificial sequences to train the models, leveraging interpolation functions, and employing systematic fine-tuning strategies without altering the core generative capacity of the original model.

Key Contributions and Results

The paper brings forward three main contributions:

1. A unified few-shot learning framework to utilize VDMs for a variety of tasks efficiently.
2. Comprehensive experimental validation showcasing the emergent capabilities of VDMs in handling diverse visual assignments.
3. The introduction of VDMs to the ARC-AGI benchmark, enhancing their demonstrated reasoning capacities significantly.

Quantitative assessments across different tasks indicate robust performance, notably in visual reasoning, where the model achieved a competitive outcome on ARC-AGI when compared to established LLM approaches. Such results underscore the adaptability of VDMs, revealing their viability as prospective backbone models for future visual AIs.

Implications and Future Directions

The implications of this research extend both practically and theoretically. Practically, the findings advocate for the practicality of VDMs in situations constrained by limited training samples. Theoretically, the work enriches our understanding of how generative models can be cast into versatile platforms capable of abstract thought and few-shot learning. The reframing of VDMs as adaptable learners breaks the conventional boundaries, urging further exploration toward integrated models that unify perceptual, generative, and reasoning functionalities within a singular architecture.

Future research should investigate the scalability of these insights, particularly across diverse domains outside of vision, and extend to understanding the emergent properties that facilitate such efficient adaptation. It also raises prospects for investigating compositional learning in other multimodal settings, potentially analogous to LLMs but realized in richer visual representations.

By venturing into the underexplored territory of few-shot learning with VDMs, this paper contributes a substantial advance, equipping the research community with insights and methodologies to harness the immense potential within generative models beyond traditional confines.