From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models

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 introduces a novel few-shot fine-tuning framework using LoRA to repurpose video diffusion models for diverse vision tasks.
• Experimental results demonstrate VDMs effectively perform tasks ranging from image segmentation to abstract reasoning with minimal examples.
• The study highlights the potential of using generative VDMs as adaptable visual learners to drive future advancements in visual AI.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models

Introduction

The paper "From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models" by Acuaviva et al. explores the dual capability of Video Diffusion Models (VDMs), traditionally viewed as tools for high-quality video synthesis, to also serve as general-purpose visual learners. Through innovative use of few-shot learning and a novel task conversion methodology, the authors propose VDMs as adaptable visual architects suitable for broader AI applications.

Methodology

At the core of the paper is a new few-shot fine-tuning framework that leverages the inherent structure-learning potential of VDMs. VDMs model video transitions, compelling them to form coherent spatiotemporal representations of visual content. The proposed framework exploits Low-Rank Adaptation (LoRA) to train on short input-output sequences without modifying the larger architecture of the VDM. Tasks are translated into visual transitions using interpolation, effectively aligning them with VDMs’ generative capabilities.

Figure 1: Proposed framework. Given a task encoded as input-target image pairs (dashed gray box), a transition video is constructed to transform the input into the target. We fine-tune LoRA adapters with the core model frozen. At inference, the model outputs a transition video from a new input, with the final frame used as the prediction.

The paper's methodology involves reformatting tasks for compatibility with pre-trained VDMs by constructing transition videos as target sequences. This allows VDMs to operate as task adapters, demonstrating the latent, structured representations that VDMs encode. This approach facilitates few-shot adaptation across tasks ranging from segmentation to abstract reasoning.

Experimental Evaluation

The experimental results reinforce the notion that VDMs can be adept visual learners. Through extensive experiments, the authors illustrate that VDMs can tackle a spectrum of tasks typically outside their original scope, from low-level image segmentation to high-level reasoning challenges, using only a handful of examples.

Figure 2: Examples of solved tasks from ARC. Training samples for each task (first three rows), followed by evaluation (last row).

The authors evaluate their framework on various vision tasks and abstract reasoning corpora such as ARC-AGI, showcasing VDMs' strong generalization capabilities. The methods' effectiveness is demonstrated not only on conventional computer vision tasks but also on abstract reasoning benchmarks, positioning VDMs as prospective cornerstones for vision foundation models.

Implications and Future Directions

The findings suggest that VDMs can form the backbone of future visual foundation models, combining generative prowess with task adaptability. The versatility observed in VDMs highlights a potential shift toward video-based models in visual AI, where structural richness and temporal dynamics provide a powerful inductive bias for unifying perception-based tasks and abstract reasoning.

The paper encourages future explorations into the structured knowledge encoded by VDMs, acknowledging limitations associated with task specificity and computational complexity. It suggests further research into methods of reducing computational overhead, possibly through advanced LoRA compositions, to make few-shot learning more efficient.

Conclusion

This study reframes the role of Video Diffusion Models beyond generation, positing them as adept visual learners capable of strong few-shot learning performance. By uncovering the rich, latent representations in VDMs, the paper paves the way for deploying these models as adaptable, general-purpose vision systems. Further research is encouraged to enhance the computational feasibility and expand the application scope of such models, pointing toward a dynamic future for visual AI technologies.