Data-efficient Large Vision Models through Sequential Autoregression (2402.04841v1)

Abstract: Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended to not only comprehend but also seamlessly transit to out-of-domain tasks. However, current endeavors are hamstrung by an over-reliance on colossal models, exemplified by models with upwards of 3B parameters, and the necessity for an extensive corpus of visual data, often comprising a staggering 400B tokens. In this paper, we delve into the development of an efficient, autoregression-based vision model, innovatively architected to operate on a limited dataset. We meticulously demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding during the testing phase. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint, and a marked decrease in training data requirements, thereby paving the way for more sustainable and accessible advancements in the field of generalist vision models. The code is available at https://github.com/ggjy/DeLVM.

Data-efficient Large Vision Models through Sequential Autoregression

The paper "Data-efficient Large Vision Models through Sequential Autoregression" articulates a novel approach to enhance the training efficiency of large vision models (LVMs), addressing the prevalent dependency on colossal models and extensive datasets. The research advances the development of autoregression-based vision models capable of generalization across diverse visual tasks, while importantly reducing both the parameter count and the volume of required training data.

Core Contributions

The authors propose an innovative method that utilizes sequential autoregression to develop general-purpose vision models trained solely on visual data, sans linguistic inputs. This approach emphasizes adaptability to out-of-domain tasks and robust performance in scenarios with limited datasets. In doing so, the paper introduces several key contributions:

1. Autoregressive Model Architecture: Inspired by the success of autoregressive LLMs in NLP, the authors adapt this framework to vision tasks, leveraging visual sentences for image tokenization through a VQGAN and processing them via a LLaMA transformer model. This technique facilitates diverse task learning while maintaining model efficiency.
2. Data Augmentation and Task Balance: Recognizing the skewed distribution of data across different visual tasks, the paper proposes a data augmentation strategy to mitigate imbalances. For instance, by balancing the training samples, the method ensures equitable task representation, enhancing model accuracy and reducing instances of performance degradation. The empirical validation indicates that augmenting data achieves results akin to introducing new data.
3. Knowledge Distillation (KD): To further refine model efficiency, the paper explores the application of KD, which leverages a pre-trained, larger model (teacher) to guide a smaller, efficient model (student). KD shows promise in narrowing performance gaps between large and compact vision models, even enhancing single-task and multi-task outcomes.
4. Validation and Practical Implications: The paper presents robust empirical validations across key vision tasks, including image segmentation, pose estimation, and image deraining. Findings highlight the considerable potential for practical deployment of these models, especially in environments where computational resources are a constraint.

Numerical Results and Claims

The paper provides detailed quantitative analyses demonstrating the advantage of the proposed methods. In scenarios where data augmentation is applied, there is a notable reduction in validation loss and perplexity. For example, augmenting human pose estimation datasets yields comparable results to direct data introduction, reinforcing the methodology's efficacy in data-scarce settings. Moreover, KD significantly enhances the student model's performance, achieving validation accuracies close to those of the more parameterized teacher models.

Implications and Future Directions

This research has both practical and theoretical implications in the field of AI. Practically, the development of efficient LVMs enables broader accessibility and deployment in real-world applications where hardware limitations are a concern. Theoretically, the insights into data augmentation and KD underscore the evolving paradigms in vision model training, offering a framework that balances efficiency with performance.

Looking forward, this work suggests potential for further exploration into autoregressive frameworks for vision models, particularly in refining tokenization strategies and expanding task adaptability. The paper highlights the need for developing methods to convert visual results into actionable, quantifiable outputs, suggesting a promising domain for future research.

Overall, the paper paves the way for sustainable advancements in scalable vision models, aligning closely with ongoing efforts to optimize AI applications for diverse and resource-constrained environments.